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

ArXiv ID: 2311.00964 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Fraud Prevention Rules, Pareto Optimality, Precision and Recall, Heuristic Optimization, Rule Set Mining, FinTech/Credit Risk

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced multi-objective optimization theory (Pareto fronts, hypervolume indicators, heuristic frameworks) and mathematical categorization of SSF problems, while demonstrating strong empirical rigor through extensive experiments on public and proprietary datasets, including real-world Alipay applications with concrete metrics.

  flowchart TD
    A["Research Goal: Improve two-stage fraud rule mining\nin Fintech (Precision & Recall trade-off)"] --> B["Data Inputs:\nPublic & Alipay Datasets"]
    B --> C["Stage 1: SpectralRules Algorithm\nGenerates compact, diverse rule pool"]
    C --> D["Intermediate Stage: PORS Framework\nApplies Pareto Optimality to find Non-Dominated Rule Subsets"]
    D --> E["Core Process: Solution Selection on Front (SSF)\nCategorization & Heuristic Optimization"]
    E --> F["Stage 2: Pareto Front Analysis\nFlexible selection of optimal rule subsets"]
    F --> G["Key Findings:\n1. SpectralRules improves subset quality\n2. PORS simplifies selection criteria\n3. Validated via Alipay applications"]