# Building a system that answers customers' questions and completes simple tasks in a chat or voice channel inside a bank

Source: https://www.bankingnewsai.com/agentic-banking/build/customer-chatbots
Last updated: Sep 17, 2026

The customer-facing assistant is the pattern supervisors have looked at hardest, and their findings are specific: wrong answers about rights and fees, customers unable to reach a person, and systems that hide what they are. The design is routing: classify the intent, answer knowledge questions from the governed document set with citations, answer account questions through tools to the live system, hand transactions above a threshold to a person, and make the route to a human one step away at all times.

## The brief (default answers)

| Choice | Answer |
| --- | --- |
| Who is affected | Customers, directly |
| Reversibility | With cost |
| Stakes | Material |
| Knowledge | Both |
| Verifiability | By judgment |
| Steps | Partly |
| Jurisdiction | United States |
| Delivery route | Through a cloud platform |

- **Pattern:** Workflow — Several model calls orchestrated by your code, in a sequence or a graph you designed.
- **Tier:** 2, Tier 2: act with approval — Material but recoverable. The model may prepare and, within limits, act, with a person approving anything that leaves the bank or touches a customer.
- **Workflow shapes:** Routing, Evaluator and optimizer
- **Human involvement:** The model prepares and may act within limits; a person approves anything that reaches a customer or cannot be undone.
- **Knowledge:** Retrieval over the governed document set, with a citation on every answer and a refusal when nothing relevant is found.; Tool calls to the system of record for anything live. Never retrieval for a balance, a case status or a limit.; Documents to reason with, systems to check against: the workflow decides which question goes where before the model answers.

## Decomposition

| Step | Owner | Note |
| --- | --- | --- |
| Identify the customer and the intent | system | Authentication in the channel; intent classification with a confidence. |
| Answer a knowledge question | model | Retrieval over product terms and policies, with the source shown. |
| Answer an account question | system | A tool call to the system of record; the model formats, never invents. |
| Execute a low-value task | system | Within an envelope, by rule, confirmed with the customer. |
| Escalate | human | Complaints, disputes, hardship and anything the classifier is unsure about. |

## Control layer weights (0–4)

| Layer | Weight |
| --- | --- |
| governance | 1 |
| identity | 1 |
| actions | 0.9 |
| data | 1 |
| models | 1 |
| runtime | 0.8 |
| observability | 0.7 |
| oversight | 1 |

## Documents that apply

- [CFPB Chatbots in Consumer Finance (issue spotlight, 2023)](https://www.bankingnewsai.com/ai-regulation/documents/cfpb-chatbots-in-consumer-finance-2023) — Inaccurate answers and blocked access to a person can violate consumer law.
- [Regulation (EU) 2024/1689](https://www.bankingnewsai.com/ai-regulation/documents/eu-ai-act-regulation-2024-1689) — Article 50: people must be told they are talking to an AI system.
- [NIST AI 600-1 (Generative AI Profile)](https://www.bankingnewsai.com/ai-regulation/documents/nist-ai-600-1) — Confabulation named as a generative-AI risk, with suggested actions.
- [DFS Virtual Currency Customer Service Guidance (May 2024)](https://www.bankingnewsai.com/ai-regulation/documents/ny-dfs-industry-letter-2024-05-30-vce-customer-service) — New York's expectations for customer service, including a route to a human.
- [SR 26-2](https://www.bankingnewsai.com/ai-regulation/documents/fed-sr-26-2) — US model risk management as revised in April 2026.
- [SR 23-4](https://www.bankingnewsai.com/ai-regulation/documents/fed-sr-23-4) — US third-party risk management, including the model provider.
- [SB 26-189](https://www.bankingnewsai.com/ai-regulation/documents/co-sb26-189) — Colorado: notice, explanation and human review for consequential automated decisions from Jan 1, 2027.
- [BCBS Third-Party Risk Principles (Dec 2025)](https://www.bankingnewsai.com/ai-regulation/documents/bcbs-third-party-risk-principles-2025) — Nth-party supply chains and concentration on cloud providers.
- [BCBS ICT Risk Management Report (June 2026)](https://www.bankingnewsai.com/ai-regulation/documents/bcbs-ict-risk-management-range-of-practices-2026) — How supervisors look at ICT and cloud dependencies.
- [OCC Bulletin 2026-13](https://www.bankingnewsai.com/ai-regulation/documents/occ-bulletin-2026-13) — For a national bank, the OCC's copy of the 2026 model-risk guidance.
- [NIST AI RMF 1.0](https://www.bankingnewsai.com/ai-regulation/documents/nist-ai-100-1) — The voluntary Govern, Map, Measure, Manage frame for everything model-risk guidance leaves out.

## Evals

- A golden dataset of at least 150 real cases with expected outputs, including adversarial inputs: wrong documents, unusual formats, prompts that try to change the task.
- A judge model scoring against a written rubric (accuracy, completeness, tone, citation present), calibrated against a human-scored sample every month. Threshold set from the human sample, not guessed.
- Citation checks: every factual claim resolves to a passage in the governed set; unsupported claims below 2% of answers.
- Escalation evals: the cases that must reach a person do, on a held-out set, with precision and recall both reported.
- Trace evals per step, not only end to end: which step fails, how often, at what cost, so a prompt or model change can be judged step by step.
- The same suite reruns on every prompt change, model version change and retrieval change; a regression blocks the release. That is what ongoing monitoring and outcomes analysis mean in model-risk terms.

## Human gates

- Per-action approval by a competent reviewer for anything customer-facing or irreversible; sampled review for the rest.
- Validation proportionate to materiality, with monitoring for drift on inputs and outputs.
- An escalation route to a person that the customer can reach in one step.
- Monthly review of the evals and the exception log by the accountable owner.
- The customer is told they are dealing with an AI system and can reach a person in one step.

## What an examiner will ask

- Where is this system in your inventory, what tier did you assign, and who signed it off?
- What counts as a model here, and what does your validation cover for the parts that are not?
- Show me the data lineage behind the retrieval set and the training or tuning data.
- What can the system do without a person, and where is that written down?
- How do you know it is still working: which evals run, how often, and what happened the last time one failed?
- What did you do about the vendor: due diligence, contract, exit plan, concentration?
- Walk me through one wrong output from production and what the customer, if any, saw.
- Who can switch it off, and has that been tested?
- Show me how a customer reaches a person, and how long it took the last ten who tried.

## What the board should hear

- What it does: answers customers' questions and completes simple tasks in a chat or voice channel; the model prepares and may act within limits; a person approves anything that reaches a customer or cannot be undone.
- Pattern: workflow (routing, evaluator and optimizer); tier 2: act with approval.
- Rules it answers to: 4 documents across US, each linked in the brief.
- How we know it works: a golden dataset, automated checks on every release, and human review at the level the tier demands.
- What could go wrong and who answers: the accountable owner, the kill switch, the escalation route.

## Pitfalls

- Retrieval used for live state, producing confident wrong balances.
- No disclosure that the customer is talking to an AI system.
- Escalation buried behind repeated attempts to keep the customer in the bot.

Change any answer on the interactive map: https://www.bankingnewsai.com/agentic-banking/build/customer-chatbots

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