Predictive Analytics for Financial Risk Management Using Machine Learning and Econometric Techniques

Abstract

Financial institutions are facing a rapidly evolving and challenging landscape with heightened uncertainty, where precise forecasting of financial risks is crucial for stability, profitability, and compliance with regulatory requirements. While traditional econometric models have been used for years as the basis for assessing financial risk, the increased volume, velocity and variety of financial information has led to the development of more sophisticated techniques for predicting risk. The purpose of this study is to investigate the potential of using predictive analytics techniques in financial risk management by combining machine learning algorithms and econometric methods. The research is designed to test the ability of both approaches to identify and predict the major financial risks, such as credit risk, market risk, liquidity risk and systemic risk. Secondary financial data and comparative predictive modelling are used to adopt quantitative research methodology. In addition to machine learning algorithms like Random Forest, Support Vector Machine, Gradient Boosting, Artificial Neural Networks and Deep Learning models, econometric methods are analyzed based on logistic regression, autoregressive integrated moving average (ARIMA), generalized autoregressive conditional heteroskedasticity (GARCH) and vector autoregression (VAR). Performance of the model is evaluated using common model evaluation metrics namely accuracy, precision, recall, F1-score, root mean square error (RMSE), mean absolute error (MAE) and area under the receiver operating characteristic curve (ROC-AUC). The results show that machine learning models as a rule are better at predicting than the classical econometric models, and they can also better capture the complex nonlinearities in financial data; on the other hand, the econometric models still have advantages in terms of interpretability and statistical inference. The study suggests that the hybrid predictive framework combining the machine learning and econometric methods provide more powerful and reliable means for financial risk management. This comprehensive approach can boost the accuracy of risk prediction, aid in decision-making, bolster regulatory adherence, and enhance organizational resilience to financial challenges.