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Lenders Transformation Playbook: Bridging Strategy and Execution

Blanc Labs

Lenders Transformation Playbook: Bridging Strategy and Execution

Introduction

The “Lenders Transformation Playbook” serves as a comprehensive guide for financial institutions embarking on digital and operational transformation. In an era of rapid technological change and evolving customer expectations, lenders face increasing pressure to innovate and align business strategies with technology capabilities. This document provides a structured approach for bridging the gap between strategy and execution, ensuring that transformation efforts are targeted, phased, and measurable for sustainable growth.

Executive Summary

  • Strategic Alignment: The playbook outlines a transformation roadmap that integrates business objectives with technology capabilities, driving profitable growth, operational efficiency, and effective risk management.
  • Phased Approach: A structured, phased strategy breaks transformation into manageable steps, minimizing risk while steadily building necessary capabilities.
  • Detailed Initiatives and Costs: Each initiative is mapped out with its associated costs, dependencies, and timelines, enabling optimized resource allocation and ensuring maximum impact.
  • Current State Assessment and Target Setting: The document emphasizes evaluating the current state to set a clear, achievable target, supported by KPIs for tracking progress and outcomes.
  • Governance, Risk, and Compliance (GRC): GRC frameworks are integrated into the transformation process to align technology investments with regulatory compliance and business continuity.
  • Prioritization Framework: Initiatives are ranked using a Feasibility vs. Business Value Matrix, focusing on strategic value, ROI, and feasibility for streamlined execution.
  • Progress Measurement: A phased, metric-driven approach (Crawl, Walk, Run) measures success, with continuous stakeholder feedback for real-time adjustments and sustainable growth.

What Good Looks Like

A successful transformation roadmap provides a structured approach to achieving long-term business objectives. Key components include:

Clear alignment between business and technology strategies.

A phased approach to transformation, breaking down the journey into manageable initiatives.

A focus on prioritization, ensuring the highest impact projects are delivered first.

Phasing the transformation allows organizations to mitigate risks while steadily building capabilities. A good roadmap offers a structured sequence for initiatives based on their strategic relevance and feasibility, ensuring resources are optimized for maximum impact.

Chart 1

The strategic roadmap visually and functionally integrates these key elements:

  • Business Objectives: These are high-level goals the organization aims to achieve, such as Profitable Growth, Operational Efficiency, and Compliance and Risk.
  • Business IT / Capabilities: For each business objective, specific IT capabilities are identified that need to be developed or enhanced to achieve these objectives.
  • Strategic Actions: Aligned with each capability are strategic actions. These are specific activities or projects that need to be undertaken to develop the capabilities necessary for achieving the business objectives.
  • Strategic Roadmap: This section maps out the timing and sequence of initiatives derived from the strategic actions. It is presented in a timeline format across quarters or months. Each initiative is associated with a specific strategic action and is plotted against time to show when it will be initiated and its duration.
  • Cost: Each initiative on the timeline has an associated cost, which helps in budgeting and resource allocation planning.
  • Key Dependencies and Risks: Below the timeline, key dependencies and risks are illustrated. Dependencies show how certain initiatives are reliant on the completion of others, while identified risks might impact the execution of the initiatives.
  • Metrics / KPIs: Corresponding to the initiatives are metrics or KPIs that will be used to measure the success of each initiative, ensuring that each step taken aligns with the strategic goals and delivers the intended outcomes.

Three Steps Towards Creating a Transformation Roadmap

Developing a successful transformation roadmap requires a structured approach, ensuring that both business objectives and technology capabilities are fully aligned. This section outlines three critical steps to creating an effective roadmap, merging the analysis of the current state and the definition of the target state into a single step, as these are deeply interconnected processes.

Chart 2

Aligning Business Objectives with Technology Capabilities

In large-scale transformations, aligning business objectives with technology capabilities is a critical step. The success of a transformation initiative hinges on whether your technology strategy is effectively calibrated to deliver business value. This alignment is more than just matching technology solutions to business needs—it’s about creating a cohesive strategy that considers business vision, processes, architecture, and execution plans.

Establishing Strategic Linkage Between Business and Technology

Image 3

As shown in the diagram, lenders should align their technology investments with business strategy, ensuring each objective, whether focused on customer engagement, efficiency, or growth, is tied to relevant technology capabilities. By evaluating emerging technologies such as AI, cloud, and data analytics, lenders can ensure these investments are both strategically relevant and scalable, driving meaningful outcomes.

Translating Business Strategy into Operational Execution

A well-defined business strategy must translate into an executable plan, which is facilitated by enterprise architecture. The architecture serves as the “glue” that binds strategy with operational execution by guiding critical questions like:

Image 4

Governance, Risk and Compliance (GRC)

Governance, Risk, and Compliance (GRC) frameworks are crucial in aligning business objectives with technology capabilities in the highly regulated financial services industry. For lenders, GRC goes beyond regulatory compliance to enhance business continuity, risk mitigation, and operational integrity. GRC should be integral to the enterprise architecture, guiding technology selection and influencing architectural decisions to minimize vulnerabilities and ensure compliance from the outset.

Effective governance sets up processes, standards, and oversight to align technology investments with business goals, such as improving customer experience or boosting operational efficiency. By establishing clear roles and responsibilities through frameworks, it ensures accountability and facilitates informed decision-making and swift execution.

Risk management identifies and addresses threats like data breaches or system failures, crucial for safeguarding data privacy, transaction security, and service availability. Integrating risk management into technology planning helps lenders preempt regulatory risks and embed compliance into technology solutions from the start.

Compliance dictates specific technology adoptions, driving investments in data management and compliance-centric capabilities like automated reporting and audit trails. This ensures lenders meet regulatory demands efficiently and responsibly, particularly as they increasingly use AI and data analytics, adhering to privacy laws and ethical guidelines.

Assessing the Current State and Defining the Target State: Where We Are and Where We Want to Be

The transformation process begins by evaluating the current state of the organization’s technology capabilities and business alignment and setting a defined target state which the transformation aims to achieve. Utilizing tools like the Online Capability Maturity Assessment ensures that these assessments are thorough and accurately reflect the organization’s capability across various domains.

Assessing the Current State of Technology Capabilities

The first step is to gain a clear, objective understanding of where the organization stands today. This involves a thorough evaluation of current technology capabilities. The assessment phase serves as a diagnostic tool to identify existing gaps, inefficiencies, and misalignments that hinder the organization’s ability to achieve its goals. Capabilities will be organized into three domains:

Image 5

IT Operations
Applications & Data
Infrastructure & Security

Digital Organization: Evaluates the integration of digital technology into all areas of business, changing how operations are performed and value delivered.

Strategy & Innovation: Assesses how effectively the organization fosters innovation and implements strategic technology initiatives that align with business goals.

Technology Finance: Reviews financial management strategies related to technology investments to ensure cost-effectiveness and alignment with financial goals.

Project Portfolio Management: Analyzes the management of the entire portfolio of IT projects to ensure they deliver on intended business values and are aligned with strategic objectives.

Process Reengineering: Focuses on the analysis and redesign of workflows and processes within the organization to drive improvements in cost, quality, service, and speed.

App/SaaS Portfolio Rationalization: Assesses the use and effectiveness of applications and Software-as-a-Service solutions to identify overlap, reduce costs, and streamline software use.

Solution Delivery & Support: Examines how solutions are developed, delivered, and supported to meet user needs effectively and efficiently.

Enterprise & Data Analytics: Evaluates the capabilities in managing and leveraging data across the organization to support decision-making and achieve competitive advantage.

Data Center & Facilities Management: Reviews the management of physical data centers and related facilities to ensure they meet operational needs securely and efficiently.

Server & IaaS Management: Assesses the effectiveness of server management and Infrastructure-as-a-Service provisions to support scalability and business continuity.

Enterprise Security: Focuses on the policies, technologies, and controls deployed to protect data, applications, and the associated infrastructure from internal and external threats.

Networks & Communication Services: Reviews the infrastructure and services that support networking and communications to ensure they are robust, secure, and capable of meeting current and future demands.

The assessment incorporates detailed evaluations across these domains, utilizing metrics ranging from absent (0) to optimized (5), providing a clear picture of technology and business alignment.

Assess the Current State of Business Capabilities

To thoroughly assess the current state of business capabilities, an enterprise must first construct and examine its value chain. This involves a detailed analysis of all processes within the value chain to ensure they effectively support the organization’s strategic objectives. This assessment is critical for identifying efficiency gaps, opportunities for enhancement, and areas where the business may be at risk of falling behind market demands or technological advancements.

The construction of the value chain involves mapping out all key activities involved in creating and delivering the product or service to the customer. This includes everything from inbound logistics, operations, and outbound logistics to marketing, sales, and service. Each segment of the value chain is scrutinized to understand how individual activities contribute to overall value creation and competitive advantage.

Each of these business capabilities, and processes should be guided by key value drivers that align with the organization’s broader business objectives. These value drivers might include:

  • Cost Efficiency: Ensuring that processes are cost-effective and provide the best return on investment. Identifying areas where costs can be reduced without compromising quality is crucial.
  • Quality Enhancement: Maintaining or improving the quality of outputs to meet or exceed customer expectations. Processes should be assessed for their ability to consistently produce high-quality results.
  • Innovation and Adaptability: Gauging the organization’s capacity to innovate and adapt to changing market conditions. This includes the flexibility of processes to incorporate new technologies or methodologies.
  • Risk Management: Evaluating how risks are managed within the business processes. This includes assessing the robustness of the process against internal and external shocks and compliance with relevant laws and regulations.
  • Customer Satisfaction: Measuring how processes impact customer satisfaction and loyalty. This involves understanding customer needs and expectations and how well the business delivers on these.

Let’s take underwriting as an example:

To determine the maturity of the “Underwriting” business capability with regard to the key value drivers of cost efficiency and risk management, you can pose specific questions that probe the depth, effectiveness, and integration of current practices. These questions should help evaluate whether the underwriting processes are optimized to meet the organization’s goals and regulatory requirements effectively. Here’s a breakout of the questions can be asked for two key drivers:

Image 8

Defining the Target State: Where We Want to Be

After the current state has been assessed, the next step is to define the target state—the future vision for both technology and business capabilities. The target state represents the desired end-goal of the transformation process, and it serves as a guide for building the roadmap that will take the organization from its current state to this future vision.

Aligning the Target State with Strategic Objectives:

  • The future state should be directly aligned with the organization’s long-term business goals. Whether the objective is to drive revenue growth, improve customer satisfaction, or increase operational efficiency, every element of the target state should contribute to these outcomes.
  • Key Performance Indicators (KPIs) should be established to measure success. Examples of KPIs include reducing operational costs by 20%, increasing the speed of customer onboarding by 30%, or improving customer retention by 15%. These metrics ensure that progress toward the target state is measurable and aligned with business priorities.

Here is an example of the evaluations of the current state and the projection of the target state of technology capabilities:

Also, here is an example of the evaluation of the Underwriting business capability, under Cost Efficiency and Risk Management key drivers, and the target state.

Strategic Steps to Achieve Target State:

To strategically achieve the target state, the organization should implement several key measures. First, establishing interim goals is crucial; these act as milestones that progressively enable the achievement of target capabilities. This phased approach not only facilitates continuous assessment but also allows for necessary adjustments along the transformation journey. Secondly, priority setting is essential. The organization must focus on areas identified in the maturity assessment as needing the most improvement—these are the domains where enhancements can drive significant business value. Finally, effective resource allocation is paramount. Resources should be strategically deployed to areas that demand immediate attention and have the potential to make the most significant impact on reaching the target state. Collectively, these strategies ensure a structured and effective progression towards the envisioned future capabilities.

Sequencing initiatives based on Prioritization

In any large-scale transformation, successful execution depends heavily on the ability to effectively sequence initiatives. For lenders, this is particularly important given the complexities of aligning technology upgrades, process changes, regulatory requirements, and customer experience improvements. Developing a prioritization framework based on a feasibility versus business value matrix, along with other key factors, allows for focused and efficient execution of transformation efforts.

Feasibility vs. Business Value: A Practical Framework for Prioritization

Before delving into the detailed analysis with the Feasibility vs. Business Value Matrix, it’s crucial to first conduct a prefilter step. This preliminary assessment serves to screen initiatives by answering key questions related to their strategic alignment, optimization potential, technical feasibility, potential return on investment, and compliance considerations. By addressing these questions, we can effectively filter out initiatives that are misaligned with our strategic goals or lack viability, ensuring only the most promising initiatives proceed to the detailed evaluation phase. This streamlined approach helps prioritize resources efficiently and focus on initiatives that offer tangible benefits.

Image 11

Feasibility Factors
Business Value Factors
  • Technical Complexity
    Determine how challenging it is to implement the technology required. Initiatives involving advanced technology integration, significant infrastructure upgrades, or large-scale system migrations are generally considered less feasible.
  • Resource Availability
    Assess whether the organization has the necessary resources—people, time, skills, and budget—to execute the initiative. High feasibility is associated with initiatives that require minimal additional resources or leverage existing capabilities.
  • Organizational Readiness
    Consider how prepared the organization is for the change. Initiatives that align well with existing processes, skills, and culture are more feasible, while those requiring significant change management or reskilling are less so.
  • Dependency and Complexity
    Analyze whether the initiative depends on the completion of other projects or if it introduces significant cross-functional dependencies. High feasibility is associated with standalone initiatives or those with manageable dependencies.
  • Strategic Alignment
    Determine how closely the initiative aligns with the organization’s strategic priorities, such as enhancing customer experience, driving growth, or improving compliance. Initiatives that directly support key business objectives score high in business value.
  • Revenue Impact
    Evaluate the potential for increased revenue or cost savings. High-value initiatives typically offer substantial returns, either by generating new revenue streams, optimizing operations, or reducing risks.
  • Customer Experience Enhancement
    Consider how the initiative will affect customer satisfaction and loyalty. Projects that improve user journeys, reduce friction, or introduce new customer-facing features tend to have a high business value.
  • Risk Mitigation and Compliance
    Assess whether the initiative addresses critical regulatory requirements or mitigates significant risks. Initiatives that help the organization avoid penalties, improve data security, or ensure compliance with evolving regulations often deliver substantial business value.
  • Competitive Advantage
    Examine the extent to which the initiative provides differentiation in the market. High-value projects are those that give the organization a competitive edge, whether by introducing innovative products, improving service delivery, or optimizing go-to-market strategies.

To effectively prioritize initiatives using the Feasibility vs. Business Value matrix, it is essential to assign specific weights to each factor within both the feasibility and business value dimensions. Not all factors carry the same importance for every organization. By assigning weights to each factor—such as technical complexity, resource availability, revenue impact, or customer experience enhancement—organizations can create a more nuanced and tailored evaluation. Once weights are established, each initiative can be assessed by scoring it against these weighted factors. This scoring process allows for a more objective comparison of initiatives, making it easier to identify which projects should be prioritized based on their overall feasibility and potential business value. This approach not only brings clarity to the decision-making process but also ensures that resources are allocated to initiatives that best align with strategic goals while considering practical constraints. Each of the initiatives will have a Business Case, where evaluation will be placed.

Image 12

Once the initiatives are evaluated, they can be plotted in a scatter chart graph, where the initiatives will be classified into three zones:

Image 13

Initiatives to Pursue, with high ease of implementation and high business value; Initiatives to Explore, with high ease of implementation OR high business value; and Initiatives to Hold Off, with low ease of implementation OR low business value. Using this matrix allows lenders to visually map and rank initiatives, ensuring that resources are directed toward efforts that provide the greatest return on investment (ROI) while aligning with strategic objectives.

Balancing Quick Wins with Strategic Priorities

While the matrix provides a clear view of where to start, balancing quick wins with strategic priorities is key to sustaining momentum throughout the transformation. Here’s how:

  • Phased Approach: Start with quick wins that deliver early benefits. These successes build credibility and buy-in for the broader transformation, making it easier to secure resources and stakeholder support for more complex initiatives.
  • Dependency Mapping: Before initiating any strategic priority, map dependencies to understand how one initiative impacts others. For example, deploying an integrated loan origination platform may depend on completing data integration projects first.

 

Image 14

  • Risk Management: Prioritize initiatives that mitigate risks early on, such as addressing regulatory compliance issues or upgrading outdated systems that could lead to business disruptions.

Creating a Transformation Roadmap: Sequencing and Milestones

Once initiatives are prioritized, a transformation roadmap can be developed to sequence the initiatives over time. The roadmap should include:

  • Phases and Milestones: Break the transformation into phases, each with clear milestones. For example, Phase 1 might focus on foundational upgrades like data integration, while Phase 2 could introduce customer-facing digital services.
  • Resource Allocation and Timeline Planning: Align resources, including personnel, technology, and budget, with the planned initiatives. Ensure timelines are realistic and consider potential delays or risks.
  • Cross-Functional Coordination: Transformation often requires collaboration across different business units, including IT, operations, finance, and customer service. The roadmap should define how these units interact at each phase and clarify roles and responsibilities.

Measuring Progress – Tracking Success of Large-Scale Transformations

Measuring progress and tracking the success of large-scale transformations are critical to ensuring that the organization’s strategic changes yield the desired outcomes. Effective measurement and tracking not only provide a clear view of how well the transformation initiatives are performing but also help identify areas that need adjustment or further improvement.

Image 15

Establishing Clear Metrics and KPIs

Establish specific, measurable, achievable, relevant, and time-bound (SMART) metrics that align with the strategic objectives of the transformation. These could include operational efficiency metrics, customer satisfaction scores, system uptime, and financial metrics like ROI and cost savings.

Develop KPIs that are tailored to the specific goals of each transformation initiative. For example, if a project aims to enhance digital customer interactions, relevant KPIs might include digital engagement rates and digital sales conversion rates.

Phased Measurement Approach

This structured approach details the progression of architectural and operational enhancements over a 24-month period, segmented into three phases: Crawl, Walk, and Run. Each phase is defined by specific metrics that track the maturity and effectiveness of initiatives, starting from basic compliance and engagement, advancing through process optimization and governance, and culminating in technological refinement and redundancy reduction. This method ensures that improvements are sustainable and strategically aligned, facilitating continuous advancement in both technology and business processes.

chart 16

Stakeholder feedback

Stakeholder feedback plays a pivotal role in refining the transformation process. Gathering insights from internal stakeholders, including employees and management, provides a direct line of sight into the operational impacts and challenges of the transformation efforts. This internal feedback is critical for identifying unforeseen issues and areas needing improvement. Similarly, actively engaging with customers to obtain their perspectives on how changes affect their experience helps in adjusting strategies to better meet consumer demands, particularly for customer-facing innovations.

Learning, Adaptation and Documentation

Incorporating continuous learning and adaptation ensures the long-term success of transformations.

Regular data analysis allows for real-time adjustments and fine-tuning of strategies.

Transparent reporting and thorough documentation build trust and align stakeholders.

Sharing success stories reinforces the transformation’s value.

A structured framework to measure progress through ‘Crawl’, ‘Walk’, and ‘Run’ phases helps achieve immediate goals while supporting sustainable growth and adaptation.

Conclusion: Securing Success Through Strategic Transformation

Large-scale transformations require meticulous planning, execution, and continuous oversight, aimed not only at achieving short-term goals but at reshaping the organization for long-term success. A well-structured transformation roadmap helps align strategic actions with broader business objectives, ensuring each step contributes meaningfully. By adopting a phased approach—crawling before walking, and walking before running—organizations can manage the pace of change, solidifying capabilities before moving on to more complex initiatives, reducing risk, and enhancing the likelihood of success.

Continuous progress reviews, real-time monitoring, and stakeholder feedback are crucial for keeping the transformation relevant and responsive. Fostering a culture of learning and adaptation ensures that insights from each phase are integrated into future strategies. Transparent communication and documentation further support this adaptive approach, keeping stakeholders informed and engaged. Ultimately, transformation success lies not just in completing initiatives, but in positioning the organization to meet future challenges and seize new opportunities, ensuring sustainable growth and evolution.

Ruben Armas
Technology Strategy, Process and Data Specialist – Advisory Services

Ruben is a technology strategist with a passion for turning complex challenges into streamlined solutions. With over 8 years of experience in process optimization and business intelligence, he has led automation, data analytics, digital innovation, and technology transformation projects across the Financial, Insurance, and Healthcare sectors, consistently delivering results that drive growth and efficiency.

Prithvi Srinivasan
Managing Director – Advisory Services

Prithvi Srinivasan, the Managing Director for Advisory Services, brings extensive expertise in technology strategy and digital transformation to drive mission-critical programs and advance financial institutions with AI and automation. His ability to transform complex strategies into successful transformations underscores his commitment to innovation and client value.

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AI’s Mid-Market Makeover in Financial Services

Blanc Labs

AI’s Mid-Market Makeover in Financial Services

hero

Summary

Mid-sized financial services institutions (FIs) are facing significant challenges during this period of rapid technological change, particularly with the rise of artificial intelligence (AI). As customer expectations grow, smaller banks and lenders must stay competitive and responsive. Canada’s largest financial institutions are already advancing in AI, while many others remain in ‘observer’ mode, hesitant to invest and experiment. Yet, mid-sized FIs that adopt the right strategy have unique agility, allowing them to adapt swiftly and efficiently to technological disruptions—even more so than their larger counterparts.

This report explores how AI can tackle these challenges by transforming operations and generating substantial value across departments and business lines. We present a detailed approach for identifying, prioritizing, and implementing AI use cases, empowering mid-sized financial institutions to maintain a competitive edge in the fast-changing financial industry.

AI offers a major opportunity to enhance productivity, personalize customer interactions, improve decision-making, and create new business models. Successful AI integration requires a balanced, strategic approach that aligns with business goals and carefully evaluates potential risks.

Several challenges facing mid-sized financial services companies

In a time of rapid technological advancements and changing market conditions, mid-sized financial services companies are at a crossroads. They must meet rising customer expectations and deal with growing competition, presenting them with unique challenges that can either hinder their growth or drive them to seek new opportunities. Here are the top four challenges in this area:

Rising Customer Expectations

Competitive Pressures

Workforce Dynamics and People Constraints

Technology and AI Disruption

In this exploration, we look at how mid-sized FIs can use AI to overcome challenges and improve operations and customer engagement. In a following article, we will prioritize AI use cases to help these institutions make informed decisions. 

Creating Value Across All Departments

Amid the challenges faced by mid-sized financial firms, AI stands out as a key driver of progress. It enables transformative change, boosts operational efficiency, personalizes customer interactions, enhances decision-making, and fosters the development of new business models. By addressing these challenges, AI has the potential to create significant differentiation and impact every aspect of the business.

Here’s how AI can boost businesses:

Productivity Customer Decision making Business model
Increase Productivity and Efficiency Personalize Customer Experiences Augment Decision Making Invent New Business Models

Increase Productivity & Efficiency

AI-driven tools are revolutionizing operational workflows by streamlining processes and reducing manual intervention. Through intelligent document processing, regulatory compliance monitoring, and process automation, financial services firms can achieve unprecedented levels of productivity. By leveraging AI-powered copilots to augment humans in tasks like email management and coding modernization, organizations can optimize their day-to-day operations and enable their workforce to focus on higher-value activities.

Personalize Customer Experience

In today’s competitive market, personalization is key to customer satisfaction, and AI is at the forefront of this capability. With AI-powered sentiment analysis and recommendation engines, financial services companies can tailor services and products to meet individual customer needs more effectively. Furthermore, AI-driven customer engagement tools, from text-to-video communications to generative experiences, enable businesses to create seamless, personalized journeys that enhance customer loyalty and retention.

Augment Decision Making

AI augments decision-making processes by providing real-time, data-driven insights that enhance business strategies. From underwriting assistance to dynamic pricing and risk scoring, AI enables financial institutions and other businesses to make informed decisions faster and more accurately. By integrating AI in areas like fraud detection and smart financial advisory, companies can mitigate risks while optimizing their decision-making processes, leading to improved outcomes and competitive advantage.

Invent New Business Models

AI is not just about optimizing existing processes; it also drives the creation of entirely new business models. For example. the advent of AI-powered embedded finance and autonomous lending platforms demonstrates how AI can unlock new revenue streams and business opportunities. Through insights-as-a-service offerings and new partnership value streams, organizations can capitalize on their data, leverage AI to explore untapped markets, and invent business models that could disrupt traditional banks and lenders.

In a subsequent article, we’ll explore these topics in depth, breaking them down with detailed explanations and real-world use cases.

GenAI Value Creation: Aspiration vs. Reality

As financial institutions navigate the complex landscape of AI adoption, two distinct sentiments emerge, each underpinned by its own set of expectations and concerns.

On one hand, the optimists in the financial sector view GenAI as a revolutionary force capable of driving significant industry changes. This group is excited by the potential for:

True Hyper-personalization: Tailoring products and services to individual preferences and needs for every single customer.

Maximum Customer Experience: Leveraging conversational AI to improve service quality and user engagement across all channels.

Automation Everywhere: Implementing AI-driven processes to streamline operations and reduce the need for manual and paper-based work.

Giant Productivity Leaps: Increasing efficiency & effectiveness across all organizational levels, by augmenting employees to focus on the highest value activities.

Zero-Fraud Financial Systems: Using advanced AI algorithms to detect and prevent fraud in real-time.

These optimists are encouraged by projections suggesting that GenAI could unlock over $1 trillion in banking revenue by 2030[1], signaling a major shift in how financial services are structured and delivered. Moreover, 75% of executives anticipate that GenAI will drive disruptive changes within the industry over the next three years.[2]

Conversely, the skeptics approach GenAI with caution, emphasizing the potential pitfalls over the promised perks. Their concerns include:

Boom & Bust Cycles: Risk of overinvestments driving unsustainable growth, low ROI, and financial problems down the road, as was the case with several technology waves in the past decades.

Reliability Issues: Worries about the limitations and flaws of new GenAI technologies, and their impact on mission-critical financial processes where the room for error is very small.

Regulatory Constraints: Challenges to AI growth due to evolving, stringent regulations across local and federal jurisdictions in financial services.

Security and Privacy Risks: Increased risk of cyber threats, data privacy concerns, and potential bias as AI continues to handle and process more sensitive information on customers and employees.

Overpromise, Underdeliver: Many worry about the prospect of AI failing to meet the current hype, and see in it an over-marketed tech that is less revolutionary that it seems.

Interestingly, despite the much touted prospects of AI, a significant 66% of executives surveyed express ambivalence or dissatisfaction with their organization’s progress in AI integration, highlighting a gap between expectation and execution.[3]

Embracing AI Optimism with Prudence

Broadening Our AI Understanding
Evaluating AI’s Imperfections
Looking Beyond the Present
Avoiding Common Pitfalls

AI encompasses a wide array of tools and capabilities, including battle-tested solutions based on both predictive and generative AI. Recognizing the full range of AI technologies will enable us to leverage the most effective solutions for our specific needs.

Like humans, AI is not infallible—it makes mistakes. However, combining human oversight with GenAI’s capabilities could enhance our decision-making processes beyond what’s achievable by human efforts alone.

The rapid progress of AI means what’s cutting-edge today might be outdated tomorrow. We should consider the potential of future AI developments, much like how each model (e.g. GPT-4o) has expanded on the capabilities of its predecessors. Looking into the not-so-distant future, imagine what GPT-14 would be capable of vs. GPT-4.

AI isn’t a one-size-fits-all solution, and should not be treated solely as a technology problem. Rather than adopting AI for its own sake, it’s vital to focus on strategic AI use cases that align with our specific business goals.

The Spectrum of AI Adoption in Financial Services: Where Companies Stand

In the financial services sector, firms vary significantly in their approach and adoption of artificial intelligence. This diversity is best understood through four distinct groups: Observers, Experimenters, Implementers, and Innovators. Each category defines the group’s current engagement with AI technologies and also underscores its strategic vision and operational readiness for future disruptions.

 

new-chart

Observers

Many observers are choosing to wait for large, proven returns on investment (ROI) and clearer technology convergence before diving into AI. While this cautious stance lets them learn from early adopters, it risks putting these FIs behind competitors who are seizing AI-driven advantages now.

Experimenters

Experimenting FIs are cautiously dipping their toes into AI through small-scale projects and pilot programs, testing the waters to gauge capabilities and potential benefits without diving into full-scale implementation. This approach nurtures a culture of learning and adaptability, but without a comprehensive strategy, its impact and scalability may remain limited.

Implementers

Implementers are more advanced in their AI journey, embedding AI systems within specific departments and processes to generate tangible business value. These FIs are building significant AI capabilities and gaining advantages in differentiation and operational efficiency. However, their challenge is to scale these benefits across the entire organization to prevent siloed success and ensure widespread impact.

Innovators

Innovators are embedding comprehensive AI strategies across their business models, reshaping their operations and potentially revolutionizing the financial services industry for a powerful competitive edge. However, this ambitious approach comes with heightened risks and substantial investments, making the journey to success both challenging and uncertain.

Competitive AI Landscape: Lessons from Canada’s Big Financial Institutions 

As mid-sized FIs consider the question of AI, it’s crucial to recognize the significant strides made by Canada’s largest banks. Notably, three of the Big Five banks are ranked among the top ten globally for AI maturity [4], showcasing their leadership in using AI to transform financial services. Here’s how some of these banks are creating competitive advantage with AI: 

Ranked among the top three globally for AI maturity, RBC owns Borealis AI, a dedicated research and development institute. It has launched AI-driven products like Aiden for trading and NOMI for personal finance management, and utilizes AI to enhance internal processes and modernize legacy systems. 
With its AI R&D center, Layer 6, TD has grown its AI talent to over 200 professionals and is developing over 50 AI solutions across its business lines. It is the leading patent filer in AI among Canadian FIs (450+ AI-related patent filings) and uses predictive AI models to pre-approve mortgages in seconds, demonstrating a strong commitment to integrating AI across its operations.[5]
Winner of the Best Gen-AI Initiative by The Digital Banker 2024, CIBC has developed its own custom built ‘CIBA AI system to improve productivity and enable staff to focus on high-value activities. The introduction of tools like GitHub CoPilot boosts developer efficiency, and the bank plans to significantly expand its data and AI workforce by hiring 200+ related roles over the next 12 months.[6]
Scotiabank’s customer-facing AI chatbot handles 40% of queries with a 90% accuracy rate and has significantly sped up agent training. The bank has also launched a global AI platform to enhance customer insights across all touchpoints and employs large language models to improve employee experiences.[7]
BMO considers AI integral to its bank strategy. Its mobile app provides AI-driven personalized insights to improve customer engagement, its ‘Digital Workbench’ offers real-time analytics for commercial clients, and its contact centers’ service quality is boosted through NLP tech. Last year, it upskilled over 3,500 employees through learning & development programs centered on AI and cloud. Externally, BMO demonstrated commitment to AI by sponsoring the ‘Next AI accelerator.[8]

 

Strategic Imperative for Mid-Sized FIs 

The AI advancements by Canada’s largest banks illustrate a clear trend: AI is not just a technological upgrade but a strategic necessity. Mid-sized FIs must recognize the need to keep pace and innovate beyond traditional technologies and models. Key actions for staying ahead include: 

  1. Accelerated AI Adoption and Scale-Up: Mid-sized FIs need to prioritize their own AI roadmaps and seek opportunities to rapidly scale adoption of AI solutions across various departments. 
  2. Target High-Value AI Applications: Focus on areas where AI can quickly deliver the most significant returns, such as improving customer experience and increasing operational efficiency – before exploring how AI can enable the development of new financial products & services. 
  3. Invest in AI Talent and Partnerships: Expand internal capabilities by hiring AI specialists and consider partnerships with AI tech & service providers to tap into external expertise. 
  4. Monitor and Adapt Best Practices: Keep a close eye on industry leaders and continuously adapt their AI best practices to suit smaller scale operations and unique market demands. 

By embracing these strategies, mid-sized FIs can respond to the competitive pressures exerted by larger banks and carve out their own niche in the rapidly evolving financial landscape. 

Competitive AI Landscape: Lessons from Canada’s Big Financial Institutions 

As mid-sized FIs consider the question of AI, it’s crucial to recognize the significant strides made by Canada’s largest banks. Notably, three of the Big Five banks are ranked among the top ten globally for AI maturity [4], showcasing their leadership in using AI to transform financial services. Here’s how some of these banks are creating competitive advantage with AI: 

Ranked among the top three globally for AI maturity, RBC owns Borealis AI, a dedicated research institute. It has launched AI-driven products like Aiden for trading and NOMI for personal finance management, and utilizes AI to enhance internal processes and modernize legacy systems. 

With its AI R&D center, Layer 6, TD has grown its AI talent to over 200 professionals and is developing over 50 AI solutions across its business lines. It is the leading patent filer in AI among  Canadian FIs (450+ AI-related patent filings) and uses predictive AI models to pre-approve mortgages in seconds, demonstrating a strong commitment to integrating AI across its operations.[5]

Winner of the Best Gen-AI Initiative by The Digital Banker 2024, CIBC has developed its own custom built ‘CIBA AI system to improve productivity and enable staff to focus on high-value activities. The introduction of tools like GitHub CoPilot boosts developer efficiency, and the bank plans to significantly expand its data and AI workforce by hiring 200+ related roles over the next 12 months.[6]

Scotiabank’s customer-facing AI chatbot handles 40% of queries with a 90% accuracy rate and has significantly sped up agent training. The bank has also launched a global AI platform to enhance customer insights across all touchpoints and employs large language models to improve employee experiences.[7]

BMO considers AI integral to its bank strategy. Its mobile app provides AI-driven personalized insights to improve customer engagement, its ‘Digital Workbench’ offers real-time analytics for commercial clients, and its contact centers’ service quality is boosted through NLP tech. Last year, it upskilled over 3,500 employees through learning & development programs centered on AI and cloud. Externally, BMO demonstrated commitment to AI by sponsoring the ‘Next AI accelerator.[8]

 

Strategic Imperative for Mid-Sized FIs 

The AI advancements by Canada’s largest banks illustrate a clear trend: AI is not just a technological upgrade but a strategic necessity. Mid-sized FIs must feel the urgency to not only catch up but also innovate beyond traditional models to remain competitive. The following strategic actions are recommended: 

  1. Rapid AI Adoption and Scale-Up: Mid-sized FIs need to prioritize AI adoption and seek opportunities to scale AI solutions across their operations quickly. 
  2. Focus on High-Impact AI Applications: Identify areas where AI can create significant value, such as enhancing customer experience, improving operational efficiency, and developing new financial products. 
  3. Invest in AI Talent and Partnerships: Expand internal capabilities by hiring AI specialists and consider partnerships with AI tech & services firms to leverage external expertise. 
  4. Monitor and Adapt Best Practices: Keep a close eye on industry leaders and continuously adapt their AI best practices to suit smaller scale operations and unique market demands.

 

By embracing these strategies, mid-sized FIs can respond to the competitive pressures exerted by larger banks and carve out their own niche in the rapidly evolving financial landscape. 

Conclusion

The financial services industry is at a turning point, especially for mid-sized FIs. Adopting AI is no longer a choice but a necessity to remain competitive and meet the growing demands of the market. With extensive experience working with mid-sized FIs, Blanc Labs recognizes the transformative potential AI brings—from boosting operational efficiency to tailoring customer experiences and introducing new business models. As technology continues to advance at a rapid pace, it’s crucial that these institutions take decisive action to leverage these tools. By embracing AI now, your organization can gain a competitive advantage, fuel growth, and position itself for long-term success in an increasingly fast-paced industry. 

The next article in this series will explore real-world examples of AI in action within financial services and offer a structured approach to identifying and prioritizing the most impactful AI applications for mid-sized FIs.

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Blanc Labs Welcomes Tom Purves as a Leader in Payments and Product Innovation

Financial Services | Payments Innovation

Blanc Labs Welcomes Tom Purves as a Leader in Payments and Product Innovation

August 8, 2024

August 8, 2024 (Toronto, ON) 

We’re thrilled to announce that Thomas Purves has come onboard as Advisor & Consulting Lead, Payments Innovation. With over 20 years of leadership experience in global-scale payments, digital banking, and fintech, Tom brings a wealth of knowledge and expertise to our team. His extensive background, which includes co-founding LaHave.io, a US-based consultancy specializing in fractional CxO engagements and fintech advisory, establishes him as a leading figure in the payments industry.

We’re particularly excited about Tom’s contributions to the development of real-time rails in Canada and the broader landscape of payment innovation in 2024-2025. Tom played a critical role in developing Canada’s first real-time payment rails, as the original product manager of both Interac E-transfers and Visa Direct in Canada.

In that capacity, Tom enabled clients such as Bank of America, PNC, Navy Federal, Q2, Nium, and Marqeta to transform card issuing into modern digital-first customer experiences. For instance, in a case study with Redwood Credit Union, these efforts led to an 83% growth in transaction rates within the first three days of instant credit card issuance or re-issuance to customers’ mobile devices, along with an average of 19 additional card transactions per customer.

During his tenure at Visa, Tom also led the global digital wallet product line, designing and commercializing high-converting payment SDKs for 50 million users across hundreds of thousands of merchants in 25 countries. His work spanned partnerships with renowned brands like Starbucks, United Airlines, Walmart, PizzaHut, and Uber, demonstrating his ability to scale digital payment solutions globally. Tom has also authored more than 80 US and international patents in payments, digital wallets and tokenization technologies.

In his current consulting practice as well as in his recent role as Chief Product Officer and Chief Technology Officer of the US fintech company MainStreet.com, Tom has been spearheading AI-transformation in combination with Open Banking and embedded finance. For example, at MainStreet.com Tom enabled thousands of US businesses to quickly connect, classify, and process their purchasing and payroll expenses, unlocking billions in tax credits and procurement savings.

With Tom on board, Blanc Labs is poised to lead the charge in payment innovation for our clients.  We look forward to the transformative impact he’ll bring to our clients and the industry at large. Welcome, Tom!

Interested in speaking with Tom?  Send him a note thomasp@blanclabs.com

 

 

 

Learn More About Our Payments Expertise

Process Improvement and Automation Support the Mission at Trez Capital 🚀

 

Case Studies

Process Improvement and Automation Support the Mission at Trez Capital 🚀

Blanc Labs, Trez Capital and Microsoft Azure logos

Summary

As a leading North American real estate lending firm, investment management, loan accounting, and loan disbursements are at the core of Trez Capital’s operations. The firm distributes capital based on very specific criteria. But with over 300 investments in one quarter alone, they process numerous payment requests and deal with documents in varied data formats.

Trez was relying on manual processes and email for document handling of mission critical workflow which offered an opportunity to enhance security and reduce disbursement cycle times, thereby improving efficiency and customer satisfaction.

people working
people working
people working
98.7%
On-time Payments with New Solution
39.8%
Fewer Approvals Required with Process Redesign
33.3%
Reduction in Cycle Time
45%
Fewer Errors in Payments Workflow

Our Vision for Streamlined Payment Processes

Working with Trez team members, we developed a vision for the future of payment disbursement for real estate investment firms. We are focused on solutions that prioritize user-friendliness, easy customization and configuration, automated workflows, and seamless collaboration with external parties.

Trez Solution Approach

Joel Oakden
VP, Chief Accounting Officer

Blanc Labs delivered a quality solution in a short period of time. They worked closely with our team and were able to adapt to our changing timelines. They have been very supportive in the post-deployment process too. I wouldn’t hesitate to recommend Blanc Labs.

Redesigning the Payments Approval Process

The solution involved a comprehensive redesign of the approval process, starting with interviews with senior stakeholders to understand their views on the current process and its operational impact. We then created various payment scenarios, considering factors like amount, bank, and wire templates, to assess the effectiveness of multilevel approvals.

Aligning with Trez’s Enterprise Technology Strategy and Solution Approach

As a leading Microsoft partner to Financial Services firms, Blanc Labs used cutting-edge enterprise tools such as Power Automate, Power Apps and Power BI to deliver streamlined business processes, enhance workflow efficiency, and reduce manual interventions in the new loan payments solution for Trez.

The Trez Capital Payments solution utilizes Microsoft Power Apps and Power Automate to create a centralized platform that simplifies the management of payment requests, approvals, and communication. Microsoft Power Apps enables rapid application development through a low-code/no-code environment, focusing on functionalities like loan information management, and request and approval workflows.

Microsoft Power Apps provides a rapid low-code/no-code solution for application development, encompassing key functionalities such as loan information management, request and approval creation, and settings configuration.

High Level Payments Solution Architecture

Trez solution architecture

Adopting a Data Driven Approach to Continuous Improvement

For enhanced performance management, we’ve implemented a robust dashboard using Microsoft PowerBI, empowering Trez Capital with comprehensive insights into their processes and performance

The dashboard showcases a range of Key Performance Indicators (KPIs) derived from activity data within the tool. These include generic transactional metrics such as the number of requests processed per day and the total amount of payments processed per day. In addition to these, the dashboard provides process quality KPIs like the cycle time for processing payments and the percentage of payments circled back to initiators.

 

Process Improvement and Automation Support with Blanc Labs

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Why Banks and Credit Unions Should Pick Technology Vendors with SOC 2 Type II Certification 

Financial Services | IT Partners | IT Security & Compliance | IT Strategy

Why Banks and Credit Unions Should Pick Technology Vendors with SOC 2 Type II Certification 

July 18, 2024

Are your technology vendors SOC 2 Type II compliant? If not, your bank or credit union could be another data breach case study in the making. Many financial institutions, including big ones like Bank of America, fall victim to data breaches every year because of a third party. 

According to IBM’s Cost of a Data Breach Report, data breaches cost an average of $4.45 million. Partnering with SOC 2 Type II certified vendors can significantly minimize the risks of data breaches. In this guide, we explain how. 

What is SOC 2 Type II Compliance? 

SOC 2 Type II is a framework that assesses the effectiveness of internal controls used by a service organization to protect customer data. The American Institute of Certified Public Accountants (AICPA) established SOC (Service Organization Control) 2 standards based on five trust service criteria (TSC): 

  • Security: The systems must be protected against unauthorized physical and logical access using firewalls and authentication. 
  • Availability: The systems must be accessible as per service level agreements (SLAs). This includes, but is not limited to, monitoring network performance and availability, site failover, and security incident handling. 
  • Processing integrity: The right data must be delivered at the right price and time. Data processing must be complete, timely, validated, accurate, and authorized. 
  • Confidentiality: Access to data must be limited to authorized parties using encryption, firewalls, and access controls. 
  • Privacy: The system’s collection, use, retention, disclosure, and disposal of information must conform with the company’s privacy notice and the criteria set according to AICPA’s generally accepted accounting principles (GAAP).


The Difference Between SOC 2 Type I and Type II Compliance 

SOC 2 Types I and II differ in terms of scope and duration of evaluation. Here’s how: 

  • SOC 2 Type I is like a snapshot of your organization’s controls at a specific point in time. It confirms that necessary systems and processes exist to meet the TSC, but doesn’t consider the effectiveness of those controls. Companies often use type I reports to show commitment to security and compliance at a specific point in time, such as during contract negotiations. 
  • SOC 2 Type II involves a more comprehensive audit conducted over six to 12 months. Since the audit spans over a longer time frame, it helps validate the effectiveness of controls rather than just verify their presence. Type II reports offer more value to companies that want to offer their customers ongoing assurance about the effectiveness of controls over time. 

If you’re a bank or credit union, partner with SOC 2 Type II vendors. The longer testing window and the focus on the effectiveness of controls are vital to minimizing security and other risks. 

How SOC 2 Type II Certification is Achieved 

To become SOC 2 Type II compliant, a business must implement controls, gather supporting evidence, and engage a CPA to conduct an audit. If the CPA is assured that the organization has complied with all the requirements based on the audit, they give the company an unqualified opinion. Of course, the process is easier said than done. Here’s a quick overview of what the process looks like: 

Identify Gaps 

The organization looking to get certified needs internal controls in one or more of the five TSC. Security is a mandatory TSC, but the organization can choose others if it makes sense for their business or customer needs. Next, they need to assess their current controls and processes against the TSC to find gaps. This gives them a starting point and a preview of the quantum of effort required for certification. 

Design and Implement Controls 

Each TSC has sub-criteria that require the organization to establish and test controls, and remediate where necessary. As simple as it may sound, designing controls and collecting evidence can be quite complex. That’s exactly why it’s best to assign the program to someone with adequate technical knowledge, such as a CTO. 

The CTO must monitor the designing of controls and implement them across the organization. This involves training staff, updating IT systems, and reengineering or modifying processes to effectively support new controls. The organization must document all control activities, policies, and procedures in detail. These documents are crucial to demonstrating compliance during the audit. 

After all controls are in place, the organization must conduct a pre-assessment to test the effectiveness of controls and fix any issues or deficiencies that come to light. 

Select an Auditor 

Once all the controls are in place, it’s time to engage an independent third-party auditor to perform the SOC 2 Type II audit. The company looking to get certified can hire one of the Big4 firms, a CPA firm, or an individual practicing CPA, but it’s important to choose a CPA who has experience with SOC 2 assessments and understands the company’s industry and compliance needs. 

The CTO should work closely with the auditor throughout the process. The auditor will require a minimum of six months to assess the design and operational effectiveness of controls and verify compliance with TSC. If the auditor finds deficiencies during the audit, the organization must be prepared to promptly address them. 

Obtain Certification 

Once all issues have been identified and remediated and the audit is completed, the auditor will issue a SOC 2 Type II report. The report assures customers and stakeholders that the company’s controls are effectively designed and operating consistently over time. Beyond this point, the organization must continuously monitor and review controls, conduct periodic assessments, and stay updated with regulatory changes to maintain compliance. 

The Importance of Working with a SOC 2 Type II Compliant Vendor 

Banks and credit unions handle massive volumes of sensitive financial data every day. It’s critical to ensure this data’s security during day-to-day operations. SOC 2 Type II certification assures you and your customers that your vendors have the infrastructure and internal controls in place to securely process sensitive data. Let’s dive deeper into why banks and credit unions need to seek vendors with SOC 2 Type II certification: 

Data Security 

Data breaches at financial organizations can have serious repercussions—they can erode customer confidence and invite thousands of dollars in penalties. That’s why you should work with SOC 2 Type II compliant technology vendors—their compliance with SOC 2 standards assures that internal controls are in place to secure data. 

Risk Mitigation 

Partnering with SOC 2 Type II certified vendors minimizes the risk of data breaches, regulatory penalties, and damage to reputation. The number of cases of data violations jumped to 744 in 2023 from 138 in 2020—in a world where data breaches are a major risk, working with vendors that have adequate security can significantly lower the probability of mishaps. 

Compliance Alignment 

Many SOC 2 Type II requirements overlap with requirements of standards and regulations like PCI DSS (Payment Card Industry Data Security Standard) and GLBA (Gramm-Leach-Bliley Act), and guidelines from FFIEC (Federal Financial Institutions Examination Council). 

Common compliance requirements include controls over areas such as data encryption, network security, and vulnerability management, as well as data protection measures like tokenization and secure transmission protocols. 

Operational Resilience 

SOC 2 Type II assesses the availability and processing integrity of the vendor’s systems. Certified vendors have robust measures in place to ensure uninterrupted access to critical systems and maintain the reliability and accuracy of data processing. This offers operational resilience and continuity and minimizes disruption, allowing banks to deliver frictionless services to customers. 

Enhanced Trust and Reputation 

Partnering with certified vendors shows your customers that you prioritize integrity, transparency, and accountability when looking for vendors. It makes customers feel more confident when sharing personal and sensitive data with your systems. Over time, this builds trust and enhances reputation among clients and the broader community. 

Select Technology Vendors You Can Trust 

Non-certified vendors pose a significant burden and risk to not just your bank or credit union but also to customers who choose to bring their business to you. Falling victim to a data breach can ruin your bank or credit union’s reputation and invite costly penalties from regulators. 

The best way to safeguard your business, clients, and reputation is to only trust SOC 2 Type II certified vendors like Blanc Labs with your data. If you’re looking for help with designing, building, and integrating future-ready core banking systems, book a free consultation with us today. 

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How Open Banking Will Empower Small Businesses in Canada

Financial Services | Consumer Driven Banking | Open Banking

How Open Banking Will Empower Small Businesses in Canada

June 6, 2024
small business owners benefitting from open banking technology

As the old saying goes, “cash is king” and with 85% of business owners saying they’re looking for faster and easier access to capital, it seems like this is still truer than ever in today’s economy. Factors like high inflation and slow lending processes are making it harder for small businesses to access growth and operating capital, while needing to respond to challenges and opportunities in an ever-evolving business landscape. Open Banking can help address some of these challenges. In this guide, we discuss how.

What is Keeping Small Businesses From the Capital They Need?

To understand how open banking helps small businesses access capital faster, we need to look at the challenges they currently face.

Margins Under Pressure

Small businesses find accessing capital difficult when lenders tighten their belts or become risk-averse. An unfavorable credit market can result from multiple factors, such as high interest rates or a global financial crisis.

Inflation is a major contributor to tightening the credit market. It increases the cost of labor, raw materials, and operating expenses. In fact, 49% of respondents cited the cost of goods as the biggest inflation-related concern in an Equifax survey.

If small businesses can’t pass on the increased costs to customers because of competitive pressures or customer resistance, inflation can put pressure on profit margins. This translates to a lower net income percentage and cash generation—banks don’t like to see either when lending to a business.

Slow Lending Processes

Banks require applicants to submit tax returns, financial assets, a business plan, and various other documents. Excessive paperwork leaves room for errors and missing information. This is a major reason small businesses don’t get loan approval and are often hesitant to apply at all.

Stringent Know Your Customer (KYC) checks and rigorous anti-money laundering regulations significantly slow down loan approvals. Banks in Canada take risk assessment seriously. While there’s a good reason for it, analyzing credit history, projecting cash flows, and valuing collateral take time.

Even after going through the process, the number of small businesses that secure funding is often as low as 13.5%. Moreover, applicants who do make it through the approval phase might receive approval for an amount lower than requested.

Lack of Complete Information

Banks often don’t receive complete information from applicants. This can skew their decisions. For example, an applicant’s business might have changed significantly since they filed their last return.

Suppose your business recently secured a huge long-term contract with a multinational corporation. The bank might not see it on your previous return and won’t factor it into their assessment. That’s why banks need access to real-time data when making a lending decision.

Some banks rely on the borrower’s personal credit score to make lending decisions. This means you may find getting loan approval more difficult if you belong to Black or Hispanic communities where low credit scores and credit invisibility are common.

How will Open Banking Technology Transform the Lending Process?

Open banking, or “consumer-driven banking,” is about to revolutionize how small businesses secure debt capital. A whitepaper by Equifax explains:

“Two use cases stand out among those lenders already using Open Banking: payment initiation and more accurate consumer lending decisions. Not far behind, at 24%, are organizations that are using Open Banking to improve estimation or verification of consumer income.”

Open banking is not available in Canada yet, but work is underway to implement it as soon as possible. Open banking will transform the lending process in the following ways, making it easier for small businesses to access debt capital from financial institutions.

Easy Access to Data for Lenders and Small Businesses

Banks rely on financial data to make lending decisions. Open banking does a great job of mobilizing financial data, and here’s how it helps both small businesses and lenders:

Offers Banks a Thorough Credit Picture

Open banking technology connects small businesses and financial institutions via a third-party provider—usually fintech companies and alternative lenders. Third-party providers have access to a wealth of financial information, including transaction history, income, and the applicant’s creditworthiness. This gives lenders ample data to assess a loan application.

Third-party apps use application programming interfaces (APIs) to access real-time financial data from multiple sources, including the applicant’s bank account. This eliminates the need for manual documentation and verification, minimizes administrative load, and streamlines the lending process.

Drives Fairer Outcomes for Applicants With Low Credit Visibility

Access to more accurate data also drives fairer outcomes for a diverse range of applicants. This is especially important for Canada, where 12.2% of small business owners or primary decision-makers belong to a visible minority.

Many of these owners, especially those who recently migrated to Canada, don’t have enough financial history that banks can use to assess their creditworthiness. Open banking can help these applicants by enabling them to supplement loan credit checks with transaction data.

Enables Small Businesses to Seek Funding More Strategically

Open banking products give small businesses a holistic view of their financial position. For example, small businesses can monitor their cash flows and profitability during times of economic turmoil and apply for a loan once their financial statements project greater resilience.

In countries where open banking has already been implemented, businesses have realized various benefits, including greater visibility over cash flow. The UK’s Open Banking Implementation Entity interviewed 900 SMEs, of which 77% reported that open banking provided them with better visibility of their financial position.

Streamlined Application and Approval Process

Funding that doesn’t arrive on time is often futile. If you need access to quick cash during a high-growth phase or cash crunch, you probably don’t want to wait weeks until, and if, the lender approves your application. Not to mention, the loan application process itself could take up to a week, depending on the lender you’re dealing with.

Open banking automates various tasks in the lending process, such as:

Customer Data Aggregation

Open banking APIs enable applicants to share financial data with the lender via the open banking app. There’s no need for applicants to submit documents and statements manually. Once data flows in through the API, advanced data analytics tools and algorithms categorize, structure, and analyze the data to assess creditworthiness.

Cash Flow Analysis

Your cash flow statement may give lenders an idea of your business’s ability to generate cash. However, analyzing the sources and applications of cash using a cash flow statement requires manual work.

Instead of manually going through the cash flow statement, lenders can run the data through an algorithm or data analytics tool. They can apply advanced data analytics techniques to customer’s cash flow data sourced through open banking to understand the customer’s sources of income, recurring expenses, and spending habits.

Integration with Loan Origination Systems

Open banking enables seamless integration with the lenders’ loan origination systems, facilitating automated data transfer. This means underwriters, loan officers, and decision-makers have all the data they need readily available, resulting in faster approvals.

Greater Transparency and Competition

Here’s how open banking enhances transparency and competition: 

Makes  Shopping for Loans Easier 

Open banking offers small businesses greater transparency over prevailing rates.  With better data portabilitybusiness owners could use a third-party  app to compare interest rates, terms, and features of a loan product across multiple lenders without having to approach them individually. 

Increases Efficiency and Innovation 

Open banking levels the playing field by lowering the entry barrier for new financial services startups. Open banking has been instrumental in facilitating new lending models like peer-to-peer (P2P) lending platforms and digital lending marketplaces where you can explore a wide range of financing options. 

 

Open Banking for Small Businesses: Immediate and Long-Term Benefits

Here are some benefits of open banking for small businesses: 

Faster Access to Capital

Small businesses often face various challenges when accessing traditional financing options like bank loans, thanks to stringent eligibility criteria and lengthy approval processes. According to Ellie Mae, the average time to close a loan from the day of application to the disbursement of funds is 52 days. 

Open banking makes it easier for small businesses to access capital via alternative financing options. Countries that have already implemented open banking are seeing massive benefits. For example, Infosys built an open banking solution for a non-banking financial company (NBFC) in India. The solution helped the NBGC disburse funds faster while remaining 100% compliant. 

Helps Build Long-Term Financial Fortitude

Financial prudence offers various long-term benefits. Many small businesses aren’t profitable and deal with cash flow challenges. Using open banking solutions to track financial data across multiple accounts can add financial fortitude to your business, which is critical to getting the best loan terms and quick approval. 

Open banking software can even help reduce costs. A survey reveals that open banking helps businesses save up to 150 hours and 36 minutes each year—a little over four full working weeks. This translates to greater profitability and favorable changes in key metrics like the interest coverage ratio, eventually improving the odds of getting a loan approved. 

Tailored Financial Products

Open banking offers personalized financial products and services that cater to a small business’s specific needs. 

Suppose a small business uses an open banking app that monitors the business’s cash flow. The app keeps forecasting cash flows in real-time and alerts you when it sees a possible cash crunch coming. 

It also recommends a working capital loan, a list of lenders offering great terms, and a repayment schedule so the business owner or decision-maker can understand the impact on cash flows. 

Fosters Innovation and Competition

Greater access to financial data gives fintechs the ammunition to develop unique solutions and  tailored offerings that address specific needs insmall business finance. 

Mastercard is already making strides towards innovating unique solutions. The company has partnered with payments platform Dwolla to help users securely send funds without sharing account or routing numbers. 

It’s also collaborating with the fintech company Jack Henry, which services small community and regional banks, to provide account holders with the ability to see all of their financial information across multiple accounts (including those outside their primary financial institution) in one place. 

 The UK has already seen a massive range of innovative solutions powered by open banking. For example, the UK has apps to help people easily pay tax—the UK government received over £10.5 billion in taxes through open banking payments, according to a report by the Joint Regulatory Oversight Committee published in February 2023. 

Empowers Customers

One of the most significant aspects of open banking is customer empowerment. Open banking promotes transparency and accountability in the financial ecosystem by putting control of data into the customers’ hands and letting them decide whether they want to share their data with a third party. Customers can choose to share data with lenders that offer the most competitive loan terms, driving competition among lenders. 

Open Banking Strategy and Implementation 

Financial Institutions can benefit significantly from creating an open banking ecosystem for small and medium-sized businesses.  The global impact potential of Open Banking adoption across all sectors of banking  has been estimated at between 1 and 5 % of GDP according to McKinsey. But getting started can be challenging. To ensure a successful outcome, a strategic and methodical approach is crucial. Here are some key steps to effectively implement open banking for your institution: 

  1. Define Clear Use Cases: Start by identifying customer’s needs and how open banking can enhance your current SMB service offerings. Evaluate how these initiatives align with your overall business strategy in unlocking new opportunities for growth.  
  2. Develop a Partnership Roadmap: Open banking is an ecosystem that thrives on collaboration. Map out potential partnerships with other financial institutions and third parties. Focus on enhancing data utilization and creating customer offerings. While choosing partners, make sure they align with your technology capabilities and business goals.  
  3. Standardize and Manage APIs: APIs should be managed with a comprehensive framework which includes protocols for access, security, maintenance, and governance. This framework will be critical in ensuring that your open banking ecosystem runs smoothly and securely, which complying to data protection standards.  

Are you ready to implement Open Banking Technology?

Partner with Blanc Labs to build an open banking platform or solution that will help you innovate, stay ahead of the curve, and thrive as the open banking ecosystem matures.  

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Align, Assemble, Assure: A Framework for AI Adoption

Align, Assemble
Assure

Executive Summary 

In the rapidly evolving landscape of artificial intelligence (AI), enterprises face significant challenges in differentiating their AI capabilities to achieve strategic objectives. This article provides a comprehensive approach to organizing internal structures across business, technology, and governance/risk/compliance domains to build a robust and differentiated AI capability. 

The first part of the article emphasizes the importance of Stakeholder Alignment. To ensure success, AI initiatives must align closely with the organization’s strategic objectives and core values. This alignment ensures that AI projects not only drive innovation but also resonate with the organizational ethos and mission. By integrating AI principles and strategies, enterprises can foster a culture that supports and accelerates AI adoption. 

The second section delves into the Operating Model, which is crucial for driving AI innovation and efficiency. This involves defining the ideal team structures and identifying the essential capabilities required across people, processes, and technology. A robust operating model includes a well-defined AI platform that supports strategic objectives and maximizes returns. It also emphasizes the importance of ideation, experimentation, and the productionalization of AI projects to ensure they deliver tangible business value. Effective resource allocation and management are key to maximizing the returns on AI investments. 

The third critical aspect covered is Governance, Risk, and Compliance. As AI technologies advance, so do the associated risks and regulatory requirements. It is imperative for enterprises to identify and manage potential risks linked to AI projects proactively. Establishing comprehensive compliance mechanisms ensures that AI applications adhere to all regulatory and ethical standards, thereby safeguarding the organization against legal and ethical pitfalls. 

To address these multifaceted challenges, this article introduces the Align | Assemble | Assure (AAA) Framework for AI adoption within enterprises. 

  1. Align: This phase focuses on ensuring that AI initiatives are in sync with the strategic objectives and core values of the enterprise. By developing a clear AI strategy and set of principles, organizations can provide a coherent direction for AI adoption that supports broader business goals. 
  2. Assemble: This phase is about building the necessary capabilities across the organization. It involves organizing teams effectively and ensuring the right mix of talent, processes, and technology. By developing an AI use case portfolio, roadmap, business case, and budget, enterprises can prioritize AI investments and drive projects that offer the highest return on investment (ROI). 
  3. Assure: The final phase focuses on risk management and compliance. This involves assessing AI risks comprehensively and ensuring compliance with internal policies and external regulations. Implementing an AI risk management framework and compliance measures ensures that AI initiatives are secure, ethical, and compliant with all necessary standards.

In the next section, we will explore in depth how the Blanc Labs AAA Framework for AI Adoption can be implemented using actionable and measurable steps.

Align

First, ensure that all AI initiatives are aligned with the company’s strategic objectives and core business values. This involves understanding the business landscape and determining how AI can solve existing problems or create new opportunities. By focusing on alignment, the enterprise ensures that every AI project drives meaningful impact and contributes positively to the overarching goals of the organization. 

Here’s how you can effectively execute this alignment:

Identify Strategic Objectives

Business Goals: Begin by clearly understanding the core goals of the organization. What are the key performance indicators (KPIs) or business outcomes that matter most? How can AI contribute to these areas? Thinking through this at the beginning of the process will help you make the right choices pertaining to resources, time and effort, resulting in the long-term success of your AI strategy.  

Value Alignment: Ensure that the planned AI initiatives resonate with the company’s values and culture. People are a consequential pillar in AI adoption. Aligning your goals and vision will establish a clarity of purpose, which will in turn drive employee motivation and participation. 

Stakeholder Engagement

Collaboration: Engage with stakeholders across various departments to gather insights and identify needs that AI can address. This includes executives, operational staff, and IT teams. AI can improve processes across the mortgage value chain, starting with lead generation and pre-approval at the loan origination stage, going all the way up to default management. Examples of how AI can add value include automating routine tasks to allow employees to concentrate on strategic activities, helping underwriters make complex decisions faster by analyzing research and data; using data to tailor customer experiences, which can boost sales, customer retention, and engagement; and developing comprehensive AI-driven services like chatbots or specialized products for small businesses, which can increase both new and existing revenue streams. 

Feedback Loops: Establish continuous communication channels to keep all stakeholders informed and involved in the AI integration process. This helps in adjusting strategies as needed based on real-world feedback and evolving business needs.

Market and Competitive Analysis

Benchmarking: Analyze competitors and industry standards to understand where AI can provide a competitive edge or is necessary to meet industry benchmarks. 

Innovative Opportunities: Identify gaps in the current market that AI could fill, potentially opening new business avenues or improving competitive positioning.

Risk Assessment and Mitigation

Identifying Risks: Part of alignment involves understanding the potential risks associated with AI deployments, such as data privacy issues, biases in AI models, or unintended operational impacts. 

Mitigation Strategies: Develop strategies to mitigate these risks upfront, ensuring that the AI initiatives proceed smoothly and with minimal disruption.

Scalability and Sustainability

Future-proofing: Consider how the AI initiatives align with long-term business strategies and technological advancements. Ensure that the solutions are scalable and adaptable to future business changes and technological evolution.

Regulatory Compliance

Legal and Ethical Considerations: Ensure that all AI deployments follow relevant laws and ethical guidelines, which is particularly important in industries like healthcare, finance, and public services. 

Assemble 

Next, assemble a dedicated cross-functional AI Center of Excellence (CoE). This centralized team should consist of experts in AI, data science, ethics, compliance, and business operations. The CoE acts as the hub for AI expertise and collaboration within the company, enabling the standardization of tools, techniques, and methodologies. It also facilitates the pooling of resources and knowledge, ensuring that AI projects across the organization benefit from a consistent approach and high levels of technical and ethical oversight. 

To delve deeper into the “Assemble” part of the “Align, Assemble, Assure” framework using the people, process, technology, and organization structure components, we need to consider how each of these elements supports the creation of a robust AI capability within an enterprise. 

People 

Assembling the right talent is critical. This includes hiring and nurturing: 

  • AI Specialists: Data scientists, machine learning engineers, and AI researchers who can develop and optimize AI models. 
  • Technology Experts: Cloud architects, cyber security engineers, solution architects, software developers and testers who can build and support AI applications 
  • Business Domain Experts: Project managers and business analysts who understand the specific challenges and opportunities within the industry and can ensure that AI solutions are relevant and impactful.
  • Support Roles: Risk, legal and compliance officers to oversee AI projects and ensure they align with business and regulatory requirements. 

 

Process 

Establishing clear processes ensures AI projects are executed efficiently and effectively: 

Development Lifecycle:  

Define a standard AI project lifecycle, from ideation and data collection to model training and deployment. Here is a detailed breakdown of the various stages of the project lifecycle, including challenges, solutions and how to measure impact: 

Evaluation and Planning: Define critical performance indicators to measure success. Detail both functional and non-functional requirements, outline the necessary output formats, develop a comprehensive monitoring strategy, and define AI literacy requirements for the system. Additionally, determine the necessary data inputs and set clear criteria for explanations. Select the appropriate Generative AI technology, determine how it will be customized, and outline a high-level support and accountability framework. Evaluate and address the risks associated with these activities proportionally, documenting significant effects and strategies for risk mitigation. 

Pro Tip: When setting up your evaluation framework, maintain a balance between technical precision and flexibility. This allows your team to adapt quickly to new insights or changes in technology without compromising the system’s integrity or performance. Regularly revisit and refine your performance metrics and requirements to ensure they remain aligned with your strategic goals and the evolving landscape of AI technology. 

Data Set Up: Access, clean, and transform data to ensure it is high-quality, well-understood, and relevant for the specific use case. Address data privacy, security, legal, and ethical concerns by putting in place robust safeguards and compliance measures for both the input and output of data, making sure that data owners have approved its use. Set up strict guardrails for the input of information to third-party generative tools and the output from the solution. This includes establishing processes for refining or filtering the output before it reaches the end user, and setting clear restrictions on how the output can be utilized. 

Pro Tip: Emphasize the importance of automation in cleansing and transforming your data. This not only saves time but also reduces human error, ensuring consistent data quality. Additionally, continually update and refine your data governance policies and generative AI guardrails to keep pace with technological advancements and evolving regulatory landscapes. This proactive approach will help maintain the integrity and security of your data, enhancing overall trust in your AI solutions. 

Development: Start by selecting the optimal tool that matches your requirements in terms of size, language capabilities, and pre-trained features, along with the most appropriate integration method, whether it’s an API or an on-premises solution. Customize your tool using various advanced techniques such as fine-tuning, which involves teaching the tool to perform new or improved tasks like refining output formats, and Retrieval Augmented Generation (RAG), which enhances prompt responses and outputs by incorporating external knowledge sources. Develop and refine a systematic approach to prompt engineering to create, manage, and continuously improve prompts. Focus on refining the solution to boost performance, accuracy, and efficiency through methods like adjusting parameters, tuning hyperparameters, improving data quality, conducting feature engineering, and making architectural adjustments. 

Pro Tip: Prioritize the scalability and adaptability of your tools and methods. As you refine and expand your AI applications, ensure that the tools you select can evolve with your needs and can integrate new features or data sources seamlessly. Regularly revisit your prompt engineering and customization strategies to keep them aligned with the latest advancements in AI technology, thus maintaining your competitive edge and maximizing the effectiveness of your solutions. 

Implementation and Management: Integrate the AI solution into the operational applications, ensuring seamless transition into production environments. Assign clear ownership to oversee the solution’s lifecycle. Develop a support structure that includes performance-based Service Level Agreements (SLAs) and operational guidelines aimed at fulfilling the established non-functional requirements, and introduce practices for consistent data management. Enhance organizational understanding and capability through targeted AI training programs. Ensure diligent registration of any new use of AI. Complete all the necessary documentation to provide clear explanations and transparency regarding the AI solution’s functionalities and decisions. 

Pro Tip: Establish a feedback loop between the operational performance and the development teams. This ensures that any insights gained from real-world application can be swiftly acted upon to refine the solution. Regularly updating your integration practices and operational protocols in response to these insights will help you maintain high standards of performance and reliability, ensuring that your AI system remains robust and effective in ever-changing environments. 

Monitoring and Maintenance: Implement continuous monitoring of the deployed AI solution to assess its performance, accuracy, overall impact (including any unintended biases), usage, costs, and data management practices. Promptly investigate and resolve any issues or anomalies that arise during operation. Utilize the insights gathered from the monitoring process to regularly update, maintain, and enhance the solution. Maintain vigilant oversight of any updates made to the AI system. Ensure all new applications of the AI are properly registered. Occasionally, updates pushed by the supplier might require a comprehensive review due to potential significant changes. Provide ongoing reporting to highlight the benefits and value added by the AI solution. 

Pro Tip: Consider implementing automated tools that can alert you to anomalies in real-time. Regularly scheduled reviews of the system’s outputs and operations can help preempt problems before they escalate, ensuring that the AI continues to operate efficiently and effectively. Moreover, documenting every adjustment and update not only aids in compliance and governance but also provides valuable historical data that can inform future enhancements and deployments. 

The Role of AI Quality Assurance and Testing

AI development demands rigorous and continuous testing. The role of Quality Assurance (QA) and Testing is to assess the relevance and effectiveness of the training data, ensuring it performs as intended. This process starts with basic validation techniques, where QA engineers select portions of the training data for the validation phase. They test this data in specific scenarios to evaluate not only the algorithm’s performance on familiar data but also its ability to generalize to new, unseen data. Evaluation metrics such as accuracy, precision, recall, and the F1 score are defined based on the specific use case, recognizing that not all metrics are appropriate for every scenario.

If significant errors are detected during validation, the AI must undergo modifications, similar to traditional software development cycles. After adjustments, the AI is retested by the QA team until it meets the expected standards. However, unlike other software, AI testing by the QA team does not conclude after one cycle. QA engineers must repeatedly test the AI with various datasets for an indefinite period, depending on the desired thoroughness or available resources, all before the AI model goes into production.

During this repetitive testing phase, also known as the “training phase,” developers should test the algorithm on various fronts. Notably, QA teams will need to focus not on the code or algorithm itself but on whether the AI fulfills its intended function. QA engineers for AI testing will primarily work with hyperparameter configuration and training data, using cross-validation to ensure correct settings.

The final focus is on the training data itself, evaluating its quality, completeness, potential biases, or blindspots that might affect real-world performance. To effectively address these issues, QA teams need access to representative real-world data samples and a deep understanding of AI bias and ethics. This comprehensive approach will help them pose critical questions about the AI’s design and its ability to realistically model the scenarios it aims to predict, ensuring robustness and reliability across various applications.

 

 

Technology

To deliver comprehensive and effective AI solutions, enterprises need to establish robust technology and tooling capabilities within their AI platforms. These capabilities span several domains, each critical for developing, deploying, and managing Gen AI, Cognitive AI and machine learning (ML) models.

Gen AI Development is foundational for creating full-featured generative AI applications. Enterprises must have the capability to customize and deploy models to meet specific business needs. This includes fine-tuning model inputs and engineering prompts to enhance performance and relevance. Additionally, managing the lifecycle of models during development is crucial, ensuring that models are continuously improved and updated. Orchestrating various AI models allows enterprises to integrate multiple AI systems seamlessly, optimizing the overall functionality and efficiency of the AI platform.

Gen AI and ML Operations focus on the day-to-day management and fine-tuning of AI models once they are in production. This involves managing the lifecycle of models and their versions to maintain consistency and reliability. Effective access management to models ensures that only authorized personnel can modify or utilize these models, enhancing security. Fine-tuning ML models and their training is essential to adapt to new data and evolving business requirements. Furthermore, managing data sources efficiently ensures that the models are fed with accurate and relevant data, which is vital for generating reliable outputs.

Gen AI and ML Governance is essential for maintaining the integrity and compliance of AI systems. Enterprises need to monitor models continuously to ensure they produce accurate and appropriate responses. Safeguarding models from inappropriate or malicious inputs is critical to prevent misuse and potential harm. Compliance with legal and regulatory frameworks is another significant aspect, ensuring that AI operations adhere to the necessary standards and regulations. Managing the data leveraged by models helps maintain data privacy and security, which is paramount in today’s regulatory environment.

Cognitive AI Capabilities are designed to mimic human cognitive functions, providing advanced interactions through APIs and SDKs. These capabilities include understanding and translating languages, recognizing images and sounds, and extracting and summarizing data. By integrating these cognitive functions, enterprises can enhance their AI applications to provide more natural and intuitive interactions, thereby improving user experience and engagement.

Establishing these comprehensive technology and tooling capabilities within an enterprise AI platform is crucial for developing robust, secure, and compliant AI solutions. By focusing on these areas, enterprises can harness the full potential of AI, driving innovation and achieving strategic business objectives.

Organization Structure 

Effective organization structure facilitates AI adoption and integration: 

  • Centralized AI Unit: An AI Center or hub, possibly under a Chief AI Officer, to centralize expertise and provide leadership. 
  • Cross-functional Teams: Integration of AI teams with other business units to promote collaboration and ensure AI solutions meet business needs.  
  • Change Management: Structures to support change management processes, helping the workforce adapt to new technologies and methods introduced by AI. 

Assure 

Finally, implement robust governance to assure the safety, compliance, and ethical integrity of AI deployments. This includes setting up frameworks for ongoing monitoring and evaluation of AI systems, ensuring they adhere to regulatory requirements and ethical standards. The governance process should also involve stakeholder engagement to maintain transparency and address any concerns related to AI projects. 

Responsible AI 

Responsible use of AI will require a commitment to ethical guidelines that prioritize: 

Transparency & Explainability: Create AI systems that are both transparent and clear in their functioning. As an organization, you should be able to pinpoint and clarify AI-driven decisions and outcomes. This will be especially important in the context of customers and stakeholders.  

Compliance: Designate responsible individuals for AI systems and ensure that the systems comply with regulatory standards. Conduct frequent audits to ensure the accuracy of your AI solutions, handle unforeseen outcomes, and check that the solutions fulfill legal and regulatory requirements. 

Equity and Diversity: Design, develop and deploy AI in an ethical, fair and inclusive manner. Proactively strive to identify and address biases that can emerge from training data or decision-making algorithms.  

Data Privacy: It’s imperative to safeguard personal data and prioritize individuals’ well-being, preventing any potential harm. Commit to respecting privacy rights, secure personal data, and mitigate potential risks and negative impacts on customers by obtaining consent and implementing responsible data practices.  

Security: Create AI solutions that can withstand attacks, malfunctions and manipulation. Implement safeguards to secure AI data, services, networks and infrastructure to prevent unauthorized access, breaches or data manipulation.  

Examples of Prohibited Use Cases: 

Using a risk assessment framework guided by the ethical AI principles mentioned above, here is an example of use cases that will not pass:  

  • Assessing, categorizing, or evaluating individuals based on their biometric information, personal attributes, or social behavior. 
  • Using subliminal, manipulative, or misleading techniques to sway an individual’s behavior. 
  • Taking advantage of people’s vulnerabilities (e.g., their age, disability, or social/economic status). 
  • Emotion recognition, as defined by your legal department.  
  • Collecting biometric data indiscriminately. 
  • Generating false content that seems to represent a particular person.

 

See Also: Artificial Intelligence and Data Act Canada

The proposed Artificial Intelligence and Data Act (AIDA) aims to establish standards for the responsible design, development, and deployment of AI systems, ensuring they are safe and non-discriminatory. This legislation will require businesses to identify and mitigate the risks of their AI systems, and to provide transparent information to users.

Under AIDA, the level of safety obligations for AI systems will depend on the associated risks, and businesses will need to adhere to new regulations across the design, development, and deployment stages. The government is working to create regulations that align with existing standards, aiming to facilitate compliance for businesses. A new AI and Data Commissioner will monitor compliance to ensure AI systems are fair and non-discriminatory. By introducing this law, Canada is among the first countries to propose AI regulation, aiming to balance innovation with safety, and ensuring international competitiveness while considering the needs of all stakeholders.

For more information, view the full act.

 

The diagram above shows how you can create your own AI risk assessment framework. Risk can be measured along four factors: personal information, decision-making, bias and external access.

In Conclusion

With the principle of “Align, Assemble, Assure,” the enterprise can methodically approach AI integration, ensuring that the efforts are strategically sound, well-supported by a specialized team, and maintained under stringent ethical and regulatory standards. This principle not only fosters innovation and efficiency but also builds trust and reliability in AI applications across the business.

Take the first step towards transforming your business with AI

Prithvi Srinivasan
Managing Director – Advisory Services

Prithvi Srinivasan, the Managing Director for Advisory Services, brings extensive expertise in technology strategy and digital transformation to drive mission-critical programs and advance financial institutions with AI and automation. His ability to transform complex strategies into successful transformations underscores his commitment to innovation and client value.

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Blanc Labs Welcomes Two New Leaders to Advance AI Innovation and Enhance Tech Advisory Services for Financial Institutions Across North America

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Blanc Labs Welcomes Two New Leaders to Advance AI Innovation and Enhance Tech Advisory Services for Financial Institutions Across North America

May 7, 2024

May 6 (Toronto, ON)— Blanc Labs, a pioneer in Lending Technology, Artificial Intelligence (AI) and Intelligent Automation, is thrilled to announce the appointment of two new members to its executive team, Prithvi Srinivasan and Ali Khachan, both distinguished leaders in technology and consulting.

Prithvi Srinivasan, a former Partner at Deloitte Consulting, is joining Blanc Labs as the Managing Director for Advisory Services. Prithvi brings a wealth of expertise in technology strategy, digital transformation, and business operations excellence, and specializes in driving mission-critical transformation programs. Leveraging his extensive experience in aligning technology initiatives with business value, Prithvi is set to propel financial institutions forward using Blanc Labs’ expertise in lending technology, AI, and intelligent automation. His proven track record in driving successful enterprise transformations and optimizing technology estates underscores his commitment to fostering innovation and delivering value to clients. Prithvi excels in transforming complex strategies into actionable successes, ensuring technology not only meets but advances business objectives.

Ali Khachan, a tech startup leader and former Principal at Boston Consulting Group (BCG), joins Blanc Labs as Managing Director of AI. As a strategic operator with over 15 years of leadership at the nexus of technology and business, Ali has demonstrated a keen ability to drive transformational growth through AI and digital innovations. His expertise in scaling up ventures, spearheading digital transformations, and maximizing value creation for global enterprises will play a pivotal role in shaping the future of Blanc Labs as it continues to pioneer groundbreaking AI solutions.

“The addition of Prithvi and Ali to our executive team significantly strengthens Blanc Labs’ strategic capabilities and reinforces our position as a leading technology partner for the financial sector,” said Hamid Akbari, CEO of Blanc Labs. “Their exceptional expertise and visionary leadership will accelerate our strategic initiatives across Advisory Services, Data, Automation, Artificial Intelligence, and Generative AI solutions. I am confident that with Prithvi and Ali on board, Blanc Labs will achieve new heights in delivering exceptional value to our clients and shaping the future of digital transformation in the financial services industry.”

Both Prithvi and Ali will be speaking at the Canadian Lenders Association Bankers Summit in Toronto on May 15th. They will conduct a series of workshops focused on AI in Lending, demonstrating Blanc Labs’ commitment to fostering knowledge exchange and leading the charge in AI solutions for financial services. To register for the workshops, please reach out to Blanc Labs through their website.

 

ABOUT BLANC LABS

Blanc Labs is a preferred partner for enterprises looking to digitize and build the next generation of technology products and services. To help companies rapidly deliver on their digital initiatives, Blanc Labs has developed expertise and bespoke solutions in a wide variety of applications in financial services, healthcare, enterprise productivity, and customer experience. Headquartered in Toronto, Blanc Labs serves the Americas through operations in Toronto, New York, Bogota, and Buenos Aires. For more information on how Blanc Labs is building a better future, visit  www.blanclabs.com.

 

For media inquiries, please contact:

Shriya Ghate

Senior Marketing Manager

Blanc Labs

E: shriyag@blanclabs.com

Accelerate Data Analysis

Begin Your Digital Transformation

Blanc Labs Partners with TCG Process to Integrate their Automation and Orchestration Platform and deliver Advanced Intelligent Workflow Automation to Financial Institutions

Financial Services | Banking Automation | Enterprise Automation

Blanc Labs Partners with TCG Process to Integrate their Automation and Orchestration Platform and deliver Advanced Intelligent Workflow Automation to Financial Institutions

May 3, 2024
Blanc Labs TCG Process Partnership

May 3, 2024 (Toronto, ON) – Blanc Labs, a leader in Lending Technology, AI, and Business Process Improvement, is excited to announce a strategic partnership with TCG Process, leveraging their DocProStar platform to transform lending operations for mid-tier banks, credit unions, and other mortgage origination and servicing providers in Canada. This partnership marks a significant milestone in Blanc Labs’ mission to enhance operational efficiencies and unlock new revenue opportunities for lenders through cutting-edge technology.

Blanc Labs’ Intelligent Automation Methodology, designed by seasoned experts, pinpoints and addresses critical areas for improvement within document intensive workflows. The solution seamlessly integrates with existing technology infrastructure, enabling rapid deployment and immediate impact on operational efficiencies, with minimal investment.

“Rapid advancements in powerful new AI technologies like LLMs are further unlocking opportunities to move away from manual, data heavy workflows that cause frustrations for customers and lending teams alike,” said David Offierski, VP of Partnerships for Blanc Labs. “We are seeing a significant shift from the manual ingestion of customer information to a much more efficient and automated workflow solution, allowing stakeholders to improve both customer and employee experience while reducing operational costs.”

TCG Process’ DocProStar provides intelligent document ingestion, orchestrated processing and powerful AI capabilities that will enable Blanc Labs to quickly deploy advanced, cost-effective, and scalable solutions to meet the unique needs of lending teams.

The collaboration between Blanc Labs and TCG Process signifies a joint commitment to deliver innovative solutions, leveraging cutting-edge technologies to streamline lending processes. The opportunity is clear: to create a more seamless experience for customers, tailored to the unique needs of the Canadian lending market.

“Our partnership with Blanc Labs is more than just technology—it’s about shared expertise and collaboration. By combining our strengths, we will provide industry leading solutions that cater to the diverse needs of the financial services sector.” Joseph Capone, Chief Revenue Officer, TCG Process (Canada).

Hamid Akbari, CEO of Blanc Labs, said, “The use of technologies like DocProStar and our Kapti platform can greatly improve compliance, increase customer retention, reduce servicing cost, and boost revenue in the lending sector. Our deep expertise in lending technologies, and especially intelligent processing of mortgage documents, enhances our role as a transformation partner.”

Blanc Labs operates with agile, expert teams, ensuring that clients typically see a return on their investment within 12 months. The company remains dedicated to its role as a pivotal player in lending industry transformation.

 

 

ABOUT BLANC LABS

Blanc Labs is a preferred partner for enterprises looking to digitize and build the next generation of technology products and services. To help companies rapidly deliver on their digital initiatives, Blanc Labs has developed expertise and bespoke solutions in a wide variety of applications in financial services, healthcare, enterprise productivity, and customer experience. Headquartered in Toronto, Blanc Labs serves the Americas through operations in Toronto, New York, Bogota, and Buenos Aires. For more information on how Blanc Labs is building a better future, visit  www.blanclabs.com.

ABOUT TCG PROCESS

TCG Process, with headquarters in Switzerland, is an international organization that develops and integrates input management and intelligent process automation software. Its solutions are used in industries such as banking, insurance, healthcare, government, and public administration to digitize and automate document-driven processes. TCG Process sells both directly and via partners globally. Visit www.tcgprocess.com

 

For media inquiries, please contact:

Shriya Ghate

Senior Marketing Manager

Blanc Labs

E: shriyag@blanclabs.com

 

Tracy Weller-McCormack

Head of Marketing, Canada & Australia

E: tracy.mccormack@tcgprocess.com

 

 

 

The Transformative Influence of Large Language Models (LLMs) on Document Processing

BPI in Banking and Financial Services in the US & Canada

Financial Services | Business Process Improvement | Enterprise Automation

BPI in Banking and Financial Services in the US & Canada

April 23, 2024
Business Process Improvement in Financial Services in US & Canada

Banking and financial services are evolving at immense speed. Transitioning from traditional, paper-based processes to digital and AI-powered processes is critical to survival. That’s why you need to think about business process improvement in banking and financial services.

Business process improvement comes with a price tag. You need experts on your team, a culture centered on continuous improvement, and a powerful set of automation tools. But these tools offer extensive benefits as well. For example, Accenture estimates that automation and augmentation can help save financial services firms $140 billion and banks $70 billion.

Revolutionizing Banking and Financial Services Through Business Process Improvement

Banking and financial services firms have grown tremendously through business process improvement. Look back at your own organization’s past. You likely had to radically redesign processes to bring about any revolutionary change in your organization. From developing a mobile app for your bank to building your ATM network, all changes were a result of improved, more efficient processes.

The next leg of growth in banking and financial services requires using technologies like AI and natural language processing (NLP) to reduce costs, increase efficiency, and improve customer experience.

These technologies will help you stay ahead of the curve and improve your workflow. However, implementing technologies like AI, Gen AI, and Robotic Process Automation (RPA) requires experience and expertise, so find the right partner for smooth implementation and minimal disruption.

Let’s now dive into how you can plan to improve business processes to prepare your financial institution for the future.

BPI vs BPM

Business Process Improvement (BPI) and Business Process Management (BPM) are two distinct but complementary approaches within organizational process optimization. BPI is primarily concerned with improving the efficiency and effectiveness of specific processes. It involves identifying, analyzing, and enhancing existing business operations to reduce costs, improve service delivery, or increase quality. On the other hand, BPM takes a broader view, focusing on the entire set of processes within an organization. It integrates all aspects of process management—from design and modeling to monitoring and optimization—with the goal of aligning processes with the strategic objectives of the organization. While BPI zooms in on process improvements, BPM includes holistic management of all processes to ensure coherence and alignment with overall business goals. 

Financial Processes That Need Improvement

There’s plenty of room for improvement in financial processes as we know them today. AI and RPA technologies have opened doors to a whole universe of process improvements that were previously unimaginable. Here are some examples of processes you should consider working on when implementing BPM:

Customer Onboarding

Onboarding sets the stage for your relationship with the client. Customers who have a positive experience when opening their bank account are 73% more likely to become the bank’s promoters.

Improving your onboarding process by implementing a fully digitized workflow eliminates the need to manage piles of paperwork, errors, and delays. A frictionless onboarding experience doesn’t just attract new customers; it also builds a solid reputation and loyalty among customers.

Customer Support

The modern customer wants their bank or financial institution to respond within seconds and be available 24/7. Not delivering on these expectations is essentially the same as handing your clients to competitors.

Intelligent Digital Assistants (IDAs), next-gen chatbots powered by AI, “are fully equipped with natural language understanding which aids in understanding and retaining context for polished conversations while carrying out a variety of tasks to fulfill a user’s requirements.” They act as “knowledgable bankers” who not only understand the unique journeys of customers, but are also able to recommend products, offers and next steps based on their knowledge of a customer’s past behavior and financial history.

Risk and Compliance Management

Tailoring business processes to minimize risks and comply with industry regulations helps prevent penalties as well as lower your compliance team’s workload. For example, you can create a digital workflow that provides real-time (or frequent) tracking for compliance requirements as well as detailed compliance reports.

Analytics can play a key role in bolstering your bank’s or financial institution’s risk assessment capabilities. AI-powered systems can ingest massive amounts of data your organization generates to identify patterns. The system can detect anomalies and predict risks using this data.

Loan Approvals

Loan origination systems are often plagued by labor-intensiveness, errors, and a ton of paperwork. Automating your loan origination system can transform your efficiency and the customer’s experience.

If your organization currently uses traditional processes, consider completely reengineering the process. Modernizing your lending process, right from eligibility checks to approval, is important not to develop a competitive advantage, but to survive in an environment where quick approvals and disbursals are the norm.

 

Implementing Business Process Management in Banking and Financial Services

Changing and managing business processes is easier said than done. Your team might need to unlearn and relearn. You might have to deal with reluctance and proactively offer training to help them get up to speed.

However, there’s an even greater challenge — implementing the right set of technologies and building the right infrastructure. That’s why you need a partner who has experience helping banks and financial services firms implement technologies to improve business processes.

With that in mind, here’s an overview of how you can implement business process management:

Step 1: Identify Key Processes

The first step is to identify key processes within your bank or financial institution that could use some improvement. The process could be onboarding, loan processing, risk assessment, or customer service.

Consider asking employees — people on the ground executing these processes — how you can improve. Identify their pain points and bottlenecks and prioritize areas for optimization based on your analysis.

For example, if your bank’s loan origination process is lengthy and has high error rates, fixing that should be your priority over streamlining your support processes.

Step 2: Map Your Processes

A process map gives you an overview of the entire workflow and the steps involved in the process, including stakeholders, decision points, and the flow of information. A visual representation of your process helps you understand its current state, identify inefficiencies, and find opportunities to improve the process.

Step 3: Optimize Processes

Once you’ve identified inefficiencies and redundancies, it’s time to eliminate them. The exact solutions to implement depend on the challenge you’re addressing. However, automation and standardization are the first steps towards optimizing processes.

If you’re thinking to yourself, “We’ve already automated repetitive tasks,” that’s not enough. Use AI to automate some tasks you previously thought were best handled by humans.

For example, don’t use scripted chatbots that make customers feel like they’re talking to a robot. Find a partner who can develop a chatbot powered by technologies like AI and Gen AI. AI-powered chatbots can personalize conversations and interact just like humans.

If you’re not using AI, you’re already late to the party — Bank of America has been using its AI chatbot, Erica, since 2016. Bank of America claims that Erica has been able to provide more than 98% of its clients with the answers they need.

Step 4: Monitor and Improve

Optimizing business processes is not a one-off task. It’s an ongoing journey. Measure the results your optimization efforts yield with process metrics like cycle time, error rates, and customer satisfaction.

You can use AI-powered data analytics to gain deeper insights into process performance and find areas that require further improvement.

For example, if you recently optimized your loan origination process, use data analytics to monitor processing time.

Has it improved to the extent you expected? If not, use data to find out why. Is the optimized process still prone to errors? Or is your team still going through the learning curve?

 

The Role of Automation in Improving Financial Processes

Automation plays multiple roles in improving banking and financial processes. Here’s a quick overview of why automation is vital to your business process improvement initiatives:

  • Say goodbye to repetitive tasks and errors: You don’t need to spend thousands of dollars on data entry, reconciliations, and reports. Automation can perform those tasks more efficiently than humans and doesn’t make mistakes. Moreover, software allows you to keep costs under control even as you continue to scale.
  • Enforce standardized processes: You can create all the SOPs (standard operating procedures)you want. But there will always be someone trying to take a shortcut. Automation helps you achieve consistency in executing standardized processes — you can impose rules through your automated system to eliminate deviations from a standardized process.
  • Greater efficiency: Automation helps you multiply productivity. The more productive your bank or financial institution is, the more loans you can process, accounts you can reconcile, reports you can generate, and customers you can onboard without compromising on accuracy.
  • Improved customer experience: One of the biggest roles of automation in business process improvement is tailoring your processes to meet the customers’ needs. Automation allows you to personalize processes, eliminate errors, offer convenience, and deliver fast experiences at scale.
  • Deeper insights: Automating data collection and reporting helps you derive value out of the massive volume of data financial institutions generate. AI-powered analytics help you spot patterns and detect outliers. For example, if there’s fraudulent activity in a customer’s account, AI can pick up on that. Analytics also helps you make data-driven decisions, predict customer behavior, and assess risk exposure.
  • Improved customer experience: Automation allows you to improve customer experience across channels. Your AI chatbot can deliver personalized responses, analytics can help you predict customer behavior, and streamlined onboarding helps you make a great first impression.

Revolutionize Financial Services with Business Process Improvement

Let’s face it. Redesigning business processes comes with risk. However, the risk-reward ratio is in your favor when you use the right technologies and work with a team of experts. Strategic business improvement can transform your customer experience and generate massive cost savings for your business. 

Blanc Labs is committed to helping partners succeed through the use of cutting-edge technologies. Our approach can help you redesign processes in a way that fits your business’s needs. 

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