This workflow helps evaluate and review fraud alerts without treating a model score as a finding of wrongdoing. Define the intended action with the AI fraud evaluation guide before preparing a trial.
Create a protected review record
Use a case reference, alert time, model or rule version, relevant evidence source and responsible reviewer. Keep sensitive details in the approved system rather than copying them into a broad spreadsheet or team chat. Record the permitted next action and any unresolved question.
Distinguish the alert’s prediction from the case outcome. Use outcome labels such as confirmed fraud, confirmed legitimate or unresolved, based on the authorised review process. Do not quietly label every blocked case as fraud.
Prepare a representative evaluation
Use appropriately approved historical or test cases with reliable outcome information. Separate evaluation cases from training data where relevant. Identify the periods, transaction types and limitations of the sample. Include difficult legitimate cases as well as known fraud examples.
NIST’s AI accuracy guidance supports considering false positives, false negatives and conditions beyond training. Your pilot should describe the actual sample and operating context.
Compare candidate thresholds
- Count confirmed fraud cases that produced alerts.
- Count confirmed fraud cases missed by the threshold.
- Count legitimate cases unnecessarily flagged.
- Record unresolved cases separately.
- Measure the review queue and time required.
Compare like-for-like samples. A lower threshold may produce more alerts without giving reviewers enough time to investigate them. Include the capacity and consequences of the proposed review process.
Test escalation and interruption
Verify that an alert reaches the intended authorised person with enough information to investigate. Define what happens when evidence is missing, a provider is unavailable or the reviewer cannot resolve the case. Use a documented fallback rather than silently turning an uncertain score into an irreversible decision.
Review live results cautiously
If a trial proceeds to approved live use, monitor alerts, review outcomes, delays and missed cases. Some outcomes arrive later, so keep the relevant evaluation window open. Review differences after model or rule updates instead of assuming past performance still applies.
Keep a decision record explaining the accepted scope, remaining limitations and owner of the next review. Protect customers and the business through a transparent review process, not a claim that the system prevents all fraud.
