Playbook: fraud detection with a private LLM

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Isolate the use case and the minimum data required

Do not start with 'all fraud'. Start with one alert type, one bounded signal set, and the minimum data the model needs to help. Every extra field that enters without reason expands risk without improving review.

Separate automated decisioning from analytical assistance

The playbook should make clear what remains solved by a rule or score and what the LLM adds: summarizing history, explaining inconsistencies, prioritizing reading, or suggesting next review steps. That boundary prevents overdelegating to a layer that should not approve or block on its own.

Use the LLM for context, not for the final verdict.
Keep hard signals outside free text whenever possible.
Log which evidence the analyst read and what the system suggested.

Design prompts around case structure, not imagination

The prompt should request a summary of findings, risk hypotheses, and information gaps in a fixed format. The more structured the output, the easier it is to compare reviews and detect when the model leaves the frame.

Create a two-speed review flow

First, fast classification to prioritize cases. Then, expanded review only for the cases that cross a threshold. This lowers cost, protects analyst focus, and avoids deep reading of files that were never going to escalate.

Audit false positives, false negatives, and drift

A fraud system degrades if nobody reviews which case types it starts summarizing badly or which new tactics it fails to recognize. The operation needs periodic sampling and closed feedback with the risk team.

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Playbook: fraud detection with a private LLM

Isolate the use case and the minimum data required

Separate automated decisioning from analytical assistance

Design prompts around case structure, not imagination

Create a two-speed review flow

Audit false positives, false negatives, and drift

Unlock the full article

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