AI Fraud Pattern Detector with Human Investigation
Detect unusual patterns across transactions and behavior, explain the signals, and route cases to qualified investigators.
Links defined transaction or account signals into a reviewable alert, distinguishes observed events from inferred risk, and routes the case to an investigator.
- Define the event and review baseline
- Collect authorized evidence
- Score and explain unusual patterns
Start with one documented fraud pattern supported by labeled history, known data coverage, an alert threshold, and an authorized investigator.
What this workflow does
Detect unusual patterns across transactions and behavior, explain the signals, and route cases to qualified investigators.
Start with one transaction or event type that has a documented baseline, known signals, an investigator, and a defined case outcome. Treat alerts as leads rather than proof; account freezes, formal reports, adverse actions, and final fraud findings remain human decisions.
How the workflow runs
Define the event and review baseline
Choose one transaction or behavior type with documented normal patterns, known signals, and a named investigation owner.
Collect authorized evidence
Use only approved transaction, identity, device, account, and case data with the required access and retention controls.
Score and explain unusual patterns
Surface the signals that contributed to an alert, relevant comparisons, missing data, and uncertainty instead of presenting a verdict.
Route alerts to qualified investigators
Treat each alert as a lead; people decide account restrictions, reports, adverse action, and final fraud findings.
Record outcomes and tune the workflow
Link the investigation result, false-positive or missed-pattern feedback, threshold changes, and reviewer approval.
Controls to define before launch
| Control area | What the agent handles | What the team controls |
|---|---|---|
| Metrics | Tracks confirmed-alert rate, false positives, missed cases found in backtesting, investigator time, and case outcomes. | Defines labels, review samples, alert thresholds, and acceptable coverage for each monitored pattern. |
| Review | Prepares a signal-linked alert and separates observed events from inferred risk. | Keeps account restrictions, adverse action, formal reporting, and fraud conclusions with authorized investigators. |
| Exceptions | Stops on identity conflicts, sparse or delayed data, model drift, rule disagreement, and high-impact action requests. | Assigns investigators, model owners, compliance, or security to resolve the exception before action. |
| Evidence | Records event identifiers, signal values, data timestamps, model or rule version, alert reason, and coverage gaps. | Retains investigator corrections, disposition, supporting records, action approval, and case outcome. |
| Recovery | Stops scoring or labels coverage incomplete when a data feed, rule service, or model is unavailable; it takes no adverse action. | Activates manual monitoring, restores the component, reviews the outage window, and revalidates alerts before use. |
What to do before and after the pilot
Before launch
Before launch, start with one transaction or event type that has a documented baseline, known signals, an investigator, and a defined case outcome.
After launch
After launch, review confirmed-alert rate, false positives, missed cases from backtesting, investigator time, and outcomes by pattern and population.
Connect this workflow with OpenMax
Use OpenMax to prepare signal evidence and triage queues while investigators retain fraud conclusions, reporting, restrictions, and adverse actions.
Frequently asked questions
Where should the first AI fraud pattern detector pilot begin?
Start with one transaction or event type that has a documented baseline, known signals, an investigator, and a defined case outcome.
Which decisions must remain with people?
Treat alerts as leads rather than proof; account freezes, formal reports, adverse actions, and final fraud findings remain human decisions.
How should the pilot be evaluated?
Review confirmed-alert rate, false positives, missed cases from backtesting, investigator time, and outcomes by pattern and population.
