ENHANCING STRESS-TESTING FRAMEWORKS IN COMMERCIAL BANKS: INTEGRATING MACROECONOMIC SHOCKS AND MACHINE LEARNING MODELS IN EMERGING MARKETS

This thesis explores ways to enhance stress-testing frameworks in commercial banks by integrating macroeconomic shocks and machine learning (ML) models in emerging markets. Based on an analysis of Basel III supervisory standards, Presidential Decree No. DP-5992, and scholarly literature, it is shown that while traditional linear stress tests are established, they fail to capture non-linear macro-financial tail risks and high-dimensional borrower default dynamics. A conditional sequence for implementation, the "macroeconomic shock vector – hybrid ML credit-loss engine – explainable AI (XAI) capital trigger" principle, and five readiness assessment indicators are substantiated.