mlsanityData to 14 Oct 2026

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

arXiv 2411.04459 ↗First posted 7 Nov 2024cs.CE cs.LGcited by 2

On the map: LLM reasoning → Reasoning for agents, planning and code

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning. Builds on 7 of its 25 references that are in the map. First posted on arXiv 7 Nov 2024.

Builds on

7 of 25 references.

References · 7

  1. 2017Graph Attention Networkscited by 8,923
  2. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  3. 2017Thinking Fast and Slow with Deep Learning and Tree Searchcited by 252
  4. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 250
  5. 2020A Semi-supervised Graph Attentive Network for Financial Fraud Detectioncited by 151
  6. 2021SymbolicGPT: A Generative Transformer Model for Symbolic Regressioncited by 105
  7. 2015Intelligent Financial Fraud Detection Practices: An Investigationcited by 4

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Modelscited by 384
  2. 2025From System 1 to System 2: A Survey of Reasoning Large Language Modelscited by 320

Similar, not cited

Close in wording, no citation either way

  1. 2025Improving Monte Carlo Tree Search for Symbolic Regression
  2. 2025Navigating the Alpha Jungle: An LLM-Powered MCTS Framework for Formulaic Factor Mining
  3. 2026Reinforcement Learning of Large Language Models for Interpretable Credit Card Fraud Detection
  4. 2018Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization
  5. 2023Beyond Classification: Financial Reasoning in State-of-the-Art Language Models
  6. 2026Self-Evolving Multi-Agent Symbolic Discovery for Financial Fundamental Analysis
  7. 2026A Multi-Scale Graph Learning Framework with Temporal Consistency Constraints for Financial Fraud Detection in Transaction Networks under Non-Stationary Conditions
  8. 2026Semantic Pareto-DQN: A Multi-Objective Reinforcement Learning Framework for Financial Anomaly Detection