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Rules are widely used in Fintech institutions to make fraud preventiondecisions, since rules are highly interpretable thanks to their intuitiveif-then structure. In practice, a two-stage framework of fraud preventiondecision rule set mining is usually employed in large Fintech institutions.This paper is concerned with finding high-quality rule subsets in abi-objective space (such as precision and recall) from an initial pool ofrules. To this end, we adopt the concept of Pareto optimality and aim to find aset of non-dominated rule subsets, which constitutes a Pareto front. We proposea heuristic-based framework called PORS and we identify that the core of PORSis the problem of solution selection on the front (SSF). We provide asystematic categorization of the SSF problem and a thorough empiricalevaluation of various SSF methods on both public and proprietary datasets. Wealso introduce a novel variant of sequential covering algorithm calledSpectralRules to encourage the diversity of the initial rule set and weempirically find that SpectralRules further improves the quality of the foundPareto front. On two real application scenarios within Alipay, we demonstratethe advantages of our proposed methodology compared to existing work.

On Finding Bi-objective Pareto-optimal Fraud Prevention Rule Sets for Fintech Applications