Explicable Integration Techniques: Relative Temporal Position Taxonomy

In data-driven anti-fraud engineering for online payment services, the integration of proper function modules is an effective way to further improve detection performance by overcoming the inability of single-function methods to cope with complex and varied frauds. However, a qualified integration is really inaccessible under multiple demanding requirements, i.e., improving detection performance, ensuring decision explainability, and limiting processing latency and computing consumption. In this work, we propose a qualified integration system, named CAeSaR, that can simultaneously meet all of the above requirements. This satisfactory result is achieved by the cooperation of two innovative techniques. The first is a novel three-way taxonomy of function division, called TRTPT, according to the temporal positions of transactions relative to a reference fraudulent transaction. Based on TRTPT, CAeSaR can introduce three kinds of anti-fraud function modules which collaboratively cover all types of frauds theoretically. The second is an effective integration scheme, called TELSI. It generates the candidate decision strategies by combining the judgments of three function modules by only two simple logical connectives, which essentially ensures the decision explainability. Particularly, TELSI can assign the most effective decision strategy to the corresponding transaction adaptively by a devised stacking-based multi-classification. The advantages of CAeSaR are validated in practice over real-life data from a prestigious bank.