AI
AI Agents for Banks and Insurers: 7 Use Cases That Pay Off, and How to Deploy Them Safely
Banks, insurers and takaful operators run on documents: product terms, underwriting manuals, claims procedures, circulars from the regulator, credit policies, and thousands of pages of contracts. Staff spend a large part of the day finding the right paragraph and checking it against a case.
AI agents can do much of that work: they answer from your documents with the source quoted, check files against your rules, prepare cases and draft replies. In a regulated business they must also do it under control: inside the region your regulator expects, with each employee seeing only what they may see, and with a person approving anything that changes a record.
Below are seven use cases that pay off early, the controls that make them acceptable to risk and compliance, and a 90-day plan from pilot to production.
7 use cases that pay off early
1. Product and policy answers for branch and call-centre staff
Staff ask in plain words (“can a customer top up this plan after a missed contribution?”) and get the answer from the current product document, with the clause quoted. New joiners become useful faster, and answers are consistent across branches.
2. Underwriting file checks
The agent reads the submitted documents, compares them with the underwriting checklist, and lists what is missing or outside policy. The underwriter decides; the agent does the reading.
3. Claims intake and preparation
A claim arrives with forms, scans and photos. The agent reads them, including scanned pages, fills the claim summary, flags missing items and drafts the request for more information. A claims handler approves before anything is sent.
4. Compliance and regulatory questions
“Which circular changed the limit for this product?” The agent answers from your library of regulations and internal policies and shows where each statement came from, so compliance staff can check it in seconds.
5. Customer replies, drafted for approval
On e-mail, your website or WhatsApp, the agent drafts the reply from approved knowledge. For public questions it can answer alone; for anything about an account, a person approves.
6. Credit and investment memos
The agent pulls the facts from statements, reports and earlier memos into your memo template. Analysts spend their time on judgement, not copying numbers.
7. Internal IT and HR help
Leave rules, travel policy, how to reset access: high volume, low risk, and the fastest way to show value across the whole company.
The controls risk and compliance will ask for
Plan these from day one. They decide whether the pilot ever reaches production.
| Control | What it means in practice |
|---|---|
| Data location | Documents, index, conversations and logs stay where your regulator expects, including your own cloud account or premises |
| Permission-aware answers | Each person gets answers only from documents they can already open; branch staff do not see board papers |
| Approvals | Reading runs alone; sending, updating and anything with money waits for a named person |
| Masking | Card numbers, IBANs, phone numbers and your own identifiers are hidden before the model reads them |
| Audit trail | Every question, answer, action and approval recorded, with who and when |
| Single sign-on | Your directory controls access; leavers lose it with their account |
| Model choice | You decide which model handles which work, including models you host |
| Grounded answers | Answers quote the source, and the agent says so when the documents do not contain the answer |
If a vendor cannot show all eight in a demo, the security review will find the gap later, at a higher cost.
A 90-day plan from pilot to production
Days 1 to 30: one team, one question set. Pick a team with a clear pain, such as product questions in the call centre. Load their documents, write 30 real questions with the correct answers, and measure how many the agent gets right with the right source. Use non-sensitive documents first.
Days 31 to 60: controls on. Connect single sign-on, set approvals on every tool that writes or sends, switch on masking, and review the audit trail with risk and compliance. Move the workspace to the region or cloud account your policy requires.
Days 61 to 90: production for the first team, plan for the next. Open the agent to the whole team in the channels they already use (the web app, Slack or WhatsApp), keep reviewing a sample of answers each week, and choose the next use case from the list above.
What it costs
Most of the cost of a pilot is people’s time, not software. In production, usage-based pricing means you pay for what agents do rather than for seats, which suits the uneven use you see between, say, a claims team and a branch network. Running in your own cloud adds the cost of the servers and database there.
Where promptev fits
promptev is a platform for sovereign AI agents for business. Agents answer from your company’s data, see only what each person may see, and ask before they act.
- Runs where your regulator expects. promptev’s cloud, your own cloud account in any region, or your own premises, with the same product in all three.
- Reads what banks and insurers have. PDFs, Word, spreadsheets and scanned pages, plus Google Drive, SharePoint, OneDrive, Confluence and other connected systems kept in sync.
- The controls, built in. Permission-aware answers, approvals, masking, audit, single sign-on with SCIM, model choice and answers that name their source.
- Where your staff and customers already are. The web app, Slack, WhatsApp and your own website.
You can start free with 1,000 credits and test it on one team’s documents, or talk to us about a pilot.
Related reading
- Sovereign AI agents: a buyer’s guide
- Permission-aware AI: how to stop your AI assistant oversharing
- PII masking for AI
- Human-in-the-loop AI
Frequently asked questions
What can AI agents do in a bank or insurance company?
The safest first wins are answering staff questions from policies, product terms and procedures; checking documents against a checklist; preparing claim and underwriting files; drafting customer replies for a person to approve; and answering compliance questions with the source quoted. Agents that change records or move money should always wait for a person's approval.
Are AI agents allowed in regulated financial services?
Generally yes, under the same rules as other outsourced technology: you must know where data is stored and processed, control who can see it, keep an audit trail and stay accountable for decisions. Check your regulator's rules on cloud and outsourcing, and choose a platform that can run where those rules require.
Should a bank run AI agents in its own cloud?
For customer data and confidential documents, often yes. Many banks and insurers pilot on non-sensitive knowledge in a managed service, then run the same product in their own cloud account, in the region their regulator expects, before customer data is used.
How do AI agents avoid giving wrong answers on policy and product terms?
By answering only from your approved documents, quoting the source of each answer, saying when the documents do not contain the answer, and having people review answers in the pilot. An agent that guesses when it does not know is not ready for a regulated business.
How long does it take to deploy an AI agent in a bank?
A focused pilot on one team's documents can run in two to four weeks. Moving to production with single sign-on, approvals, audit and your own cloud usually takes two to three months, mostly for security review rather than building.
Faisal Saeed is Founder & CEO of promptev, the platform for sovereign AI agents for business: agents that answer from company data, see only what each person may see, and ask before they act.