Article: A real-time financial risk detection model for enterprises based on distributed reinforcement learning Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.66 pp.1 - 19 Abstract: It is very important to be able to find corporate financial risks in real time in order to protect financial stability and make sure that businesses may grow in a sustainable way. This work presents a distributed reinforcement learning-based detection model to overcome the shortcomings of conventional forecasting techniques. Initially, it utilises a distributed computing architecture to effectively handle multi-source financial data; subsequently, it dynamically acquires knowledge of corporate financial conditions and forecasts risks; ultimately, it improves model adaptability and predictive precision through the integration of multi-source data. To validate the model's effectiveness, comparative and ablation experiments demonstrated superior performance. Its prediction accuracy and recall rate reached 87.6% and 82.9% respectively, representing a significant breakthrough over traditional methods. The model provides good technical support for managing corporate financial risk. Inderscience Publishers - linking academia, business and industry through research

Title: A real-time financial risk detection model for enterprises based on distributed reinforcement learning

Authors: Min Hu; Wei Jin

Addresses: School of Accounting and Finance, Taizhou Vocational College of Science and Technology, Taizhou, 318020, China ' School of Accounting and Finance, Taizhou Vocational College of Science and Technology, Taizhou, 318020, China

Abstract: It is very important to be able to find corporate financial risks in real time in order to protect financial stability and make sure that businesses may grow in a sustainable way. This work presents a distributed reinforcement learning-based detection model to overcome the shortcomings of conventional forecasting techniques. Initially, it utilises a distributed computing architecture to effectively handle multi-source financial data; subsequently, it dynamically acquires knowledge of corporate financial conditions and forecasts risks; ultimately, it improves model adaptability and predictive precision through the integration of multi-source data. To validate the model's effectiveness, comparative and ablation experiments demonstrated superior performance. Its prediction accuracy and recall rate reached 87.6% and 82.9% respectively, representing a significant breakthrough over traditional methods. The model provides good technical support for managing corporate financial risk.

Keywords: distributed reinforcement learning; corporate financial risk; real-time detection; multi-source data fusion.

DOI: 10.1504/IJICT.2026.154125

International Journal of Information and Communication Technology, 2026 Vol.27 No.66, pp.1 - 19

Received: 09 Sep 2025
Accepted: 11 Dec 2025
Published online: 13 Jun 2026 *

Title: A real-time financial risk detection model for enterprises based on distributed reinforcement learning

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