How banks can evaluate AI tools for regulatory compliance

Banks evaluating AI tools for regulatory compliance should start with a narrow, high-value use case — helping employees find accurate answers to policy and procedure questions faster — and assess the tool across six areas: problem fit, grounded knowledge access, security and governance, workflow integration, answer quality, and measurable business impact.

Regulatory requirements in banking keep growing in volume and complexity. Compliance teams, branch staff, and operations managers all need fast access to the same body of policies, procedures, and guidance — but that knowledge is scattered across document repositories, intranets, ticketing systems, and training platforms. When finding the right answer takes too long, front-line employees improvise, escalations spike, and inconsistency creates operational risk. A Bank Policy Institute survey found that between 2016 and 2023, employee hours dedicated to regulatory compliance increased by 61%, even as total employee hours grew only 20%.

AI tools built for banking-specific compliance workflows can close that gap, but only if banks evaluate them with the same rigor they apply to any new vendor in a regulated environment. The right framework focuses on retrieval quality, access controls, auditability, and real outcomes — not marketing claims about what AI might do someday.

Assess AI tools across six areas tied to bank outcomes

Start with the problem, not the technology. The strongest first use case for AI in banking regulatory compliance is internal knowledge access — giving compliance officers, branch managers, and operations staff a faster way to answer policy and procedure questions inside the tools they already use. That use case is concrete, measurable, and low-risk enough to pilot without enterprise-wide change management.

Evaluate the tool across six areas tied directly to bank outcomes:

1. Problem fit. Confirm the tool addresses a defined compliance need — such as reducing time-to-answer for front-line regulatory questions — rather than promising broad "intelligence."
2. Grounded knowledge access. Test whether the system can retrieve and surface cited answers from the sources your bank already maintains: policy repositories, procedure documents, intranet pages, collaboration platforms, and training content. According to Gartner's 2023 digital workplace survey, employees spend an average of 2.5 hours per day searching for information — and in a regulated environment, slow or wrong answers carry real consequences.
3. Security and governance. The tool must respect your existing permissions, support audit trails, and meet data residency requirements without workarounds. Glean Search, for example, delivers permission-aware, cited answers grounded in your company's knowledge by connecting to 100+ enterprise sources through its Enterprise Graph — so employees see only what they're authorized to access, and every answer links back to its source document.
4. Workflow support. The tool should meet employees where they work — inside ticketing systems, collaboration tools, and internal portals — rather than requiring a separate interface that breaks existing processes.
5. Answer quality. Measure quality through a structured pilot with defined users, a scoped content set, and clear success metrics: fewer escalations, faster resolution times, and stronger consistency in how regulatory questions get answered.
6. Human oversight. Require human oversight from day one. The goal is speed and consistency, not removing accountability. Any AI tool used in a regulated environment should make it easy for compliance teams to verify answers, flag gaps, and maintain control over how knowledge is surfaced and applied.

1. Define the regulatory knowledge and front-line support use case

Before evaluating any AI tool, pin down exactly where employees lose time or introduce risk when answering regulatory questions. Those moments are specific: a branch teller checking whether a new ID requirement applies to joint accounts, a call center agent hunting for the latest approved disclosure script, an operations specialist comparing two versions of a wire transfer procedure, or a compliance analyst fielding the same question about suspicious activity reporting thresholds for the third time that week.

Not every task carries the same stakes. High-frequency knowledge tasks — retrieving approved guidance, summarizing a procedure change, confirming which form to use — are strong first deployments because the answers already exist in your documents. A strong enterprise knowledge management strategy ensures those documents are organized, current, and accessible to the tools that need to retrieve them.

High-risk judgment tasks — deciding whether to file a SAR, granting an exception to a lending policy — require human decision-making and should stay outside an AI tool's scope during early pilots. Draw that line before you demo a single product.

Map the user groups who will interact with the tool: branch staff, contact center agents, operations teams, compliance analysts, and supervisors who review escalations. Then catalog the content sources the tool must connect to — policies, procedures, job aids, FAQs, training materials, audit guidance, and regulatory change notices.

Write three to five benchmark questions your front-line employees ask daily, such as "What is the current hold schedule for new accounts under Reg CC?" or "Where is the updated BSA/AML training checklist?" Assign a risk level to each use case, and define decision rights: who owns the pilot, who controls which content the tool can access, and who approves expanding the scope after initial results.

Glean Search connects to 100+ enterprise sources through its Enterprise Graph, which means you can scope a pilot to a defined set of policy repositories and expand only after validating accuracy against those benchmark questions.

2. Check whether the tool grounds answers in approved regulatory and policy sources

The first product test is straightforward: ask the tool a question your compliance team fields regularly, and check whether the answer cites the specific internal document it drew from. If the response reads well but offers no source trail, that fluency is a liability. In a regulated environment, an ungrounded answer — no matter how articulate — is indistinguishable from a hallucination.

Require source citation on every material answer. Then stress-test the system with harder scenarios. Feed it a question where two internal documents give slightly different guidance — an older procedure and a recently updated policy memo — and see which one the tool surfaces.

Ask how the retrieval layer handles both structured content (tables, forms, numbered checklists) and unstructured content (narrative policy language, training slide decks, email-based change notices). A tool that only indexes PDFs or only parses clean HTML will miss large portions of your knowledge base. Understanding how questions to ask AI vendors about connector quality and data coverage can help you pressure-test these capabilities during evaluation.

Push further by asking about a regulation that changed in the last 90 days. If the tool returns outdated guidance, its ingestion pipeline cannot keep pace with your regulatory change monitoring cadence.

Evaluate whether the system can synthesize dense regulatory material into plain-language summaries without severing the link to the original source. Glean Assistant generates cited, permission-aware responses grounded in your company's knowledge — each answer links to the source document so the reader can verify the underlying policy text, not just trust the summary.

3. Verify permissioning, governance, and third-party risk controls

Access controls in banking are not optional features — they are foundational to every audit, examination, and board report. Any AI tool that surfaces internal knowledge must respect the permission boundaries your organization already enforces. A branch employee should never see answers drawn from executive committee minutes, and a compliance analyst reviewing BSA procedures should not pull results from HR disciplinary files.

Permission checks must happen before the tool generates an answer, not after. If the system retrieves restricted content first and then filters the response, sensitive data has already traversed the pipeline. A Wolters Kluwer survey of 148 financial institutions found that only 35.8% have established internal policies for ethical AI use, underscoring the governance gap many banks still need to close.

Ask the vendor to explain, in architectural terms, how permissions are enforced at the retrieval layer. Then review the tool's governance capabilities: audit logs showing who asked what and which sources were cited, usage reporting by role and department, content source controls that let administrators add or remove repositories, and role-based administration so compliance leadership can manage the system without IT dependency for every change.

Confirm how the vendor handles your data. Are prompts and responses stored? Are they used to train or fine-tune shared models? These questions fit directly into the third-party risk management reviews regulators already expect — the OCC's guidance on third-party relationships and the Fed's SR 13-19 both apply.

Evaluate resilience controls: authentication, encryption in transit and at rest, incident response procedures, integration with your identity provider, and data loss prevention measures. Glean's architecture enforces existing permissions at the Enterprise Graph level — every query checks the user's access rights before retrieving a single document, and audit logs capture the full interaction trail for compliance review.

4. Test how well the tool supports front-line workflows

A tool that requires employees to leave their primary work environment will struggle to gain adoption. Branch staff live in core banking platforms and service consoles. Call center agents work inside telephony and CRM systems. Operations teams toggle between case management tools and shared drives. If the AI tool adds another tab or login, usage will drop within weeks.

Evaluate where the experience shows up. The strongest deployments surface answers inside the applications employees already use — collaboration apps, browsers, intranet portals, and service desks — rather than pulling people into a separate interface. Glean Search delivers cited, permission-aware answers through browser extensions, embedded widgets, and integrations with collaboration tools so employees stay in their primary workflow.

Then test for actionability. A good answer does not stop at restating policy language; it moves the employee to the next step: a link to the correct procedure, an approved customer-facing script, an escalation path with the right contact, or the form required to document an exception. Increasingly, banks are exploring how AI agents for finance workflows can automate these multi-step actions end to end.

Run realistic scenarios during your evaluation. Ask a branch employee's question about account opening requirements for a minor and see whether the answer includes the specific documentation checklist and parental consent form. Have a call center agent test a fee disclosure question and confirm the tool returns the current approved language, not a paraphrase.

Measure whether the tool reduces escalations — if agents can resolve questions on their own, fewer calls land on supervisors' desks, and response times improve. In a company's first six months with Glean, search quality typically improves by 20% due to continuous self-learning, which means the tool becomes more useful as more employees interact with it across front-line workflows.

5. Evaluate answer quality, explainability, and human review

Build a test set of 30 to 50 real questions your internal teams ask regularly — not polished demo prompts, but the messy, context-heavy queries that reflect daily work. Score each answer against four criteria: factual accuracy, completeness relative to your internal policy, citation quality (does the source link go to the right document and section?), and alignment with your institution's interpretation of the regulation, not just the regulation's text.

Include hard cases deliberately. Test questions where policy wording is ambiguous, where two procedures overlap, or where a document has been superseded but not yet removed from the repository. A strong tool will flag uncertainty — returning a qualified answer, asking a clarifying question, or routing the user to the subject matter expert — rather than generating a confident response from stale or conflicting material.

Watch for specific generative weaknesses: summaries that omit critical qualifiers, answers that blend guidance from two different regulatory regimes, or responses that round up a "generally applicable" rule into a universal one. Industry experts anticipate that 2026 will bring a shift toward multi-agent AI systems for compliance, where specialized models handle distinct tasks — interpreting regulations, analyzing transactions, assessing risk — and synthesize findings for more robust conclusions than any single model could produce.

Require system-level explainability during your evaluation. The vendor should be able to describe how answers are retrieved, how the generative layer is governed, and what controls prevent the model from fabricating content.

Build human-in-the-loop review into your pilot: assign compliance analysts to verify a sample of answers weekly and flag gaps. Glean Assistant supports this review pattern by linking every response to its source documents and showing confidence indicators, so reviewers can quickly assess whether an answer is well-grounded or needs manual correction before it becomes part of operating practice.

6. Measure implementation effort, operational impact, and risk-adjusted ROI

Demo quality is not deployment quality. A polished product walkthrough with curated content tells you little about how the tool performs against your messy, overlapping, version-controlled document repositories. Measure the real implementation variables: time to connect to your content sources, time to onboard a pilot group, and time to produce answers under your governance requirements — not in a sandbox, but in your production environment with your permission model active.

Track time-to-answer before and after the pilot. If compliance officers currently spend 15 minutes locating the correct procedure for a customer complaint escalation, and the tool reduces that to two minutes with a cited, verified response, the time savings compound across hundreds of daily interactions. According to Deloitte, compliance operating costs have increased by over 60% for retail and corporate banks compared to pre-crisis levels — making any tool that meaningfully reduces time-to-answer a direct lever against this cost escalation.

Measure consistency improvements: fewer duplicate questions routed to the same subject matter experts, fewer escalations for questions the tool can answer directly, and higher first-response confidence among front-line staff. According to a 2023 McKinsey analysis, knowledge management solutions that reduce information search time by even 25% can recover the equivalent of one full working day per employee per week.

Layer in risk-sensitive metrics that matter to regulators: citation coverage (what percentage of answers include a verifiable source?), the share of responses grounded in approved documents, answer acceptance rate among pilot users, and escalation frequency before and after deployment. Beware of the hidden cost of AI fragmentation — deploying multiple disconnected tools can erode the ROI you are trying to measure.

Compare these results against the hidden cost of the status quo — repeated training sessions, fragmented handoffs between departments, and the operational drag of manual policy lookup. End the pilot with a structured scale decision: expand to new user groups, broaden the content scope, or pause and address gaps. Glean Agents can extend the pilot's value by automating multi-step compliance workflows — such as gathering documentation for an examination response — with enterprise context and governance, so the platform grows with your needs rather than requiring a new vendor at each stage.

Frequently asked questions

What criteria should banks use to evaluate AI tools for regulatory compliance?

Focus on six areas: fit with a defined compliance use case, grounded and cited answers from approved internal sources, permission-aware access controls with audit trails, integration into existing front-line workflows, measurable answer quality with human review, and risk-adjusted ROI that accounts for both time savings and regulatory risk reduction.

How can AI tools improve regulatory knowledge access for banks?

AI tools that connect to a bank's policy repositories, procedure documents, and training materials can surface cited answers in seconds — replacing manual searches that often span multiple systems. Glean Search, for example, uses its Enterprise Graph to retrieve permission-aware results across 100+ connected sources, so employees get accurate, sourced answers without leaving their primary work environment.

How do banks confirm that AI tools meet regulatory requirements?

Start by confirming the tool respects your existing access controls at the retrieval layer, not as a post-processing filter. Require audit logs, role-based administration, and clear data handling commitments from the vendor. Fit the evaluation into your institution's existing third-party risk management framework — the same due diligence process you apply to any regulated vendor relationship.

What metrics should banks track when assessing AI tool performance?

Track both operational and risk-sensitive metrics: time-to-answer reduction, escalation frequency, first-response accuracy, citation coverage (percentage of answers with a verifiable source), adoption rate by role, and repeat usage patterns. Compare pilot results against the cost of the current manual process to build a complete picture of value. The Federal Reserve reports that AI adoption in the financial sector reached approximately 30% of firms by late 2025, so benchmarking your institution's adoption against industry peers can add useful context to performance tracking.

How long does it typically take to deploy an AI tool for compliance knowledge access?

Deployment timelines depend on the number of content sources, the complexity of your permission model, and the size of the pilot group. Tools that connect to existing repositories through pre-built integrations — rather than requiring custom data pipelines — can reach a working pilot in weeks rather than months, especially when scoped to a single use case like regulatory Q&A for front-line staff.

The right AI tool for regulatory compliance does not ask you to lower your standards — it meets them. When the system grounds answers in approved sources, respects your permissions, and fits the workflows your teams already use, the result is faster knowledge access with stronger control.

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