Improving the Defense Lines: The Future of Fraud Detection in the Insurance Industry (with Fraud Risk Models, Text Mining, and Social Networks)

Given the current global economic turmoil and contracting economies, financial crime is on the rise. The use of analytical techniques to protect financial institutions against fraudulent activity has seen varying degrees of success in the past. Recent advances include the use of rule-based fraud detection flags, exception reporting, third-party data searching, profiling, and fraud scorecards based on quantitative data. More recently, advanced analytical techniques such as text mining and social networks have also been used to effectively support the fraud investigation process. Artificial intelligence algorithms can be used to detect human involvement where it is not expected, even where suspicious activity has not yet been detected.