Navigating AI Vendor Selection: Regulator-Ready Strategies for Financial Institutions

In the rapidly evolving landscape of artificial intelligence, financial institutions face a critical challenge: embracing AI innovation with both speed and prudence. While nearly half of all financial entities are already piloting or implementing generative AI, driven by fears of falling behind, regulatory bodies are far more concerned with accountability, transparency, and explainability than with competitive timelines.

The traditional vendor evaluation processes were simply not designed for the complexities of AI systems. Banks often find themselves caught between the urgency to adopt new technologies and the imperative to manage unprecedented risks. The path to successful AI integration requires asking tougher, AI-specific questions that most standard due diligence checklists overlook.

Why AI Vendor Due Diligence is More Critical Than Ever

• Reckless AI adoption is a significant liability. Despite widespread enthusiasm, regulators prioritize the ability of banks to understand, trust, and defend AI systems making decisions.
• Traditional vendor risk management (TPRM) is inadequate for AI. Standard checklists fail to probe critical areas like AI decision-making logic, error detection, or a bank’s ability to intervene when models malfunction.
• Competitive pressure often distorts decision-making. Many institutions select AI vendors based on peer announcements or executive “fear of missing out” (FOMO), rather than clearly defined operational needs or measurable business impact.
• Regulators demand actionability, not just innovation. Financial institutions must be able to observe, explain, diagnose, and remediate AI-driven decisions, especially in sensitive areas such as credit, fraud, and customer communications.
• Your vendor’s compliance is ultimately your compliance. If an AI system lacks documented decision logic, proven bias testing, or clear accountability, the regulatory and reputational burden falls squarely on the bank, not the vendor.
• Success hinges on disciplined urgency. Banks that proactively engage stakeholders, rigorously question vendors, and embed governance into contracts will outpace competitors while minimizing regulatory backlash.

Beyond Hype: Identifying Your Real AI Needs

Before engaging with any AI vendor, a crucial first step is to define the exact problem you aim to solve. Resist the urge to chase competitor announcements or industry buzzwords. Instead, pinpoint specific operational pain points that are currently costing your institution money or customers.

A competitor’s new AI chatbot might be generating buzz, but its true performance or cost-effectiveness remains unknown to you. Focus internally: gather direct feedback from customers and employees to identify genuine challenges. Then, map potential AI solutions against their strategic relevance and business criticality. This approach ensures that resources are allocated based on actual impact, not fleeting trends.

Cultivating Internal Alignment for AI Success

One of the primary reasons AI implementations fail is a lack of internal alignment. It’s common for IT teams to discover new vendor contracts at the last minute, or for compliance officers to learn about data processing only after a system is live. Such disconnects create significant obstacles.

Prioritize bringing key stakeholders together early in the process:

• IT Teams: Review technical architecture and integration needs.
• Operations: Quantify realistic ROI, moving beyond vendor projections.
• Marketing: Develop a customer adoption strategy with specific, measurable goals.
• Compliance: Identify potential regulatory exposures before contracts are signed.
• Frontline Staff: Ensure they understand the new tools and their value proposition.

Skipping these vital conversations often leads to budget waste and widespread organizational resistance, making successful AI deployment incredibly difficult.

Essential AI Vendor Questions Beyond Standard Checklists

Traditional Third-Party Risk Management (TPRM) questionnaires are often insufficient for evaluating AI. You need to press vendors on areas where vague assurances are common, demanding clear documentation instead.

1. Data Ownership: What Happens to Our Data?

Ask direct, unequivocal questions:

• Does our data contribute to training or improving your models?
• Who maintains ownership of the outputs generated by your system?
• What are the protocols for data handling upon contract termination?
• What safeguards are in place for our data if your company is acquired?

Any hesitation or evasiveness in their answers should be a major red flag.

2. Explainability and Actionability: Can You Defend AI Decisions?

Challenge vendors to explain a specific AI decision in detail, not just general model principles. For instance, if your bank uses AI for credit decisions, the Equal Credit Opportunity Act mandates specific reasons for adverse actions. A response like “Our AI model determined you don’t qualify” is legally insufficient.

The focus should shift from merely understanding how a model works internally (interpretability) to ensuring you can act when it errs (actionability). When an AI system denies a loan or flags fraud, your bank needs to demonstrate:

• The precise reasoning steps leading to the conclusion.
• Identification of which specific step failed in case of an error.
• Availability of logging and monitoring tools to trace decision pathways.
• The speed and mechanism for remediating identified problems.

During demos, present a real-world scenario from your operations and demand a walk-through of the system’s handling and how you would intervene if something broke. If they cannot clearly show the decision pathway or explain diagnostic processes, consider it a dealbreaker.

3. Bias Detection and Mitigation: How Do You Ensure Fairness?

Every AI vendor will claim they test for bias, but few will provide concrete methodology or results. This is critical because algorithmic bias can lead to strict liability issues, regardless of intent.

Request comprehensive documentation of:

• Disparate impact testing across various demographic groups.
• The frequency and rigor of these tests.
• Findings from bias tests and the actions taken to address them.
• Any third-party audit reports related to fairness.

Vendors committed to fairness will have this detailed documentation readily available; others will likely offer vague promises.

4. Model Governance: Who is Accountable When Systems Fail?

AI systems are not infallible. Models can drift, data quality can degrade, and security vulnerabilities can emerge. The key is whether your vendor has robust accountability structures beyond mere incident response documents.

Demand specifics:

• Who holds primary ownership of this model within their organization (by name and title)?
• How is independent validation conducted?
• What are their protocols for monitoring performance degradation?
• Detail their escalation procedures when issues arise.
• Provide evidence of active change management, such as a log from the past six months.

A lack of specific names or demonstrable governance indicates that you might face a cumbersome support ticket system instead of empowered problem-solvers when issues inevitably arise.

Strengthening Your AI Vendor Contracts

Standard vendor contracts often lack crucial AI-specific protections. Incorporate these elements:

• Enforceable Delivery Commitments: Include penalty clauses for missed feature delivery dates, especially with early-stage vendors who might over-promise.
• Technical Data Portability: Define precise data export mechanisms, including format specifications and API access, and the duration the vendor must support transitions.
• Performance Thresholds with Remediation Rights: Establish specific AI performance metrics (e.g., accuracy below X%) and define your rights to terminate without penalty if these thresholds are not met.
• Actionable Audit Rights: Clearly define when and how you can conduct audits, what you are permitted to review, and the consequences of uncovering problems.
• Liability Caps Reflecting Actual Risk: Negotiate liability terms that adequately cover the regulatory and reputational risks associated with AI systems handling sensitive data or making critical decisions, moving beyond standard fees-paid limits.

Considering Early-Stage AI Vendors

While some community banks avoid early-stage AI vendors due to perceived risk, this overlooks potential advantages. Newer companies with strong fundamentals can offer:

• The ability to influence their product roadmap.
• Rapid support response times.
• Significant pricing discounts in exchange for references.
• A strong motivation to ensure your success, as it impacts their future funding rounds.

A cash-flow positive, two-year-old company with experienced founders might pose less risk than a ten-year-old unprofitable firm struggling with product-market fit.

Developing Robust Exit Strategies

Every vendor relationship eventually concludes. Success lies not in selecting a perfect vendor, but in having the flexibility to adapt. Your vendor might be acquired, discontinue a product, face financial difficulties, or you might simply find a superior alternative.

Periodically test your exit plan. Attempt to export your data. Research alternative vendors. An untested plan is likely to fail precisely when you need it most.

The True Essence of AI Vendor Evaluation

Evaluating AI vendors transcends mere IT or procurement tasks; it’s fundamental to your institution’s strategic risk posture. Regulators have made it clear: your vendor’s compliance is inseparable from your own.

Achieve success by prioritizing actionability over abstract interpretability, demanding concrete transparency, and designing systems that allow for effective monitoring and intervention. Begin by addressing your institution’s specific pain points, foster strong internal alignment, ask the hard questions of potential vendors, and always keep your options open. This disciplined approach enables financial institutions to adopt AI safely and effectively.

Source: Thefinancialbrand.com