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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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