Improving SIU Through Data Analytics

Special Investigative Units

As anyone who works with insurance field knows, the business is always changing. The insurance industry must be able to move quickly or risk being exploited by fraudulent claims. Special Investigative Units (SIU) are hard at work making sure such claims don’t occur. They use many different tools, but one major way to keep up with changes is through data analytics. Data is a valuable resource for insurance companies. So much that many insurance carriers are employing complex technologies and analytics at rates never seen before. Having the ability to use “big data” resources to be proactive against fraud has become a priority.

The technology has to be effective in harvesting data from internal and external sources. Then aggregating all of the information into an easy to consume format. This allows the SIU members to create strategies and claim models that can improve fraud detection, gain new insights, and predict emerging trends.

According to Claim Magazine, critical data needed to create successful claim models include policy and application details, structured and unstructured claim data, investigative results, vendor data, and industry watch list data. Also, third party data like ISO loss history, public records, medical billing data, and underwriting information. Having all this raw data is immensely important to fraud modeling.

One of the challenges of collecting all this information is that it often exists in different locations and held by different departments. It is also often in various formats and quality. First, before the information is gathered data management technology and processes are needed to clean up and enrich the data, remove duplicates, and improve data quality. Data collection is limited if the data management process is not robust. Once the data is cleaned up, technology is again necessary for gathering the information, converting it to the correct formats, and uploading the information to the correct locations so advanced analytics can be done.

Once the data has been aggregated into data sets it can be sent to fraud detection solutions and tools platform. Most platforms automatically start the fraud detection process. The processes included multiple claim models and different analytical approaches. This is combined with decision-making engines that can handle this stream of information 24 hours a day.

These tools allow companies to identify fraudulent claims based on triggers or events in the data. One identification method is scoring and ranking claims to determine fraud propensity based on values generated by complex algorithms and advanced mathematical techniques. Combined with technology and comparing scores to entire claim populations, insurance companies can determine which claims are outliers, discover questionable links, and suspicious loss indicators. By using technology, SIU teams can focus on the most egregious fraud risks.

SIU teams can also utilize this information to identify emerging trends in billing, fraud, and even geographical trends. This allows companies to create new and more effective strategies. This information can also go further than just fraud detection but can also assist medical management, subrogation claims, determining the possibility of litigation, and even managing the life of a policy. Kompani Risk & Insurance Solutions, Inc. has created the best policies and fraud detection models based on all the data available. To find out how you data helps to protect you contact our CEO R. Glenn Matsen directly on his personal extension at 916-306-5902. We analyze the data to best help you.