Machine Learning-Driven Risk Management Strategies for Enhancing Stability in the Financial Sector

Abstract

The increasing sophistication and speed of
financial dealings are challenging and require sophisticated risk
management mechanisms which cater to a large-scale,
diversified and dynamically changing data. This paper
introduces a machine learning-powered framework on
optimizing financial sector stability, specifically, towards credit
risk prediction. Exploiting the German credit data, the strategy
uses strict reprocessing, characteristic selection, and data
overlapping with Synthetic Minority Over-sampling Technique
(SMOTE) that operates on data imbalance and enhances the
robustness of the models. Random Forest (RF) classifier is
applied because it handles nonlinear patterns, it is able to avoid
overfitting and also provides a readable feature importance. The
model performs ultra-high in terms of the predictive
performance, as accuracy, precision, recall, F1-score, and ROCAUC results show an accuracy of 97.61%. Comparison of
accuracy to Gradient Boosting, SVM and GRU shows the
outstanding performance of the RF model in respect to
categorizing credit risks. The results suggest that the framework
that is proposed is scalable and interpretable to provide a
solution to proactive risk management. The study advances the
research literature in that it would offer an effective model that
facilitates enlightened choices, fewer theoretical losses and
establish systemic resiliency within fast changing financial
marketplaces. The work envisaged in the future the
incorporation of real-time financial and sentiment data to build
an even greater level of predictive ability.