CUSTOMER CREDIT WORTHINESS IN THE DIGITAL AGE: A MANAGEMENT APPROACH TO MACHINE LEARNING APPLICATION IN BANKING

Abstract

This study investigates the shift in private banking from conventional creditworthiness assessment to advanced machine learning (ML) models. Employing a synthesis technique, this study conducts a review of literature and case studies and highlights how ML models, through the integration of alternative big data and advanced algorithms, can enhance accuracy in forecasting customer defaults and contribute to financial inclusion. The research underscores legal and ethical concerns regarding alternative data processing, necessitating thorough compliance checks by banks and regulatory authorities. Furthermore, it underlines the necessity for banks and regulators to develop technical skills to ensure ML models remain transparent and understandable, avoiding the pitfalls of becoming “black boxes”. Future research is suggested to explore risk mitigation strategies based on its ML deployment approach, technical aspects of ML algorithms, and the impact of ML-based credit scoring on broader macro-financial linkages.

Keywords

• risk management
• machine learning
• credit scoring models
• alternative data
• ethical considerations
• regulatory oversight