Industry Focus : Insurance

The Issue : Transaction Risk Management​

The Solution : Automated Solution to detect nefarious activities using Data Analytics.​

The  Summary : Insurance is a domain where auditing, compliance as well as book keeping needs to be at the highest of standards, however, with insurance scammers getting smarter every day, it did not make sense for our customer to keep expanding their team to reactively detect and prevent insurance fraud. They approached us to deliver a solution that would prevent the exponentially increasing cost of hiring resources to scale up with their customer base.

The Result : To our experts at SIFT, this was a data centric issue. Our customer was producing large amounts of data in transactions, forms, applications and audit reports, however, these data structures were highly unorganized. We started off by creating a unified data warehouse where all historical data was collated. After this, we developed a sophisticated algorithm to detect key words and behaviors in a customers interaction to detect any kind of potential insurance fraud. Rather than having resources manually detect these behaviors over a distributed data set, a smaller team of more experienced professionals are now presented with a case file. We’ve also created the case file based on priority, value and frequency, some exceptional situations gives our algorithm the ability to blacklist customers in case of a high profile violation. Due to this, the processors running the algorithm does the heavy lifting, providing the Risk Management team with accurate data and insights into the case, prevent the need to spend time, money and precious productivity into initial investigation. SIFT was able to help the customer reduce the cost of capability by significant factors of magnitude with this automated solution.