Financial distress prediction, the crucial link of enterprise risk management, is also the core of enterprisefinancial distress theory. With currently global economic recession and the gradual perfection of artificialintelligence technology, the study in this paper begins by optimizing the back-propagation (BP) neural networkmodel using the genetic algorithm (GA). In doing so, it can overcome the deficiency that the BP neural networkmodel is slow in convergence and easily trapped into local optimal solution. The study then conducts trainingand tests on the optimized GA-BP neural network model, using financial distress data from Chinese listedenterprises. As can be seen from the experimental results, the optimized GA-BP neural network model issignificantly improved in terms of the accuracy and stability in financial distress prediction. The study in thispaper not only provides an effective test model for the automatic recognition and early warning of enterprisefinancial distress, but also contributes to new thoughts and approaches for the application of artificialintelligence in the field of financial accounting.
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