Treating fraud prevention as a one-time implementation project rather than an ongoing operational function leads to detection capability decay as fraud patterns evolve without corresponding system updates. Siloed fraud prevention that doesn’t share signals with related compliance functions like AML and responsible gambling misses the cross-functional insights that would improve outcomes in all three areas. Inadequate feedback loops between fraud outcome data and detection model training prevent the continuous improvement that keeps detection accuracy high over time. The practical experience of identifying and correcting these failure modes in a real operational context is documented at https://seon.io/resources/case-study/soft2bet/ with insights that are directly applicable to operators at various stages of fraud prevention maturity.
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