Financial Systemic Risk Prediction Using Deep Neural Networks and Long Short-Term Memory

Traditional financial risk models do not sufficiently capture the intricacy, nonlinearity and dynamic character of contemporary financial systems, making it challenging to forecast systemic catastrophes in real time. Accurate risk prediction is crucial to avoid financial crises and protect world economies. Value-at-Risk (VaR) and stress testing are two examples of conventional methods that mainly rely on linear assumptions, overlook temporal dependencies and have trouble managing massive, high-dimensional datasets. As a result, risk evaluations are frequently erroneous or delayed. This study aimed to use deep learning to predict financial systemic risk. Initially, we gathered data about China from the IFS and World Bank datasets. The Z-score normalization technique has been used to alter the raw data before processing to guarantee consistency and reliability. High-dimensional problems are limited in scope when using Principle Component Analysis (PCA) models. To model intricate interactions between input factors and systemic risk, Deep Neural Networks (DNN) are utilized. When modeling time-dependent data, use Long Short-Term Memory (LSTM) to capture long-term dependencies and trends. They proposed a hybrid approach that enhances forecast accuracy by integrating qualitative data with conventional financial indicators. Consequently, the results indicate that deep learning in financial systemic risk prediction performs better than other comparable models in terms of F1-score (94%), AUC, accuracy (87% in training and 95% in validation), precision (97%) and prediction rate (89%). We concluded that deep learning models perform better than traditional estimation methods in spotting early warning and offering a stronger foundation for risk control.