Optimization of Enterprise Financial Risk Management and Crisis Early Warning System Supported by AI

Yu‐Hsi Yuan · Gang Wang · Hsin‐Te Wu

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Financial Distress and Bankruptcy Prediction Stock Market Forecasting Methods Evaluation and Optimization Models

Abstract

The stability of the financial market plays a key role in the overall health of the economic system, and optimizing corporate financial risk management and crisis warning systems is of great significance. Traditional financial risk management methods often face challenges when processing large-scale, resulting in a decrease in the accuracy of risk assessment. This study combining gated recurrent units (GRU), temporal convolutional networks (TCN) and attention mechanisms. Leverage diverse data sets to improve the accuracy of financial risk assessments and the effectiveness of crisis warnings. Experimental results show that the algorithm outperforms the baseline model in terms of risk prediction accuracy, efficiency and stability. For example, on the LendingClub loan dataset, FLOPs are reduced by more than 46.8%, inference time is improved by more than 48.5%. This method provides new perspectives and solutions for optimizing corporate financial risk management and crisis warning systems. This will have a positive impact on the stable operation of enterprises and other aspects.

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2022Risk prediction in financial management of listed companies based on optimized BP neural network under digital economy 2023Digital finance and corporate financial fraud 2021Research on financial early warning of mining listed companies based on BP neural network model 2022Deep Learning-Based Model for Financial Distress Prediction 2022Research on optimization of an enterprise financial risk early warning method based on the DS-RF model 2023Can Artificial Intelligence (AI) Manage Behavioural Biases Among Financial Planners?2021Exploration of Financial Market Credit Scoring and Risk Management and Prediction Using Deep Learning and Bionic Algorithm 2023Evolving deep gated recurrent unit using improved marine predator algorithm for profit prediction based on financial accounting information system

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