Semantic Pareto-DQN: A Multi-Objective Reinforcement Learning Framework for Financial Anomaly Detection
Cláudio Lúcio do Val Lopes, Lucca Machado da Silva
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Key claim
Semantic Pareto-DQN improves minority-class recall in anomaly detection.
In plain English
Financial anomaly detection often struggles with class imbalance, leading to a failure in identifying fraud. Traditional algorithms tend to favor the majority class, resulting in missed anomalies. This paper introduces the Semantic Pareto-DQN, which uses multi-objective reinforcement learning to create a more balanced approach. By leveraging large language models to synthesize transaction features, it improves recall for minority classes without distorting data. Builders in finance might find this framework useful for enhancing fraud detection capabilities while managing operational costs.
Introduces a novel multi-objective reinforcement learning framework for anomaly detection.
Empirical evaluations demonstrate improved performance over scalarized baselines.
Deep reliability assessment
The methodology supports the claim that Semantic Pareto-DQN can improve minority-class recall without data resampling, but the reliance on large language models for state representation may not generalize across all financial environments.
Reproducibility
No open source code or dataset is mentioned in the paper, making reproducibility challenging.
Key figure
Figure 1 likely illustrates the architecture of the Semantic Pareto-DQN framework, highlighting the integration of transaction features and the multi-objective optimization process.