Managing fraud is a key challenge for banks, payment processors, and insurance companies, among other institutions. The challenge is to minimize losses to due fraud while maintaining customer loyalty and satisfaction. A key element of fraud management is investigating reported false positives and false negatives to understand what is happening and improve the fraud detection patterns. Here we show a novel AI-based approach to fraud investigation developed by Athena Decision Systems that leverages contextual information, existing pattern models, and the ability to author new fraud patterns based on understanding the reported situations. This is Accountable AI!
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