Construction and Application of Financial Risk Early Warning Model based on Machine Learning

Abstract

In today's financial environment, listed corporations face increasingly complicated markets and volatile financial risks. The corporations realize the critical significance of financial risk management in the long-term development of their businesses. The difficulty is that market conditions change frequently, and existing methodologies make it impossible to identify risks in a timely and reliable manner. In order to prevent losses in the future and maintain the financial health of the organisation, it is now essential to improve the timeliness and accuracy of financial risk prediction. In this manuscript Construction and Application of Financial Risk Early Warning Model based on Machine Learning (CSN-FREWM-ML-AIDINN) is proposed. Market data, Financial statements  and financial risk event data are the first sources of data gathered. The gathered data are then put into pre-processing. In pre-processing, Innovation Saturated Koopman Kalman Filter (ISKKF) is used for normalization the data. After pre-processing the output is fed to Anti-Interference Dynamic Integral Neural Network (AIDINN) for predicting the financial risk. Typically, the AIDINN predictor does not provide ways for optimising parameters to guarantee precise financial risk prediction. Hence, proposed Red Panda Optimization Algorithm (RPOA) enhances AIDINN, accurately predict the financial risk. The proposed method is implemented in python version and efficacy of the CSN-FREWM-ML-AIDINN technique is assessed with support of numerous performances like f1-score, recall, Root mean square error, accuracy, Error rate, Index of error, profitability and Development capacity are analysed. The performance of the proposed CSN-FREWM-ML-AIDINN approach contain 22.36%, 25.42% and 18.17%high accuracy; 21.26%, 15.42% and 19.27% high precision and 25.29%, 28.36% and 28.55%  low error rate when analysed to the existing methods like Financial Risk Early Warning Model for Listed Companies Using BP Neural Network and Rough Set Theory (FREWM-LC-BPNN),  Financial Risk Early Warning Based on Wireless Network Communication and the Optimal Fuzzy SVM Artificial Intelligence Model (FREW-WNC-PNN) , An Early Control Algorithm of Corporate Financial Risk Using Artificial Neural Networks (ECAC-FR-ANN) methods respectively.