In this age, when everything is moving at light speed, fraud detection is a even bigger challenge. Being able to detect it at the shortest time, is the bottom line of insurance business.
With complex, and often unstructured data, fraud possibilities co-evolve with technology, esp. Information technology Business re engineering, reorganization or downsizing may weaken or eliminate control, while new information systems may present additional opportunities to commit fraud.Traditional methods of data analysis have long been used to detect fraud. They require complex and time-consuming investigations that deal with different domains of knowledge like financial, economics, business practices and law. Fraud often consists of many instances or incidents involving repeated transgressions using the same method. Fraud instances can be similar in content and appearance but usually are not identical.
This is a perfect example of how our team helped insurance company building a re-enforced learning model to detect possible insurance fraud, being able to predict the probability of a false claim, before the actual investigation. So the fraud investigation team can focus on high-risk cases, without spending fruitless hours on low-risk claims.