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

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.