An online insurance provider bills itself as one of the fastest to respond to user claims, but has recently run into a spat of fraudulent claims that are increasingly impacting its bottom line. The company has an established detection system that relies on using previous indicators to find similar abnormalities and red flags in new claims but has been unable to detect more sophisticated and subtle fraudulent medical claims.
Our customer needed a way to detect fraudulent claims earlier in the process but did not want to add more steps that could create bottlenecks and impact their user experience. The first step was to move away from a rigid rules-based detection system to a dynamic one. To reduce their time to fulfillment while building a smarter and more accurate detection system, the company focused on enriching its models with external data, including:
After logging in claim fulfillment periods that ranged as high as several hours, the company managed to reduce its average fulfillment time to roughly 1 hour per claim. More importantly, it managed to improve its detection rates by nearly 11%, boosting its overall detection rate to 92% of all fraudulent claims during the initial review. As a result, our customer was able to offer a much better service to their users, boosting their revenue and reducing churn. More importantly, they were able to do so while cutting down on fraud and saving significantly on each transaction.