By: Seppe vanden Broucke, Bart Baesens
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You asked: Can you provide an overview of the main existing or emerging analytics techniques that are available to practitioners to tackle the problem of fraud?
Our answer:
Most of the fraud detection models in use nowadays are expert based models. These models build upon the experience, intuition and business knowledge of the fraud analyst. It typically involves a manual investigation of a suspicious case, which may have been signaled for instance by a customer complaining of being charged for transactions he/she did not do. Such a disputed transaction may indicate a new fraud mechanism to have been discovered or developed by fraudsters, and therefore requires a detailed investigation for the organization to understand and subsequently address the new mechanism.
As more and more data is being stored about customer behavior, firms can start doing analytics. A first analytical approach is descriptive analytics which starts from an unlabeled data set and performs anomaly detection. Popular techniques are outlier detection to find transactions that deviate from the norm or average behavior. As firms get more mature in their analytics, they can also apply predictive analytics which analyses a labelled data set of historically observed fraud behavior using e.g. regression or neural network approaches. It can be used to both predict fraud occurrence as well as the amount thereof. Finally, social network learning analyses fraud behavior in networks of linked entities such as claims, policyholders, cars, car repair shops, etc. Throughout our research, we have found this approach to be superior to all others. However, it requires a significant investment in resources combined with a strong managerial belief and support in the added value thereof!
