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What “raising the floor” means
The floor is the minimum level of useful explanation and service an ordinary customer, frontline employee, or smaller financial institution can expect. Generative AI can make plain-language summaries, multilingual assistance, policy searches, and first-line support cheaper and easier to scale than bespoke software or specialist help.
That does not mean every institution now has transparent models or excellent service. A conversational interface can make an opaque process feel more approachable without changing how the underlying decision was made. NIST distinguishes explainability, which concerns how a system operates, from interpretability, which concerns what an output means in context. Generative AI is often better at presenting an existing result than establishing why the system produced it.
Three layers determine whether an explanation is trustworthy
The model layer: what produced the result?
This includes the model, rules, data, and decision process behind an outcome. A language model that writes a clear explanation does not make this layer transparent. Existing explainability techniques can themselves be inaccurate or unstable, and can produce misleading accounts; the BIS Financial Stability Institute discusses these limits in its paper on how regulators can address AI explainability.
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The evidence layer: what supports the account?
A dependable explanation should be traceable to the actual factors considered, relevant records, and the policy or rule in effect at the time. It should distinguish a factor that drove a decision from one that merely correlates with it, and preserve enough information to reproduce the account later.
The communication layer: can a person understand it?
This is where generative AI can help most directly: turning validated information into a clearer summary, adapting the reading level, translating terminology, or answering a follow-up question. The communication layer should make a sound explanation more accessible—not manufacture a rationale after the fact.
Clear wording is not the same as a faithful explanation
Financial institutions should distinguish among several things that can sound alike to a customer:
- A faithful explanation accurately reflects the factors that actually contributed to the decision and has been validated.
- A post-hoc approximation estimates why a model produced an outcome after it happened; it may not faithfully represent the model’s reasoning.
- A policy explanation describes general rules but does not establish which factors caused a particular result.
- A service explanation makes a result easier to understand without proving its cause.
- A hallucinated explanation sounds plausible but cites a factor the system did not use.
Only a faithful, supported account can serve as a reliable explanation of a high-stakes decision. A fluent answer can be especially risky because an incorrect reason may sound empathetic and personalized.
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In the United States, creditors must provide specific and accurate principal reasons for adverse actions under the applicable fair-lending rules. The CFPB says that using a complex algorithm does not excuse a creditor from identifying the reasons actually considered or scored; a generic statement such as “failed to meet our criteria” is not enough when more specific reasons are required. The agency also says a creditor’s inability to understand its own model is not a defense. See the CFPB’s circular on adverse-action notices and complex algorithms and its 2023 guidance on credit denials involving AI.
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A safer design is to have the decision process produce structured, validated reason codes and relevant facts, then use generative AI only to explain those inputs in accessible language. The generated wording must not omit a principal factor, add a reason that was not used, or soften the meaning until it becomes inaccurate. If the system cannot support an answer from authoritative records, it should say so and route the case to a person—not invent a cause.
This U.S. lending example is not a universal statement of law for every country, product, or kind of financial communication. Requirements depend on jurisdiction and use case. NIST’s AI Risk Management Framework and its generative AI profile offer voluntary risk-management guidance, not a substitute for legal obligations: AI Risk Management Framework and Generative Artificial Intelligence Profile, published July 26, 2024.
How customers could get more useful explanations
Plain-language account of a decision
Instead of repeating a technical phrase such as “debt-to-income ratio exceeded policy threshold,” an assistant could explain what the measure means, which information was used, and what the customer can do if that information is wrong. It must preserve the actual reason rather than replace it with a more comforting but less accurate one.
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Follow-up questions and next steps
A static notice rarely answers every practical question. A controlled assistant could point a customer to the account or document involved, explain how income was calculated, describe how to submit updated documents, or identify the process for disputing information. It should not promise that correcting a record or taking a particular action will guarantee approval.
Language and accessibility
AI may help translate financial material, offer voice interaction, simplify technical vocabulary, and produce screen-reader-friendly summaries. Translation is not automatically reliable: legal and financial terms can carry precise meanings. Controlled terminology, qualified language review, testing across dialects and contexts, and access to a human are important safeguards.
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Different audiences, different views
A customer needs an accurate reason and a route to correct or challenge information. A frontline employee needs the source policy, uncertainty signals, and escalation criteria. Risk and compliance teams need reproducible records, validation evidence, and monitoring. Supervisors need independent evidence that explanations match the decision process. BIS Project Noor is a supervisory prototype intended to help assess and interpret financial institutions’ AI models; it is not a universal regulatory standard, and institutions remain responsible for explainability. BIS Project Noor.
Access means more than getting approved
AI-assisted service can improve access to information and help people use financial products, but those are not the same as receiving credit or getting better terms. It is useful to separate:
- Application access: how easy it is to apply or ask for help.
- Information access: whether people can understand products, fees, rights, and options.
- Consideration: whether more applicants can be evaluated.
- Approval: whether more applicants are accepted.
- Affordability and quality: whether approved customers receive suitable terms and can manage them.
Generative AI can make the first three easier without improving approval rates or affordability. Alternative data and automated underwriting may have potential to improve efficiency or expand consideration, but they also raise concerns about discrimination, privacy, and inaccurate predictions. The CFPB discusses both the potential and risks in its overview of adverse-action notices and AI/ML models.
Access also depends on whether a person has reliable internet, a suitable device, confidence using automated systems, and a way to reach a human. Chat may help people who need support outside branch hours or in a language a call center does not consistently offer, while excluding people who are offline, have limited digital literacy, or prefer a person. Maintaining appropriate telephone, paper, branch, or other human routes is part of access—not an optional extra.
Where generative AI is most useful in financial services
Customer and employee assistants
Assistants can search approved policies, explain account processes, help with complaint intake, and summarize documents. Their answers should be grounded in approved, versioned sources, show where important claims came from, and escalate uncertainty rather than improvise.
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Adverse-action explanation drafting
AI can potentially turn structured and validated reasons into clearer notice language. It should not infer reasons from a completed decision or draft a legally operative notice without controls, validation, and accountable review.
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Fraud and transaction alerts
An assistant may explain what a customer needs to do to verify a transaction or secure an account. It must avoid revealing detection thresholds or other sensitive details that could help someone evade fraud controls.
Document and policy summaries
Summaries of product terms, regulatory material, or internal procedures can reduce the effort required to find relevant information. They should identify the source, effective date, jurisdiction, exceptions, and definitions, and provide a link back to the full document.
Financial-health education
A conversational tool can help people understand budgets, repayment choices, or trade-offs. But users may mistake educational guidance for individualized investment, tax, lending, or insurance advice. The institution should define the tool’s role and apply the relevant controls and disclosures rather than allowing a general-purpose chat interface to drift into regulated advice.
Risk, audit, and supervisory support
For trained users, AI can help search model documentation, compare cases, identify missing records, suggest test cases, and summarize incidents. These tools can be valuable when they help people find evidence faster while leaving judgment and accountability with qualified staff. Supervisory exploration is also under way through efforts such as BIS Project Noor, which focuses on model evaluation rather than merely customer-facing prose.
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What a responsible explanation system needs
A practical architecture separates the decision from its presentation:
- Record the decision: preserve the relevant model and policy versions, inputs, output, and structured reasons.
- Validate the rationale: confirm that the reasons correspond to the actual decision process and identify uncertainty or missing evidence.
- Retrieve authoritative content: supply approved policy, account, and jurisdiction-specific material with effective dates.
- Generate the presentation: adapt language, format, or translation without changing the underlying reasons.
- Apply controls: block unsupported claims, protect sensitive information, and route uncertain or disputed cases for review.
- Keep an audit record: retain the sources, versions, and exact response needed to investigate complaints and reproduce what the customer saw.
Before deployment, institutions should test whether explanations are faithful, specific, actionable, and understandable. They should test translations and equivalent prompts across demographic and language variants; monitor unsupported claims, consistency, complaints, escalations, and disparities; and check for stale policies or model drift after material changes. Privacy controls should restrict access to personal data and sensitive fraud or underwriting information. Human reviewers need enough evidence and uncertainty information to challenge a polished but potentially wrong answer.
NIST’s AI Risk Management Framework is a voluntary resource for organizing AI risk controls. Its generative AI profile addresses risks and actions across the AI lifecycle. Neither creates a universal legal safe harbor or replaces obligations that apply to a particular financial product or jurisdiction.
The last mile is whether people can act
An explanation is useful only if it helps someone understand what happened and do something meaningful with that knowledge. Can the person check the data, correct an error, submit documents, dispute a result, appeal, or reach a human who can reconsider the case? If not, a more polished answer may simply make a dead end easier to read.
The right measure of progress is therefore not how natural the chatbot sounds. It is whether people can understand and verify the explanation, correct bad information, obtain appropriate recourse, and receive fair and accessible service—with the institution still accountable for the decision.
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