SIM-swap and account fraud detection

Help fraud teams review unusual account activity with the supporting signals together. This example explores when an event deserves closer investigation or verification.

Telecomindustry context

Risk and fraud operationswho uses it

Loss avoidancewhat to measure

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What sim-swap and account fraud detection could help with

The reviewer needs a clear reason for the flag and a way to correct it. Test legitimate customer changes, missing signals and the effect of false positives before choosing a response.

Who reviews it: Risk and fraud operations
What to measure: Loss avoidance, false positives, customer trust
Example test: Risk scoring with step-up verification workflow and reviewer console.

Risk and fraud operationsLoss avoidanceAudit trailHuman approval

The information a useful test needs

Choose a small, representative set of inputs for sim-swap and account fraud detection. Check whether the information below is available, accurate and appropriate to use before connecting a model to a working system.

Account eventsdevice signalsgeolocationtransaction history

How to evaluate sim-swap and account fraud detection

Follow the information through each step. For sim-swap and account fraud detection, the reviewer needs to see the source, understand the suggestion and know when to keep using the current process.

Capture

Start with sample imagery or events that a knowledgeable reviewer can check.

Infer

Compare model suggestions with examples your team has already reviewed.

Flag

Keep the source information beside each suggestion so a reviewer can check it.

Review

Agree which cases need a person to review and what that person should see.

Learn

Record corrections and test again before changing how the work is handled.

A focused first test

Risk scoring with step-up verification workflow and reviewer console.

Before anyone relies on it

Decide who can view the source information and who reviews sim-swap and account fraud detection results. Test how the team corrects a wrong result and returns to its current process when information is missing.

What to compare

Compare loss avoidance, false positives, customer trust with the current process. Review missed cases, incorrect suggestions and the time people spend checking the output before deciding whether the approach helps.

Questions about sim-swap and account fraud detection?

Start with one account event to discuss the evidence, customer impact and verification decisions a useful review would need.

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