GPT-Guided Monte Carlo Tree Search for Symbolic Regression in Financial Fraud Detection

TL;DR

This paper presents SR-MCTS, a novel method combining GPT-guided Monte Carlo Tree Search for symbolic regression, enabling faster, more transparent financial fraud detection compared to existing black-box models.

Contribution

Introduces SR-MCTS, integrating GPT with MCTS for efficient, explainable symbolic regression in financial fraud detection, improving speed and interpretability.

Findings

Outperforms industry-standard fraud detection methods in efficiency

Provides transparent rule-based expressions for decision-making

Enhances convergence speed of symbolic regression models

Abstract

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.