In the insurance business, fraud – especially fraud from organized crime rings – can cut deeply into profits. In the past, fraud detection relied on automated business rules, which anticipates suspicious claim activity based on past fraud. But business rules alone aren’t enough. It’s too difficult to uncover complicated fraud schemes with black-and-white rules. Rules can also result in false positives, which delay legitimate claims. And even when rules uncover one fraud scheme, new schemes continually evolve. The best way to identify fraud is to look at relationships in the data and compile information from disparate systems. So to dig deeper and uncover complex fraud schemes typical of organized crime, SBM – an insurance information and monitoring center in Turkey – turned to SAS® Detection and Investigation for Insurance. “Every year, Turkey issues 16 million motor liability and 5 million comprehensive car insurance policies. And every year, the country gets 2.5 million damage claims,” says Aydin Satici, SBM General Manager. SBM examines those claims for potential fraud. Its focus is more on organized fraud cases than individual, unique cases. And because detecting complex fraud schemes requires seeing the whole picture, SBM looks for fraud cases that a single insurance company wouldn't normally be able to detect on its own. “Typically, organized fraud involves multiple companies, experts and repair shops. Because we have comprehensive data on Turkey’s insurance claims, we can see the relationships among stakeholders more clearly,” Satici explains. “But to connect all this data and reveal the fraud network, we needed to commission an advanced analytics solution, which is why we chose SAS Detection and Investigation for Insurance.” Using the SAS solution, SBM established a fraud-analysis system. This system allows SBM to aggregate data from the insurance companies and use analytic models to assign risk scores to every claim. Cases with high-risk scores carry an increased likelihood of being fraudulent. And by using near-real-time daily batch scoring, SAS helps SBM detect fraud early in the cycle before payments occur. SBM can now explore historical data to identify repeat offenders and uncover insider fraud by integrating staff data and audit records and applying risk- and value-based scoring models to prioritize output for investigators. SBM then shares the risk scores with the insurance companies. And when the system identifies potential fraud or indicators of organized crime, SBM provides this information to the SISEB (Turkey’s Insurance Anti-Fraud Bureau) to investigate and take necessary action. The only way to make this work is to analyze large amounts of data crucial to uncovering fraud. “Left to their own devices, insurance companies can detect 5 percent of ongoing fraud,” Satici says. “But with our method powered by SAS, we believe that the rate of fraud detection will increase sharply.” The use of SAS Detection and Investigation for Insurance is already having a significant impact on profitability. “Our focus on analytics over business rules has led to the discovery of 259 million TL [US $86 million] in potential fraud cases within the first nine months of using the solution,” Satici says. “The effect on profitability would be enormous if the insurance companies could detect more fraud cases and were no longer required to pay the damage claims that they’ve always been paying. This would also have a positive effect on insurance policy pricing, allowing insurers to offer lower rates, which leads to happier customers.”
Turkish insurance information and monitoring centre, Sigorta Bilgi ve Gozetim Merkezi (SBM), has partnered with big data analytics firm SAS to tackle organised car insurance fraud. Using SAS's Fraud Framework for Insurance, data from car insurance firms is aggregated, before analytical models assign risk scores to each claim. The digital solution analyses the information in complex current and historical cases more efficiently than a human, resulting in SBM discovering TRY259m ($86m) in potential fraud cases in daily of using the SAS software.