UCI Credit dataset

Semantic Pareto-DQN: Multi-Objective RL with LLM State Representations for Financial Fraud Detection

Researchers propose Semantic Pareto-DQN, a multi-objective reinforcement learning framework that addresses class imbalance in financial anomaly detection without data resampling. The approach encodes heterogeneous transaction features as natural-language narratives via LLMs to produce scale-invariant state representations, then optimizes a vectorial reward that decouples fraud detection, false-positive friction, and semantic discovery across the Pareto frontier. Empirical results on E-Commerce fraud and UCI Credit datasets show improved minority-class recall over scalarized baselines, avoiding the 'fraud collapse' failure mode.