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The National Payments Corporation of India (NPCI) faces two major challenges: Increasing Unified Payments Interface (UPI) Frauds and their large, imbalanced dataset. It uses an ensemble of AI models to combat UPI fraud. As the methods of fraudsters evolve, models need to be trained/retrained on new data. With frequent retraining on various algorithms, frequent Hyperparameter Optimization (HPO) becomes a challenging task. For models with many tunable hyperparameters, the dimensionality of the hyperparameter space grows, ruling out traditional methods like Grid Search. This paper discusses the use of the Advantage Actor Critic (A2C) algorithm, a Reinforcement Learning (RL) algorithm to fine tune the hyperparameters of an XGBoost Classifier model. The performance was compared with Random Search (RS), Bayesian Optimization (BO), Particle Swarm Optimization (PSO), and Canonical Genetic Algorithm (CGA). A2C gave the best results by achieving the 80% F1-score target within 4.01 h (40% faster than CGA, the next fastest algorithm.)

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Reinforcement Learning for UPI Fraud Detection: Actor Critic Optimization Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Raj Jani,
  • Medabalimi Ravi Kumar,
  • Nikhil Gumasthi,
  • Saurav Singla,
  • Thirumalai Kailasam

摘要

The National Payments Corporation of India (NPCI) faces two major challenges: Increasing Unified Payments Interface (UPI) Frauds and their large, imbalanced dataset. It uses an ensemble of AI models to combat UPI fraud. As the methods of fraudsters evolve, models need to be trained/retrained on new data. With frequent retraining on various algorithms, frequent Hyperparameter Optimization (HPO) becomes a challenging task. For models with many tunable hyperparameters, the dimensionality of the hyperparameter space grows, ruling out traditional methods like Grid Search. This paper discusses the use of the Advantage Actor Critic (A2C) algorithm, a Reinforcement Learning (RL) algorithm to fine tune the hyperparameters of an XGBoost Classifier model. The performance was compared with Random Search (RS), Bayesian Optimization (BO), Particle Swarm Optimization (PSO), and Canonical Genetic Algorithm (CGA). A2C gave the best results by achieving the 80% F1-score target within 4.01 h (40% faster than CGA, the next fastest algorithm.)