Semantic Pareto-DQN: A Multi-Objective Reinforcement Learning Framework for Financial Anomaly Detection
Researchers introduce Semantic Pareto-DQN, a multi-objective reinforcement learning framework that uses LLMs to detect financial anomalies without resampling data.
- Semantic Pareto-DQN addresses the 'fraud collapse' problem in financial anomaly detection by using multi-objective reinforcement learning.
- The framework avoids data resampling by synthesizing transaction features into natural-language narratives encoded by LLMs.
- It balances anomaly detection with customer friction, offering a more adaptive solution to fraud detection.
- The approach demonstrates the potential of combining RL and NLP for interpretable financial AI systems.
Financial institutions face a persistent challenge with anomaly detection due to extreme class imbalance, where traditional single-objective algorithms often default to the majority class, a phenomenon known as 'fraud collapse'. To address this, researchers have developed Semantic Pareto-DQN, a multi-objective reinforcement learning framework designed to overcome these limitations without resorting to distortive data resampling techniques.
The framework synthesizes heterogeneous transaction features into cohesive natural-language narratives, which are then encoded by large language models. This process produces a robust and scale-invariant state representation, enabling the reinforcement learning agent to effectively balance anomaly interdiction with minimizing customer friction. The approach aims to provide a more nuanced and adaptive solution to financial fraud detection, addressing the shortcomings of existing methods that struggle with imbalanced datasets.
The research highlights the potential of combining reinforcement learning with natural language processing to create more sophisticated and interpretable anomaly detection systems in finance.
Introduces a novel framework for financial anomaly detection using reinforcement learning and LLMs.
Offers a more effective and interpretable method for detecting financial fraud without data distortion.
Provides a case study in applying multi-objective RL and NLP to real-world problems.
- fraud collapse
- A failure of anomaly detection systems to identify fraud due to extreme class imbalance, defaulting to the majority class.
- reinforcement learning
- A machine learning paradigm where an agent learns to make decisions by interacting with an environment to maximize cumulative reward.
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