Financial Risk Warning Based on Bayesian Neural Networks and Variational Auto Encoders

In recent days, predicting financial risk warnings is essential for protecting economic stability, and enabling practical actions against credit defaults, market crises, and systemic risks. Previous researchers suggested various traditional models but still, because of the complexity of and high-dimensional financial data there are challenges such as; noise, temporal dependencies, and macroeconomic factors and thus ensuring model interpretability for stakeholders, and computing uncertainty to enhance trust in predictions. This paper suggests a novel approach by incorporating ensemble methods such as Bayesian Neural Network (BNN), Long Short-Term Memory (LSTM) and Variational Auto Encoders (VAE) for effective and early financial risk prediction. Initially, the data is collected from a large dataset embedded into the Exploratory Factor Analysis (EFA) model for categorizing the data into factors. Further, the data is fed into the prediction model which is integrated using BNN, LSTM, and VAE for effective prediction of financial risk. Hence, the proposed BNN-LSTM-VAE has acquired greater results with Mean Absolute Error (MAE) of 0.07843, Mean Absolute Percentage Error (MAPE) of 3.947, and Mean Square Error (MSE) of 0.00851 respectively when compared with existing model integrated using Factor Analysis (FA), Particle Swarm Optimizer and LSTM namely FA-PSO-LSTM.