Revolutionizing risk management in banking: Implementation of AI/ML-based gradient boosting machines (GBM) and random forest models for credit risk management

Abstract

This paper is aimed at explaining the Gradient Boosting Machine (GBM) and Random Forest model's role in the banking industry's credit risk management. Starting with collecting and cleaning the required data, which entails demographic data, financial information, loan details, and economic indicators, the report explains the training and assessment of gradient boosting machine (GBM) and random forest models. Measures like accuracy, precision, recall, F1-score, and area under the ROC curve are employed to validate the efficiency of a model. After that, the practical implications of using GBM and Random Forest models in a banking operation are inspected regarding decision-making process improvements, fewer defaults, and higher banking profit. Introduction The necessity of resolving credit risk through accurate assessment and management in the current situation cannot be emphasized more than before in the financial business sphere. A game-changing dive has led to the widespread use of AI and ML methods (GBM and Random Forest algorithms). Such technologies open new horizons, expanding their analysis capacities to uncover unknown dynamics, find similar patterns, and predict credit risk with unmatched precision credit risk. GBMs and random forests shine out in finding complicated relations with non-linear features of financial data, which banks endow with a first-rate instrument to judge a borrower's creditworthiness. Through this, banks can use the available models to boost their underlying processes when making lending decisions. This will translate to lower default rates and, in turn, better profitability. Successful application of data science in financial risk management, however, calls not only for robust technological infrastructure but also for efficient cooperation between data professionals, risk managers, and business leaders to bring together the model outputs and business objectives, as well as regulatory compliance. Nevertheless, AI/ML still delivers the promise to credit risk management initiatives due to open data quality issues, model interpretability, and regulation, which require ongoing development and innovation to minimize the risks.