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By Katie Quilligan, Investor at BankTech Ventures
The acceleration of Artificial Intelligence (AI) adoption within financial institutions presents a critical balancing act. While many banks aggressively pursue cutting-edge solutions, others move cautiously, bogged down by extensive risk assessments. The key to success lies in integrating AI with both urgency and disciplined diligence, a path that demands a fundamental re-evaluation of traditional vendor selection processes.
The pressure on banks is undeniable. Roughly half of financial institutions are actively piloting or implementing generative AI, driven by the fear of falling behind competitors. However, regulators prioritize different concerns: explainability, transparency, and the ability of banks to truly understand, trust, and defend the AI systems making decisions on their behalf. The emerging regulatory focus centers on “actionability” – the capacity for banks to observe AI reasoning, diagnose failures promptly, and remediate issues effectively when they arise.
Key Considerations for Smart AI Adoption
- Reckless AI adoption is a significant liability. While nearly 50% of financial institutions are engaged with generative AI, regulators demand accountability and transparency over speed.
- Traditional vendor due diligence is insufficient for AI. Standard Third-Party Risk Management (TPRM) checklists often fail to assess the core mechanics of AI systems, including decision-making processes, error detection, or the bank’s ability to intervene.
- Competitive pressure can distort decision-making. Banks frequently select AI vendors based on industry buzz or competitor announcements rather than clearly defined operational needs or measurable business impact.
- Regulators prioritize actionability over sheer innovation. Banks must demonstrate the ability to observe, explain, diagnose, and remediate AI-driven outcomes, particularly in critical areas like credit, fraud detection, and customer interactions.
- Vendor compliance is inextricably linked to bank compliance. If an AI system lacks documented decision logic, robust bias testing, or clear accountability, the regulatory and reputational risk ultimately rests with the bank, not the vendor.
- Success demands both urgency and discipline. Financial institutions that proactively align internal stakeholders, ask incisive questions of vendors, and embed strong governance into contracts will lead the way without incurring regulatory backlash.
Prioritize Internal Problems, Not Competitor Announcements
Before engaging with any AI vendor, banks must first clearly define the specific operational problem they aim to solve. This means moving beyond generic industry trends or a CEO’s latest article. Focus on tangible pain points currently impacting your customers or costing your institution money.
Implementing AI out of fear of being left behind is a flawed strategy. A competitor’s AI chatbot, for example, might appear successful but could be confusing customers or incurring significant costs internally. Without direct insight, relying on external announcements is a risky gamble.
Begin by identifying precise pain points through direct feedback from both customers and employees. Then, evaluate potential AI solutions against two critical axes: strategic relevance and business criticality. For instance, a digital account opening platform might be business-critical for a bank expanding into new markets, but merely a convenience for one with stable customer acquisition. Resource allocation for vendor evaluation should reflect this reality, not simply industry hype.
Internal Alignment is Paramount
Many promising AI implementations falter due to a lack of internal alignment. Problems arise when IT teams are blindsided by new vendor contracts, compliance officers discover data processing details post-launch, or frontline staff receive inadequate training and actively deter customer adoption.
Before approaching vendors, ensure key stakeholders are unified:
- IT: Reviews technical architecture and integration requirements.
- Operations: Quantifies realistic ROI, not just vendor projections.
- Marketing: Develops a customer adoption strategy with specific metrics.
- Compliance: Identifies potential regulatory exposure proactively.
- Frontline Staff: Understands the new tool’s purpose and how to support it.
While seemingly obvious, skipping these crucial preliminary discussions often leads to wasted budgets and management focus on initiatives that lack broad organizational support.
Four Critical Questions for AI Vendors
Traditional TPRM questionnaires are ill-equipped for AI. Banks must probe deeper, challenging vague assurances with demands for clear documentation.
1. Data Ownership: What happens to our data within your system?
Ask directly:
- Does our data contribute to training or improving your models?
- Who holds ownership of the outputs generated by your system?
- What is the process for data retrieval and deletion upon contract termination?
- How is our data handled in the event of your company’s acquisition?
Any hedging or lack of specificity from the vendor should be a red flag.
2. Explainability and Actionability: Can you truly defend the AI’s decisions?
Present a specific, recent decision made by the AI system and demand a precise explanation of “why” – not general model principles. This is vital because regulations like the Equal Credit Opportunity Act require specific reasons for adverse credit decisions. “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 actionability. When an AI system denies a loan or flags fraud, your bank must be able to demonstrate:
- The exact reasoning steps leading to that conclusion.
- Identification of the specific step that failed during an error.
- Accessible logging and monitoring tools that trace decision pathways.
- The speed and mechanism for fixing identified problems.
Test this during the demo with a real operational scenario. If the vendor cannot clearly illustrate the decision pathway or how you would diagnose a failure, reconsider their solution.
3. Bias Detection and Mitigation: How do you test for fairness, and can we review the results?
Every AI vendor claims to test for bias, but few provide actual methodologies or results. This is critical, as algorithmic bias can lead to strict liability issues, regardless of intent.
Request documentation of:
- Disparate impact testing across various demographic groups.
- The frequency of these tests and the findings.
- Actions taken to address identified biases.
- Any existing third-party audits of their fairness metrics.
Vendors serious about fairness will have detailed documentation readily available. Those who don’t will likely offer vague promises without follow-up.
4. Model Governance: Who is accountable when systems inevitably fail?
AI systems are not infallible; models drift, data quality degrades, and vulnerabilities emerge. The crucial question is whether your vendor has robust accountability structures beyond mere incident response documents.
Ask for specifics:
- Identify the named individual and their title responsible for the model within their organization.
- Describe their independent model validation processes.
- Detail their methods for monitoring performance degradation.
- Outline their escalation protocols for system failures.
- Provide evidence of active governance, such as recent change management logs.
A lack of named accountability or visible governance indicates that resolving issues might be relegated to an impersonal support ticket system rather than empowered individuals.
Essential Contractual Protections for AI Solutions
Standard vendor contracts often overlook critical AI-specific protections. Banks should insist on incorporating the following:
- Enforceable Delivery Commitments: Include penalty clauses for missed feature delivery dates, especially with early-stage vendors who may over-promise roadmap items.
- Technically Defined Data Portability: Ensure “data export” is meaningful by specifying data formats, API access, and the vendor’s obligation to support transition for a defined period.
- Performance Thresholds and Remediation Rights: Clearly define metrics, measurement methodologies, and your right to terminate without penalty if the AI system’s performance (e.g., accuracy) falls below agreed thresholds.
- Actionable Audit Rights: Move beyond unused standard provisions. Define when and how audits will be conducted, the scope of review, and consequences for identified problems.
- Liability Caps Reflecting Actual Risk: Standard liability limits (e.g., fees paid) are insufficient for AI systems handling sensitive customer data or critical decisions. Negotiate terms that account for real regulatory and reputational exposure.
Evaluating Early-Stage AI Vendors
Community banks often shy away from early-stage AI vendors due to perceived risk, missing potential advantages. Robust early-stage companies can offer:
- Influence over their product roadmap.
- Highly responsive support.
- Significant pricing discounts in exchange for references.
- Strong motivation to ensure customer success, critical for their growth.
A well-funded two-year-old company with experienced founders and positive cash flow might pose less risk than a ten-year-old company still struggling with profitability and product-market fit.
Proactive Exit Planning
Every vendor relationship eventually concludes. Success isn’t about choosing a “perfect” vendor, but maintaining the flexibility to adapt. Scenarios such as vendor acquisition, product discontinuation, compliance issues, financial deterioration, or simply finding a better solution necessitate a solid exit strategy.
Regularly test your exit plan. Attempt to export your data and explore alternative vendors annually. Untested plans often fail precisely when they are most needed.
The True Bottom Line
AI vendor evaluation transcends a mere IT or procurement task; it is foundational to your bank’s strategic risk posture. Regulators have made it clear: your vendor’s compliance directly reflects your compliance.
Banks will succeed by prioritizing actionability over abstract interpretability, demanding concrete transparency over vague promises, and designing systems that enable clear monitoring and intervention. Instead of following competitors, begin with your institution’s specific pain points, ensure robust internal alignment, ask the hard questions, and maintain strategic flexibility. This approach allows financial institutions to adopt AI both safely and effectively.
Katie Quilligan is an investor on the BankTech Ventures team, where she finds the bank-enabling fintechs that would best serve their Limited Partner banks and the broader banking industry.
Source: thefinancialbrand.com