Version 1
: Received: 9 October 2024 / Approved: 9 October 2024 / Online: 9 October 2024 (13:11:06 CEST)
How to cite:
Ju, C.; Zhu, Y. Reinforcement Learning‐Based Model for Enterprise Financial Asset Risk Assessment and Intelligent Decision‐Making. Preprints2024, 2024100698. https://doi.org/10.20944/preprints202410.0698.v1
Ju, C.; Zhu, Y. Reinforcement Learning‐Based Model for Enterprise Financial Asset Risk Assessment and Intelligent Decision‐Making. Preprints 2024, 2024100698. https://doi.org/10.20944/preprints202410.0698.v1
Ju, C.; Zhu, Y. Reinforcement Learning‐Based Model for Enterprise Financial Asset Risk Assessment and Intelligent Decision‐Making. Preprints2024, 2024100698. https://doi.org/10.20944/preprints202410.0698.v1
APA Style
Ju, C., & Zhu, Y. (2024). Reinforcement Learning‐Based Model for Enterprise Financial Asset Risk Assessment and Intelligent Decision‐Making. Preprints. https://doi.org/10.20944/preprints202410.0698.v1
Chicago/Turabian Style
Ju, C. and Yida Zhu. 2024 "Reinforcement Learning‐Based Model for Enterprise Financial Asset Risk Assessment and Intelligent Decision‐Making" Preprints. https://doi.org/10.20944/preprints202410.0698.v1
Abstract
This study discusses the application of reinforcement learning (RL) in financial asset risk assessment, especially how to improve the ability of risk prediction and asset portfolio optimization through deep reinforcement learning (DRL) model. Traditional financial risk assessment methods are often unable to effectively cope with the dynamic changes and complexities of the financial market, but RL technology provides innovative solutions for modern financial risk management by means of real-time adjustment and adaptation mechanism. This paper experimentally verifies the advantages of DRL models in improving forecasting accuracy and risk management efficiency and discusses the potential impact of AI technology in the financial sector. The findings show that despite challenges such as data quality and model interpretation, the application of AI technology provides financial institutions with more precise and flexible risk management tools, driving further development of fintech.
Keywords
reinforcement learning; deep reinforcement learning; financial risk assessment; portfolio optimization
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
