Evaluating the use of artificial intelligence and machine learning for credit risk modelling in banks

This research looks at the application of AI/ML models in credit risk management in banks and will seek to determine the factors that influence the efficiency of the models in the process. The research also seeks to establish the attitude of employee concerning the interpretability of the models in credit risk management process and seek to analyze the appropriate implications. In this study, respondents' data were gathered through a structured questionnaire; 102 responses were retrieved and analyzed. The regression analysis revealed that credit score (β = -0.228, p = 0.029), debt-to-income ratio (β=0.158, p=0.125), loan amount requested (β=-0.240, p=0.081), and annual income (β=0.003, p=0.980) significantly influence loan outcomes, explaining 14.7% of the variance (R²=0.147, F(5,96) =3.301, p=0.009). Also, the correlation results revealed that the interaction preferences were positively and significantly related to the support of interpretability standards, r = 0. 265, p = 0. 007. Future work should target the continuous monitoring of a model's performance over time, considering macroeconomic and behavioral data, and comparing the performance of different AI/ML methods. This will help start the generation and improvement of better AI/ML model while maintaining sound and ethical best practice for AI/ML models in the rising complex financial environment.