Optimization System for Financial Early Warning Model Based on the Computational Intelligence and Neural Network Method

In India right now, there is a rapid increase in the number of businesses experiencing financial difficulties, and businesses’ overall resilience to risks is low. As a result of advances and changes throughout time, traditional financial accounting has developed into management accounting. Accountants will need to improve their skills and knowledge to add more value to their clients’ businesses in the age of computational intelligence. To establish a corporate financial crisis early warning system, this paper selects the two-year data of five companies from 2019 to 2021 for training samples and the data of five companies for prediction samples, with the goal of detecting the early warning signs of a corporate financial crisis and alerting managers in advance so that they can take swift, decisive action to eliminate any potential threats. Based on the results of the tests, the 6 index variables that best capture the energy industry’s financial woes have been chosen as the starting point for the modeling. Using In order to better the early-warning effect of enterprise financial crisis management and reduce the occurrence of enterprise financial crises, a financial crisis early-warning indicator system was developed from the five aspects of profitability: debt-paying ability, development ability, operation ability, and cash flow ability, using listed companies as examples.crises. We analyse and evaluate data from 2019 to 2021 using operational and Bayesian neural network models, to foresee fiscal risk in 2021. When comparing the two models, neural network for BP model does better than the logical model in terms of how well it fits the data and how well it predicts the future.