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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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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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Financial Services | Business Process Improvement

What is the role of a Business Process Improvement Specialist? 

February 16, 2024
Business Process Improvement Specialist in Canada

What is the role of a Business Process Improvement Specialist in Canada?

Business process improvement (BPI) involves analyzing current processes, identifying room for improvement, and redesigning processes to make them more efficient. The problem? Analyzing processes, using data to find inefficiencies, and redesigning and implementing processes with minimal disruption requires skill and experience. That’s where a business process improvement specialist steps in. 

You might tackle various challenges when working to improve your processes. According to Capgemini, the most prominent business process management challenges are functional silo culture and fragmented budgets. 

Redesigning processes while tackling these challenges can be overwhelming, but specialists are equipped with the experience needed to make the transition smoother. In this guide, we discuss the business process improvement workflow and how a specialist can make things easier for you.

The Need for Business Process Improvement before Automation

You’re investing in automation to improve business processes. Before you put money on the table, it’s vital to learn how this investment will impact your business’s efficiency and bottom line.

At its core, business process improvement is about driving efficiency and improving performance through streamlined operations. A critical analysis of business processes and workflows enables you to identify bottlenecks and inefficiencies, allowing you to focus efforts on automating the right processes.

Business process improvement is undeniably mission-critical to make your business future-ready, but it’s also important to:

  • Develop a competitive advantage: Prioritizing continuous improvement and standardizing processes can help build competitive advantages like cost leadership, differentiated products or services, better quality, scalability, and agility.
  • Improve efficiency: Zeroing in on processes you can automate or streamline improves efficiency and output. For example, you can use technologies like intelligent document processing (IDP) to streamline invoice processing. This frees up your finance team to focus on more strategic tasks like cash flow analysis.
  • Elevate customer experience: Modern customers have some ground rules. They want fast responses, consistent experiences, and personalized interactions. Modern lenders provide hassle-free digital experiences to SMB clients, where document collection is a single step and approvals take hours and not weeks.  

“The fact is that consistency on the most common customer journeys is an important predictor of overall customer experience and loyalty. Banks, for example, saw an exceptionally strong correlation between consistency on key customer journeys and overall performance in customer experience.”

The Three Cs of customer Satisfaction, McKinsey & Company

  • Foster a culture of innovation: Striving for continuous improvement encourages critical thinking, offers your team a platform for experimentation, and rewards innovation. For example, if you’re a financial institution trying to automate your loan origination process, a culture of innovation would encourage employees to take charge of every step, from process analysis to pilot implementation.

Business Process Improvement Workflow

Before you take the first step, see if you can get a business process improvement specialist on board.

Their experience and expertise go a long way in minimizing workflow disruption and efficiently designing and executing new workflows. With that in mind, let’s talk about how you can improve business processes.

Process Mapping

Process mapping involves creating a visual representation of your current business processes. You map every step, interaction, and decision point in your workflow from start to finish when mapping a process.

This map gives you a starting point to understand how the current process works. Identifying bottlenecks or ways to make a process more efficient is easier when you can view the process rather than just verbally discussing the steps in a process.

Root Cause Analysis

Time to play detective. Root cause analysis involves digging deeper into finding the underlying reasons behind a specific problem or inefficiency. For example, if you’re a financial company experiencing delays in processing customer payments, here’s what finding the root cause might look like:

  • Why are employees encountering difficulties processing the payments? Probably because employees are spending time fixing errors in payment records.
  • Why are there discrepancies in the payment records? Maybe the outdated system is causing technical problems or employees need more training.
  • Why are we using outdated systems and not investing in training? Lack of budget.

 

This is how root cause analysis takes you to a problem’s roots. In the example above, the finance team could request additional funds in the next quarter to invest in a system upgrade and training to process customer payments faster.

Blanc Labs’ Business Process Improvement Framework

Cause and Effect Analysis

Use techniques like fishbone or Ishikawa diagrams for a visual representation of the cause-and-effect relationship between factors impacting your business processes and problems.

Categorize problems into multiple buckets like people, processes, equipment, and external factors. This will help you gain insights into various elements that impact your business processes and prioritize the ones with the biggest positive impact.

Statistical Process Control

Numbers don’t lie — statistical process control (SPC) helps you understand your process through numbers. SPC enables you to identify trends, detect abnormalities in the process, and make data-driven decisions to improve your process.

Design of Experiments

Once you’ve identified a problem, you need a systematic and structured way to identify potential improvements and test them. Experimentation helps you do exactly that.

It’s the same as stress-testing your processes under various scenarios. You design an experiment, manipulate relevant variables, and measure their impact on the business process. This enables you to understand the impact of a change before you fully implement it, allowing you to minimize disruption.

How BPI and Intelligent Automation can improve the Borrowing Experience for Small Business Owners

BEFORE
AFTER

A Frustrating Lending Experience

Small business owner_loan approval

Alex applies for a loan with a traditional commercial lender to expand her cafe into a local chain. The onboarding process is frustrating and time-consuming, with lengthy paperwork and no guidance. The lender’s inaccurate reporting and static risk assessment results in conservative loan terms that fail to acknowledge her business’s recent growth and the positive trends in the local market. This jeopardizes Alex’s ability to secure an ideal property for her new location and erodes her confidence in the lender’s support for her business growth. Throughout the process, Alex experiences an impersonal service, devoid of the understanding and support crucial for an entrepreneur.

Small business owner_loan approval

A Transformative Lending Experience

Small business owner_lending

Six months later, Alex chooses a lender with a digital platform and automated loan decisioning for her next expansion. With streamlined onboarding, automated data extraction, and intelligent chatbot, the experience is transformative. Real-time updates, automated tracking, intelligent document processing, and dynamic risk assessment make loan processing efficient and user-friendly. Alex secures her loan promptly and finds the perfect location for her new café. The chatbot also offers personalized customer experience, making Alex feel valued.

Small business owner_lending

The Role of a Business Process Improvement Specialist

Process mapping, analysis, and re-engineering require a specialist. A specialist has the knowledge and experience required to minimize workflow disruption and effectively realize process efficiencies. Here’s how a business process improvement specialist helps:

Pain Point Discovery

Think of business process improvement specialists as Sherlock Holmes. They come in, zoom into your processes, and find pain points that hinder efficiency.

They don’t just observe. They conduct interviews and surveys, ask the right questions, and listen attentively to discover areas that are inefficient, error-prone, and not aligned with organizational goals. The discovery phase is vital — it sets the stage for targeted improvements.

Process Analysis

When the specialist finds room for improvement in a process, they pull up their sleeves and start dissecting it.

They map every step, decision point, and interaction in your process and use techniques like value stream mapping, data analysis, and flowcharting to take a closer look at how the process currently operates and where bottlenecks and inefficiencies lie. This lays the groundwork for informed and data-backed decision-making and process design.

Opportunity Analysis

Insights from process analysis equip the specialist to shift focus to opportunity analysis. They consider the benefits of addressing a pain point and improving the process versus the cost — do the monetary benefits of redesigning the process justify (and exceed) the cost of making those improvements?

Cost-benefit analysis requires input from multiple experts. For example, if a process redesign could improve customer experience, what would be the benefit, in monetary terms, of improved customer experience? The specialist might need insights from multiple teams like sales and accounting to understand the monetary impact.

They also consider the risks of redesigning the process. Will the redesign require significant changes? How long will it take the employees to get through the learning curve and achieve full efficiency? The specialist understands these risks and identifies ways to mitigate them.

Implementation Design

After factoring in the costs and risks associated with redesigning a process and verifying the viability of a process redesign, the specialist transitions into creating and implementing a more efficient process design.

The redesigned process might involve one or multiple changes like a more efficient workflow, new technology, or a redesigned organizational structure. For example, the specialist might use enterprise automation solutions to automate repetitive tasks, potentially multiplying your team’s productivity or include GenAI to enable the next level of customer service.

The specialist develops a detailed implementation plan including the details of redesign, clear timelines, responsibilities, and performance metrics. They collaborate with stakeholders during this phase to ensure buy-in and alignment with organizational objectives.

Business Process Improvement Integration

Process improvement isn’t a one-off activity. To truly reap the benefits of process improvement, the specialist must integrate the improvement initiatives into the organization’s culture.

The specialist can help you develop change management strategies to inculcate a culture rooted in continuous improvement; this is vital to engaging employees across the organization and encouraging them to contribute ideas that drive change.

For example, the specialist can train teams to build applications quickly using low-code platforms. This enables teams to solve problems independently and quickly. Of course, there are many ways to develop a culture of continuous improvement depending on factors like industry. For example, Bill Keen, CEO of Keen Wealth Advisors, explains:

“By owning mistakes and building processes to prevent the mistakes that do occur from happening again, you will build trust among your customers and a culture of continuous improvement among your staff.”

With training and efficient feedback mechanisms, you can sustain process improvements for a long time and ingrain them in your company’s DNA.

Business Process Improvement with Blanc Labs

We firmly believe that transformation happens over time and that process improvement is an ongoing journey. Our approach helps you practically redesign processes effectively and consistently, and in a way that fits your business’s unique needs.

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Canadian IT services firms offer a strategic opportunity for US Banks and FIs

Insight | Banking Automation | IT Management | Nearshore IT Support

Canadian IT services firms offer a strategic opportunity for US Banks and FIs

January 29, 2024
Understanding the Strategic Advantages of Canadian Nearshore IT Support for U.S. Banks_Blanc Labs

In today’s challenging environment, U.S. bank IT departments are under unprecedented pressure to deliver more with limited resources. Cost-effective Canadian nearshore IT support, especially from technology-rich regions like Toronto, are emerging as a compelling opportunity for U.S. banks seeking to transform their technology infrastructure and enhance operational efficiency.

Toronto: Access to World-Class Technology Talent

Toronto is recognized globally as a Canadian center for technology and fintech companies. This world-class and fast growing city with 6.5 million citizens in 2023 is a dynamic and creative urban environment that at this point has become a self-sustaining financial technology hub, making it an ideal partner for U.S. banks seeking talent to drive their transformation roadmaps further and faster.

The region is home to top-tier universities like the University of Toronto, Waterloo and York University are renowned for their research and technology programs, providing a steady stream of skilled graduates and fostering a culture of innovation. Additionally, Toronto hosts the MaRS Discovery District, one of the world’s largest urban innovation hubs. MaRS provides a platform for tech start-ups and entrepreneurs, further solidifying Toronto’s status as a leader in technological development and innovation.

 

Proximity and Time Zone Alignment

Toronto’s geographical proximity, where almost any major U.S. city can be easily reached with a short, direct flight, offers significant benefits, including time zone alignment for real-time collaboration and agile project management. This is particularly advantageous compared to offshoring and other options. The ease of travel not only strengthens business relationships but also ensures effective communication and alignment, which are critical for the success of complex IT projects.

Cost-Effectiveness

Choosing Toronto for nearshore IT support allows U.S. banks to maintain high service standards while managing operational costs. The significant savings, often ranging from 30-50%, are a result of the current exchange rate differences between the U.S. and Canada, coupled with the inherent efficiencies in project management and execution. These financial benefits can be redirected towards other areas of growth and innovation.

Regulatory and Cultural Alignment

The U.S. and Canada share many regulatory and cultural similarities, which simplifies compliance standards and business practice alignment. This is vital for U.S. banks to minimize legal and operational risks in the highly regulated banking sector. 

Customized and Scalable Solutions

Nearshore IT support in Toronto offers tailored, scalable solutions to meet specific banking needs. This flexibility ensures IT services adapt to the bank’s growth and changing priorities, promoting long-term sustainability. Moreover, these solutions are market-proven and highly relevant for U.S. banks, having been successfully implemented and delivering tangible results in similar banking environments.

Enhanced Security

Canadian nearshore IT support provides enhanced security, ensuring compliance with North American data protection laws. This safeguards sensitive information and customer data. Look for a SOC2 certification, which signifies a firm that can adapt to the stringent security requirements, a crucial aspect for U.S. banks in safeguarding their operations and customer information.

Looking for a Canadian tech partner? Let us help.

For U.S. banks striving to stay ahead in innovation, cost-effective Canadian nearshore IT support is a compelling strategic choice. Partnering with Toronto-based firms, such as Blanc Labs, maximizes these benefits, leading to improved efficiencies, fostering innovation, and enhancing customer satisfaction, significantly contributing to the bank’s success.

Author

Les Riedl
US Business Advisor

Les Riedl, Managing Principal of 10XBizDEV, is dedicated to connecting US Banks with innovative, best-in-class solutions and services that contribute to their success. His extensive experience as CEO and board member in fintech and financial services consulting provides valuable insights into the sector’s evolving challenges and opportunities. As US advisor for Blanc Labs, Les is instrumental in introducing their world-class capabilities to the American market.

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