What problem does it solve?
Supervisors send banks a steady flow of information requests, exam questions, thematic review questionnaires and follow up letters, each with a deadline. Answering them means working out what is really being asked, finding the evidence across policies, board papers, test results and earlier correspondence, getting contributions from several teams and making sure the answer is consistent with everything the bank has told the supervisor before.
Under time pressure, answers get assembled by email and spreadsheet, evidence is sent without a record of exactly which version went, and commitments made in a response ("we will complete the review by Q3") are tracked by memory. Accuracy is an obligation in its own right: the FCA's SUP 15.6, for example, requires information given to the regulator to be factually accurate or, for estimates and judgements, fairly and properly based after appropriate enquiries.
How does it work?
- Log and interpret the request. Each request is logged with its deadline; the assistant splits it into individual questions and states what each one asks for.
- Retrieve evidence. Retrieval over policies, procedures, committee papers, test results and prior regulatory correspondence returns candidate evidence and earlier answers on the topic.
- Assign contributors. Questions that need new input go to named owners with the deadline.
- Draft the response. The assistant drafts answers from the retrieved evidence, cites each document and flags statements that differ from earlier submissions.
- Review and sign. Subject matter experts, then legal and compliance, edit and approve the final response. Nothing leaves without sign off.
- Track commitments. Every commitment in the sent response becomes a tracked action with an owner and date, and the full package (request, evidence, response) is archived.
- Audience
- Employee facing
- Autonomy
- Copilot
- Adoption
- Emerging
- Channels
- Internal tools, Email
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
No public deployment has disclosed a measurable outcome yet.
Value drivers: Compliance quality, Speed and cycle time, Employee productivity, Risk and loss reduction.
Indicative value
A mid sized bank answering 400 supervisory questions a year
USD 32,400 to USD 216,000
Staff time released from supervisory responses per year
How this is calculated
Formula: questions * hoursPerQuestion * timeSaved * hourlyCost. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Supervisory questions and information request items per year questions, questions per year | 400 | 400 | Editorial assumption for the reference bank, not a sourced figure. Replace with your own request log. |
| Staff hours per question (search, drafting, review) hoursPerQuestion, hours per question | 6 | 12 | Editorial assumption across all contributors. Replace with your own records. |
| Share of search and drafting time saved timeSaved, fraction of time | 0.15 | 0.3 | Editorial assumption. No public measured benchmark was found; review and sign off time is not reduced. |
| Fully loaded cost of compliance and specialist staff hourlyCost, USD per hour | 90 | 150 | Editorial assumption, replace with your own. |
What it leaves out: Time only. It leaves out the value of consistent answers and fewer missed commitments, which this estimate does not price, and the cost of building the evidence repository.
Who already uses it?
3 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
Federal Emergency Management Agency
United States · Government and public sector · 2025
FEMA reports a pre deployment tool that lets staff ask questions of spend plan and actual execution data in common language, using Azure OpenAI inside the agency's system boundary, so they can give rapid responses to data calls and requests for information and show leadership where budget was planned and spent. It illustrates the evidence retrieval step of answering an oversight request from governed internal data. Not yet live; no results published.
No outcome disclosed.
U.S. Department of Homeland Security
United States · Government and public sector · 2025
The DHS management directorate reports a pre deployment use case that converts plain text statements in Congressional reports into machine readable tasks that can be managed in Outlook, Jira or other project tracking software, and turns scanned financial tables into structured data. It corresponds to the commitment tracking step of oversight work: statements an overseer writes down become tasks that can be managed in project tracking software. Not yet live; no results published.
No outcome disclosed.
U.S. Department of Homeland Security
United States · Government and public sector · 2024
Since December 2024 the DHS Executive Secretariat has used generative AI to summarise incoming letters when a new work package is created in its correspondence tracking system, so analysts can act on and assign requests faster; the summaries flow into the system that tracks correspondence and information requests. The department states a future aim of drafting responses to those requests. It is the intake and tracking half of answering an external request, run by a government body rather than a bank.
No outcome disclosed.
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- A repository of prior regulatory correspondence and submissions, with versions
- Current policies, procedures, committee papers and test results with owners
- A request log with deadlines and owners
- Rules on confidential supervisory information from the relevant supervisors
Systems to integrate
- Document management and regulatory correspondence systems
- Governance, risk and compliance platform for issues and actions
- Email and secure regulator portals (intake only, sending stays manual)
- Workflow and task tools for contributors and commitments
Complexity: Medium
The technology is standard retrieval and drafting. The difficulty is confidentiality: supervisory correspondence is often confidential supervisory information, so hosting, access control and model provider terms must be settled first.
- 1
Settle confidentiality first
Classify which correspondence is confidential supervisory information, check what the supervisor allows, and choose hosting and model providers accordingly before loading data.
- 2
Build the correspondence memory
Index prior requests, responses and evidence with dates and versions, so the assistant can show what the bank has already said on a topic.
- 3
Start with interpretation and retrieval
Use the assistant to split requests into questions and find evidence and prior answers. Measure how often experts accept its evidence before adding drafting.
- 4
Draft with citations and consistency checks
Every drafted sentence cites a document; statements that differ from prior submissions are highlighted for the reviewer.
- 5
Close the loop on commitments
Extract commitments from sent responses into the action tracker and report overdue items to senior management.
Guardrails
- Legal and compliance sign every response; the assistant cannot send anything
- Drafts cite the evidence they rely on; unsupported statements are flagged
- Confidential supervisory information stays in approved hosting with strict access control
- Full record of each request, evidence sent, response version and approver
- Consistency check against earlier submissions before sign off
KPIs to instrument
- Time from request receipt to approved response
- Share of responses sent on or before the deadline
- Reviewer edits per drafted answer and evidence acceptance rate
- Commitments tracked, closed on time and overdue
- Inconsistencies with prior submissions caught before sending
Human in the loop
Subject matter experts own the content, legal and compliance approve every response, and the head of regulatory affairs owns the relationship and the commitment log. The assistant prepares, drafts and tracks.
Common failure modes
- Confidential information in the wrong place
- Supervisory material is sent to a model provider or tool that the supervisor has not accepted. Settle hosting and terms first and block uploads elsewhere.
- Confident answers from stale evidence
- The assistant cites a superseded policy or old test result. Index versions and show dates in every citation.
- Commitments lost after sending
- The response is filed and the promise is forgotten. Extract commitments automatically and review them in governance.
- Over polished responses
- Fluent drafts hide that the bank does not actually know the answer. Reviewers must confirm facts, not just wording.
What are the risks and rules?
EU AI Act
Minimal risk
Drafting regulatory correspondence for human approval is not an Annex III use. The main risks are confidentiality and accuracy, which are handled by supervisory information rules, data protection law and internal controls.
Rules that apply
Guidance
- 12 CFR Part 261, Rules Regarding Availability of Information (Board of Governors of the Federal Reserve System, North America). Example of rules that restrict disclosure of confidential supervisory information, which govern where exam material may be processed.
- PRIN 2.1, The Principles (Financial Conduct Authority, Europe). Principle 11 requires firms to deal with regulators in an open and cooperative way and to disclose anything the regulator would reasonably expect notice of.
- SUP 15.6, Inaccurate, false or misleading information (Financial Conduct Authority, Europe). Information given to the FCA must be factually accurate or, for estimates and judgements, fairly and properly based after appropriate enquiries; a firm must notify the FCA if information it gave may have been false, misleading, incomplete or inaccurate.
- Artificial Intelligence Risk Management Framework, Generative Artificial Intelligence Profile (NIST, North America). Guidance on confabulation and information security risks that apply to drafting assistants handling sensitive material.
Controls to put in place
- Classification and access control for confidential supervisory information
- Approved hosting and model provider terms documented for supervisory material
- Sign off workflow with recorded approvers for every response
- Commitment register reviewed by senior management
- Archive of each request, evidence package and final response
Frequently asked questions
- Can we put supervisory correspondence into a generative AI tool?
- Only within the rules on confidential supervisory information that apply to you and with hosting, access control and model provider terms your supervisors would accept. Settle this before loading any correspondence.
- Who uses AI for this today?
- We found no named bank deployment in public sources. The public examples on this page come from US government bodies: the Executive Secretariat of the Department of Homeland Security has used generative AI since December 2024 to summarise incoming correspondence and information requests in its tracking system, with drafting of responses named as a future capability, and two related DHS and FEMA tools are reported as pre deployment.
- What delivers the most value first?
- In our view, retrieval of prior answers and evidence, and tracking of commitments. They target inconsistent answers and missed promises, the failure modes described above, while drafting speed matters mainly for tight deadlines. No public measured comparison exists yet.
How to cite this page
Blits.ai AI Use Case Library, "AI for supervisory exam and information request responses", last verified 27 September 2026, https://www.blits.ai/ai-use-cases/supervisory-exam-response-assembly. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 27 September 2026: First published