Artificial Intelligence-Driven Corporate Financial Forecasting and Risk Assessment Modeling

Abstract

With the continuous development of the global economy, the financial risks faced by enterprises have become more and more complex and diverse. In order to achieve more accurate prediction and assessment of enterprise financial risk, this paper constructs financial risk early warning indicators from the perspectives of solvency indicators, operating ability indicators, etc., standardizes the data using Max-Min standardization method, and reduces the dimensionality of the financial risk early warning indicators with the help of Principal Component Analysis (PCA), which reduces the problem of multiple covariance in the process of model construction. Based on the deep learning method, the combination of self-encoder and convolutional neural network is used to construct the enterprise financial prediction and risk assessment model based on AE-ResNet. Comparing this paper's model with traditional models such as ANN, 1DCNN, 2DCNN, etc., this paper's model performs optimally in evaluation indexes such as accuracy (0.9637), TPR (0.9415), G-Mean(0.9533) and AUC (0.9895), and the model has excellent performance.