Integrating Deep Learning and Reinforcement Learning for Enhanced Financial Risk Forecasting in Supply Chain Management

Yuanfei Cui and Fengtong Yao ()
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Yuanfei Cui: Inner Mongolia Agricultural University
Fengtong Yao: Inner Mongolia Agricultural University

Journal of the Knowledge Economy, 2024, vol. 15, issue 4, No 168, 20110 pages

Abstract: Abstract In today’s dynamic business landscape, the integration of supply chain management and financial risk forecasting is imperative for sustained success. This research paper introduces a groundbreaking approach that seamlessly merges deep autoencoder (DAE) models with reinforcement learning (RL) techniques to enhance financial risk forecasting within the realm of supply chain management. The primary objective of this research is to optimize financial decision-making processes by extracting key feature representations from financial data and leveraging RL for decision optimization. To achieve this, the paper presents the PSO-SDAE model, a novel and sophisticated approach to financial risk forecasting. By incorporating advanced noise reduction features and optimization algorithms, the PSO-SDAE model significantly enhances the accuracy and reliability of financial risk predictions. Notably, the PSO-SDAE model goes beyond traditional forecasting methods by addressing the need for real-time decision-making in the rapidly evolving landscape of financial risk management. This is achieved through the utilization of a distributed RL algorithm, which expedites the processing of supply chain data while maintaining both efficiency and accuracy. The results of our study showcase the exceptional precision of the PSO-SDAE model in predicting financial risks, underscoring its efficacy for proactive risk management within supply chain operations. Moreover, the augmented processing speed of the model enables real-time analysis and decision-making — a critical capability in today’s fast-paced business environment.

Keywords: Financial risk forecasting; Supply chain management; Deep autoencoder; Reinforcement learning; Data mining (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13132-024-01946-5

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