Use machine learning to prioritize fraud investigations and reduce avoidable losses, assisting your investigators, not replacing them.
Rank claims and transactions by suspicion.
Surface the cases with the most at stake first.
Flag suspicious customer and account behavior.
Detect rings and linked-entity patterns.
Catch transaction anomalies as they happen.
Cut noise so investigators focus on real risk.
Send ranked cases to the right SIU investigator.
Provide evidence summaries, not auto-denials.
No. It assists them. OKEMA ranks and prioritizes cases with evidence summaries; humans decide. It never auto-denies a claim or blocks a customer on its own.
Yes. The same anomaly-scoring and prioritization approach applies to claims fraud, transaction fraud, and suspicious account behavior.
The opposite. Cases are ranked by exposure and precision-tuned, so investigators spend time on the highest-value, highest-likelihood fraud.
Confirmed outcomes feed back as labels, so the models keep learning new patterns instead of relying on static rules.
Start with a roadmap that quantifies avoidable-loss reduction across your fraud surface.
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