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AI agent for fraud analysts

Fraud Rule Tuning Agent

Better rules with tested changes and fewer false alerts

DecisionsRisk managementFinance
See how it worksGet access
Fraud Rule Tuning Agent: what goes in, what the agent does and what you get

What it does

Fraud rules drift over time: some flood analysts with false alarms while others stop catching anything. Each month this agent reviews rule performance. It pulls alerts and outcomes and measures, for each rule, alert volume, true fraud found, false positive rate and missed fraud. For weak rules it designs changes such as new thresholds or added conditions and tests them on six months of past data. It shows the effect on fraud caught and alert volume. It checks that each change keeps fraud caught above the set floor. If a change misses too much fraud, it rejects it and tries another. It writes a change proposal with test results. Fraud leaders approve changes and IT puts them live. Edge case: a rule looks weak only because a fraud wave ended, so the agent compares several months before proposing a change.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueApprovedNo 1 STARTS WHEN Monthly review 2 USES A TOOL Pull rule alerts and outcomes 3 DOES Measure performance by rule 4 DOES Design changes for weak rules 5 USES A TOOL Test changes on past data 6 CHECKS THE RESULT Does each change keep fraud caught above the floor? If not: try another change and retest. Back to step 4. 7 YOU APPROVE Fraud leader approves changes 8 RESULT Rule change proposal
Read the steps as a list
  1. Monthly review
  2. Pull rule alerts and outcomes
  3. Measure performance by rule
  4. Design changes for weak rules
  5. Test changes on past data
  6. Does each change keep fraud caught above the floor?If not: try another change and retest. Back to step 4.
  7. Fraud leader approves changesThe agent waits here for your OK.
  8. Rule change proposal

How it decides

It ranks rules by false positive rate and tests changes on historical data.

  • False positive rate over 98%: review rule
  • Change cuts fraud caught by over 2%: reject
  • Use 6 months of data for tests

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Fraud caught floor
  • Test period
  • Rules in scope
  • Report format

What keeps you in control

It always asks you first

  • Rule changes in production

Hard limits

  • Never changes live rules
  • Never uses customer data outside the system

It stops when

  • Done: proposal ready
  • Stop: outcomes data incomplete

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensIn the May review, a card rule produced 1,400 alerts a month and caught 9 frauds. The agent tested raising the amount threshold from $300 to $500, which cut alerts by 55% but missed 3 frauds, so the check failed. It then tested a new-merchant condition at $400, which cut alerts 48% and kept all 9. The fraud leader approved it.

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