Deep Learning Architectures for Sequential Decision-Making in Financial Systems: From Fraud Detection to Risk Management

Abstract

Financial institutions face increasingly complex challenges in sequential decision-making tasks, ranging from real-time fraud detection to dynamic risk management. Deep learning (DL) architectures have emerged as powerful tools for addressing these challenges due to their ability to capture temporal dependencies and learn hierarchical representations from sequential financial data. This review examines the application of various DL architectures, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, gated recurrent units (GRUs), and transformer-based models, in financial sequential decision-making contexts. We analyze how these architectures have been adapted to handle unique characteristics of financial data, such as non-stationarity, high noise levels, and the need for interpretability. The review covers applications across fraud detection systems, credit risk assessment, algorithmic trading, portfolio optimization, and market microstructure analysis. We discuss the evolution from traditional machine learning (ML) approaches to modern DL architectures, highlighting their advantages in processing high-dimensional sequential data and making real-time decisions. Furthermore, we examine hybrid architectures that combine multiple DL components to address specific financial tasks, such as attention mechanisms for feature importance and reinforcement learning (RL) for adaptive decision policies. The review also addresses critical challenges including model interpretability, regulatory compliance, data quality issues, and computational efficiency. Through comprehensive analysis of recent developments, this paper provides insights into the current state of DL applications in financial sequential decision-making and identifies promising directions for future research, including explainable artificial intelligence (AI) integration, federated learning for privacy-preserving applications, and quantum-inspired architectures for enhanced computational capabilities.