Fraud Detection inference

Design the workflow before choosing a model

Keep evidence retrieval, signal extraction, and human decisions distinguishable. Preserve the source of each extracted signal and define who may authorize an account action.

Define a representative input and an explicit acceptance criterion. Keep model and prompt versions with the result so quality changes can be investigated.

Evaluate outcomes and failure modes

Measure false positives, missed cases, evidence traceability, and reviewer workload.

Evaluate class imbalance and changes in incoming patterns. A cheaper model that increases unnecessary reviews or misses costly cases can raise the total operating cost.

Compare candidate routes on the same held-out examples. Report how many examples were evaluated and inspect failures rather than relying on a single average score.

Roll out with a measurable cost baseline

Start with assistance to reviewers. Require the responsible team to approve thresholds and escalation rules before integrating output into consequential decisions.

Calculate cost per accepted outcome using input and output tokens, retrieval or tool fees, retries, and review effort. A lower token price is useful only if the total workflow still meets its requirements.

o10 can provide model routing for the inference steps. Your application remains responsible for workflow permissions, tool behavior, and deciding whether the final result is acceptable.