5 Tips for Evaluating AI Tools Without Violating Banking Regulations

Artificial intelligence presents a massive opportunity for banks to enhance efficiency, personalize customer experiences, and detect fraud more effectively. However, the rush to adopt these powerful tools brings significant regulatory risks. For an industry built on trust and compliance, evaluating AI solutions requires a careful, methodical approach. Many providers of IT advisory services for financial institutions are now guiding clients through this complex landscape, ensuring that innovation does not come at the cost of regulatory adherence.

Navigating the intersection of AI and banking regulations can be daunting. This article offers five essential tips for evaluating AI tools while keeping your institution safe, secure, and compliant.

1. Start with Robust Vendor Due Diligence

Before you even consider the technical capabilities of an AI tool, your vendor management process must kick into high gear. Regulators view your third-party vendors as an extension of your own operations, meaning you are responsible for their compliance failures. An inadequate vetting process is one of the most common pitfalls.

Your due diligence should scrutinize the vendor’s security posture, financial stability, and regulatory track record. Ask for their SOC 2 reports, independent security audits, and any documentation related to data governance. It is crucial to understand how they handle, store, and protect data, especially if it involves sensitive customer information. A vendor unwilling to provide this transparency is an immediate red flag.

2. Scrutinize Data Privacy and Governance

AI tools are only as good as the data they are trained on. This creates a significant challenge for banks, which handle vast amounts of non-public personal information (NPPI). When evaluating an AI solution, you must have absolute clarity on its data requirements.

Ask potential vendors:

• What specific data fields does the tool need to function?
• Will our customer data be used to train models for other clients?
• Where will the data be stored, and how is it segregated and encrypted?

Violating data protection laws like GLBA is not an option. Ensure any AI tool you consider allows you to maintain full control and ownership of your data. The contract should explicitly forbid the vendor from using your data for any purpose not directly related to the service provided to your institution.

3. Conduct a Thorough Risk Assessment

Implementing any new technology requires a risk assessment, but with AI, the scope is much broader. You must go beyond standard cybersecurity risks and evaluate potential issues related to model bias, transparency, and decision-making. Regulators are increasingly focused on “explainability”—the ability to understand and justify why an AI model made a particular decision.

For example, if an AI tool is used for loan approvals, you must be able to explain why an applicant was denied. A “black box” model that cannot provide this reasoning could lead to fair lending violations. Your risk assessment must document these new types of risks and outline the controls you have in place to mitigate them.

4. Involve Compliance and Legal Teams Early

IT departments cannot and should not evaluate AI tools in a silo. Your compliance and legal teams must be involved from the very beginning of the process. They are the experts on the specific regulations your bank must follow and can spot potential issues long before they become major problems.

These teams can help review vendor contracts for unfavorable terms, assess the tool’s impact on your existing compliance framework, and ensure its outputs do not create legal liabilities. Bringing them in early avoids the costly and time-consuming scenario of discovering a deal-breaking compliance issue after months of technical evaluation.

5. Plan for Ongoing Monitoring and Governance

Your responsibility does not end once the contract is signed. AI models can “drift” over time as new data is introduced, potentially leading to biased or inaccurate outcomes. Regulators expect you to have a governance framework in place for the ongoing monitoring of any AI system.

This includes regularly testing the model’s performance, reviewing its decisions for fairness, and ensuring it continues to operate as intended. Establish clear ownership for this oversight within your organization. Documenting this continuous monitoring process is critical for demonstrating to examiners that you are actively managing the risks associated with AI.

Conclusion: Innovate with Confidence

AI holds immense promise, but for banks, the path to adoption must be paved with diligence. By treating AI evaluation as a multidisciplinary effort that prioritizes vendor management, data privacy, and risk assessment, you can harness its power without running afoul of regulators.

Given the complexity, seeking expert guidance is a wise investment. Partnering with advisors who specialize in financial technology and compliance can provide the clarity and confidence you need to make smart, safe decisions as you embrace the future of banking.