  Rules are widely used in Fintech institutions to make fraud prevention
decisions, since rules are highly interpretable thanks to their intuitive
if-then structure. In practice, a two-stage framework of fraud prevention
decision rule set mining is usually employed in large Fintech institutions;
Stage 1 generates a potentially large pool of rules and Stage 2 aims to produce
a refined rule subset according to some criteria (typically based on precision
and recall). This paper focuses on improving the flexibility and efficacy of
this two-stage framework, and is concerned with finding high-quality rule
subsets in a bi-objective space (such as precision and recall). To this end, we
first introduce a novel algorithm called SpectralRules that directly generates
a compact pool of rules in Stage 1 with high diversity. We empirically find
such diversity improves the quality of the final rule subset. In addition, we
introduce an intermediate stage between Stage 1 and 2 that adopts the concept
of Pareto optimality and aims to find a set of non-dominated rule subsets,
which constitutes a Pareto front. This intermediate stage greatly simplifies
the selection criteria and increases the flexibility of Stage 2. For this
intermediate stage, we propose a heuristic-based framework called PORS and we
identify that the core of PORS is the problem of solution selection on the
front (SSF). We provide a systematic categorization of the SSF problem and a
thorough empirical evaluation of various SSF methods on both public and
proprietary datasets. On two real application scenarios within Alipay, we
demonstrate the advantages of our proposed methodology over existing work.
