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.

翻译：随着在线金融服务数量的日益增长，金融欺诈的发生率也在不断上升。互联网流量和交易速率的大幅提升，催生了对快速决策的需求。金融机构同时面临严格的监管要求，往往需要决策过程具备透明度和可解释性。然而，目前业界使用的大多数先进算法都是高度参数化的黑盒模型，依赖复杂计算生成评分。这些算法本质上是缓慢的，并且缺乏传统基于规则的学习器所具有的可解释性和速度。本研究提出了SR-MCTS（符号回归蒙特卡洛树搜索），该方法利用基础GPT模型引导MCTS，显著提升了其收敛速度以及所生成表达式的质量，这些表达式可进一步提取为规则。我们的实验表明，SR-MCTS能够比业界广泛使用的方法更高效地检测欺诈，同时为决策过程提供重要的洞察。