用GPT引导搜索，快速生成可解释的反欺诈规则。
GPT-Guided Monte Carlo Tree Search for Symbolic Regression in Financial Fraud Detection
- 用GPT指导蒙特卡洛树搜索，加速符号回归
- 生成的规则准确率高于行业主流方法
- 适合需要透明决策的金融风控场景
随着在线金融服务增多，金融诈骗案件持续上升。互联网流量与交易速率大幅提升，要求系统具备快速决策能力。金融机构还面临严格监管，需保证决策过程的透明性与可解释性。然而当前工业界广泛使用的算法多为高参数化的黑箱模型，依赖复杂计算生成评分，不仅运行缓慢，且缺乏传统规则学习者的可解释性与速度优势。本文提出SR-MCTS（符号回归蒙特卡洛树搜索），利用基础GPT模型引导MCTS搜索，显著提升表达式生成的收敛速度与质量，并从中提取可解释规则。实验表明，SR-MCTS在反欺诈任务中比行业常用方法更高效，同时提供丰富的决策洞察。
原文摘要 · Abstract (English)
With the increasing number of financial services available online, the rate of financial fraud has also been increasing. The traffic and transaction rates on the internet have increased considerably, leading to a need for fast decision-making. Financial institutions also have stringent regulations that often require transparency and explainability of the decision-making process. However, most state-of-the-art algorithms currently used in the industry are highly parameterized black-box models that rely on complex computations to generate a score. These algorithms are inherently slow and lack the explainability and speed of traditional rule-based learners. This work introduces SR-MCTS (Symbolic Regression MCTS), which utilizes a foundational GPT model to guide the MCTS, significantly enhancing its convergence speed and the quality of the generated expressions which are further extracted to rules. Our experiments show that SR-MCTS can detect fraud more efficiently than widely used methods in the industry while providing substantial insights into the decision-making process.
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