Semantic Pareto-DQN: A Multi-Objective Reinforcement Learning Framework for Financial Anomaly Detection

Financial fraud detection suffers from extreme class imbalance, often causing models to simply predict "no fraud" and miss rare cases. This paper proposes a multi-objective reinforcement learning approach that converts transaction data into natural-language narratives encoded by LLMs, then optimizes a reward balancing fraud detection, customer friction, and semantic insight - without relying on distortive resampling techniques. Tested on e-commerce and credit datasets, it avoids the "zero-recall trap" common to imbalanced classifiers. Applications include real-time fraud detection systems that better balance catching fraud against inconveniencing legitimate customers, offering banks and payment platforms a tunable trade-off frontier. Authors: Claudio Lucio do Val Lopes, Lucca Machado da Silva Paper: https://arxiv.org/abs/2607.09641v1