Rule Sharing for Fraud Detection via Adaptation

Writing rules to capture precisely fraudulent transactions is a challenging task where domain experts spend significant effort and time. A key observation is that much of this difficulty originates from the fact that such experts typically work as "lone rangers" or in isolated groups, or work on detecting frauds in one context in isolation from frauds that occur in another context. However, in practice there is a lot of commonality in what different experts are trying to achieve. In this paper, we present the GOLDRUSH system, which facilitates knowledge sharing via effective adaptation of fraud detection rules from one context to another. GOLDRUSH abstracts the possible semantic interpretations of each of the conditions in the rules at the source context and adapts them to the target context. Efficient algorithms are used to identify the most effective rule adaptations w.r.t a given cost-benefit metric. Our extensive set of experiments, based on real-world financial datasets, demonstrate the efficiency and effectiveness of our solution, both in terms of the accuracy of the fraud detection and the actual money saved.