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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. This paper will look at a comprehensive framework to combine the results from text data analysis, social networks, and artificial intelligence in order to improve the accuracy of fraud risk models, while also maintaining an easy-to-implement and easy-to-interpret design. The paper will include the results of its application using data of a major insurance company in South Africa.