Fraud risk exposure from claims is a major concern for the insurance industry, and it’s growing. But timely detection of attempted fraud is challenging.
Effective fraud detection must sift through huge volumes of data coming from many different sources, both inside and outside the company. In many cases, even the internal information is stored in multiple silos, preventing existing information assets from being leveraged to identify hidden connections. Additionally, companies must be able to respond rapidly to claims submissions to prevent payouts to fraudulent claimants.
Can Big Data help?
On the one hand, we have this complex problem. And on the other, we have Big Data’s promise of complexity management. So how can we bring the two together to resolve the problem?
That’s the question that led us to develop a prototype, based on Big Data technologies, for real-time scoring that can be used as a supplementary tool for fraud detection. The idea behind the prototype is simple: to provide a flexible tool to discover connections between people involved in a claim and providing prompt feedback on those connections to immediately trigger further investigation of potential fraud.
