金融欺诈检测的老大难问题有了解法：Semantic Pareto-DQN 不用重采样，直接多目标优化，既能抓到欺诈又少误拦正常交易。
金融异常检测面临极端类别不平衡，传统单目标算法常出现“欺诈崩溃”。研究者提出 Semantic Pareto-DQN，一个基于多目标强化学习的框架。它利用大语言模型将异构交易特征编码为自然语言状态表示，并优化包含金融效能、操作摩擦和语义发现的向量奖励。在电子商务欺诈和UCI Credit数据集上，该框架成功打破零召回陷阱，相比标量化基线提升了少数类召回率。
Semantic Pareto-DQN: A Multi-Objective Reinforcement Learning Framework for Financial Anomaly Detection
Financial anomaly detection suffers from extreme class imbalance, causing traditional single-objective algorithms to exhibit ``fraud collapse'', defaulting to the majority class and failing to balance anomaly interdiction with customer friction. To overcome this without distortive data resampling, we propose the Semantic Pareto-DQN, a multi-objective reinforcement learning framework. Our approach synthesizes heterogeneous transaction features into cohesive natural-language narratives, encoded by large language models, thereby producing a robust, scale-invariant state representation. The agent optimizes a vectorial reward that explicitly decouples financial efficacy, operational friction, and semantic discovery. By mapping the continuous Pareto frontier, the system dynamically navigates the asymmetric costs of missed anomalies versus false positives. Empirical evaluations across E-Commerce fraud and UCI Credit datasets show that semantic Pareto-DQN successfully shatters the zero-recall trap. It achieves superior minority-class recall compared to scalarized baselines, providing an alternative to trade bounded operational friction for financial anomaly discovery.