Author(s)

Jiangdan Liu, Qiang Lu, Zhibin Wang, Hongyu Li

Corresponding Author

Qiang Lu

Abstract

The application of artificial intelligence technology in financial risk identification and management is of great significance. Firstly, by numerically processing risk assessment indicators, the risk situation is transformed into quantifiable data, so that the severity of different risks can be accurately compared and analyzed. Secondly, the data normalization method is adopted to make the values of different indicators have relative comparability and weight in the evaluation. Utilizing artificial intelligence modeling techniques, this study aims to develop a predictive framework tailored for the identification and management of financial risks. By integrating a two-layer convolutional neural network, fully connected layers, and complementary structures, alongside suitable activation and loss functions, the model seeks to enhance accuracy and efficiency in financial risk assessment. Finally, the optimized artificial intelligence prediction model is combined with the numerical second-level index feature vector of financial companies, and the parameters of the model are constantly updated and optimized through several iterations, and the optimal model state for financial risk assessment and prediction is finally obtained, which is conducive to improving the stability and security of the financial market.

Keywords

artificial intelligence; Financial risk; Numerical processing; Data normalization; eigenvector