Why Explainability Is Becoming a Procurement Requirement in Lending AI

When a lender uses artificial intelligence to assess a loan application, the final decision is only part of the story. Credit teams, compliance officers, auditors, and borrowers may also need to understand why the system reached that outcome. This is why explainable AI lending is becoming an important consideration when financial institutions evaluate technology vendors. Accuracy matters, but lenders also need systems that can provide understandable reasons for their decisions, support oversight, and produce evidence when questions arise.

As AI becomes more involved in credit assessment, procurement teams must look beyond processing speed and predictive performance. They need to establish whether a system can explain its outputs in ways that meet the institution’s operational, regulatory, and accountability requirements.

Why Explainability Matters in Lending Decisions

AI models can identify patterns across large volumes of financial and behavioral data. These patterns can help lenders assess creditworthiness, identify potential risks, and prioritize applications. However, complex models may produce results that are difficult for people to interpret.

A model might reject an application because of several interacting variables rather than one obvious factor. Without a useful explanation, a loan officer may struggle to understand the result, a compliance team may find it difficult to investigate a complaint, and a model risk team may have limited insight into the system’s behavior.

Explainability helps address these challenges by making model outputs more understandable to relevant stakeholders. It can also help lenders distinguish between a reasonable risk assessment and a result that warrants further investigation.

The CFA Institute’s 2025 report on explainable AI in finance examines the importance of transparency and interpretability across financial services. Its focus reflects a broader concern: AI decisions affecting people’s access to credit require appropriate oversight and accountability.

Regulation Is Raising the Stakes for AI Procurement

Regulatory expectations vary by jurisdiction, product, and use case, but explainability is closely connected to several established obligations.

In the European Union, the AI Act classifies certain AI systems used to evaluate the creditworthiness of natural persons as high-risk, subject to specified exceptions. Relevant requirements include risk management, technical documentation, record-keeping, and human oversight. These obligations make governance and documentation important considerations when selecting lending technology.

In the United States, lenders must also consider existing consumer protection and fair lending requirements. For example, the Equal Credit Opportunity Act and Regulation B require creditors to provide specific reasons for adverse credit decisions. Using a complex algorithm does not, by itself, remove that obligation.

The U.S. Government Accountability Office’s May 2025 report on AI in financial services identifies potential risks associated with AI use, including lending bias and cybersecurity concerns.

For procurement teams, the practical implication is clear: vendors should be able to demonstrate how their systems support applicable legal and regulatory obligations. Buyers should verify these capabilities rather than assume that a vendor’s general claims about responsible AI are sufficient.

What Procurement Teams Should Ask AI Vendors

Explainability should be assessed during vendor evaluation, not added as an afterthought after implementation. Procurement teams can use several questions to examine a system’s capabilities.

1. Can the system explain individual decisions?

Ask whether the vendor can identify the main factors contributing to a particular output. Explanations should be understandable to the intended audience, whether that means a credit analyst, compliance officer, or customer-facing employee.

2. Can the vendor document how the model works?

Request information about the model’s intended use, input data, key assumptions, known limitations, and validation methods. The level of detail should reflect the system’s complexity and the risks associated with its use.

3. Can explanations be reproduced during an audit?

A lender may need to reconstruct a decision months after it was made. Ask whether the system records relevant inputs, outputs, model versions, and changes to decision logic. Confirm how long records are retained and whether they can be exported for independent review.

4. How does the vendor test for bias and performance changes?

A model that performs well during initial testing may behave differently as borrower characteristics, economic conditions, or input data change. Ask how the vendor monitors performance, evaluates outcomes across relevant groups, and identifies material deviations.

5. What happens when a decision needs human review?

Determine whether employees can investigate unusual outcomes, challenge model recommendations, and escalate cases appropriately. Human oversight should have a defined role rather than exist only as a contractual assurance.

Explainability Must Be Useful, Not Merely Technical

A vendor may provide feature-importance scores, technical documentation, or a visualization of how a model reached an output. These tools can help specialists, but they do not automatically give every stakeholder a meaningful explanation.

For example, a model might identify a high debt-to-income ratio as an important factor in a lending decision. A credit analyst may find that information useful. A borrower, however, may need a clearer explanation of how existing financial commitments affected the application.

Procurement teams should therefore distinguish between technical interpretability and explanations that support real decisions. They should also ask whether the explanation accurately reflects the model’s behavior. A plausible-sounding explanation is not necessarily a faithful account of how an AI system produced its output.

This distinction matters when assessing lending AI capabilities and their role in loan management. The relevant question is whether the technology provides evidence that supports review and accountability, rather than simply presenting a convincing narrative after a decision has been made.

Build Explainability Into the Procurement Scorecard

Financial institutions can make explainability easier to assess by including it in their formal vendor evaluation criteria.

A procurement scorecard could cover:

Decision transparency: Can the system provide meaningful reasons for individual outputs?
Audit readiness: Are decision records, model versions, and relevant inputs available for review?
Governance: Are responsibilities for validation, monitoring, and escalation clearly defined?
Fairness testing: Can the vendor demonstrate how potential bias is assessed and addressed?
Human oversight: Can authorized employees review and challenge outcomes?
Integration: Can explanations and supporting records be accessed through existing lending workflows?
Vendor accountability: Are reporting obligations, documentation access, and change notifications addressed in the contract?

These criteria should be proportionate to the model’s purpose and risk. A tool that extracts information from loan documents may require different controls from a model that recommends approval or rejection.

The revised U.S. interagency model risk management guidance, issued in April 2026, reinforces a risk-based approach to model oversight. It addresses model development, validation, monitoring, governance, and third-party products. Although its scope is specific to banking organizations, its emphasis on proportionate controls provides a useful reference for procurement decisions.

Conclusion

Explainability is becoming a procurement priority because lenders need to understand, review, and defend the use of AI in consequential financial decisions. Predictive accuracy remains important, but it cannot replace documentation, appropriate oversight, or compliance with applicable laws.

By evaluating explanation quality, auditability, fairness testing, and human review before selecting a vendor, financial institutions can make more informed technology decisions. The central question is no longer simply whether an AI system can produce a result. It is whether the lender can understand that result well enough to use it responsibly.