ADVANCING CREDIT RISK MODELING THROUGH GENERATIVE ARTIFICIAL INTELLIGENCE: METHODS, APPLICATIONS, AND CHALLENGES

Abstract

Credit risk modeling is an important component of the financial decision-making process because it determines whether or not credit is given and whether or not the credit given is appropriately used. Classical approaches to logistic regression, scorecards, and sophisticated machine learning techniques have proven helpful in providing institutions with practical prediction capabilities. The same issues, however, confuse these models: unstructured financial data cannot be modelled, rare default events are challenging to model, and there is always a trade-off between model accuracy and model explainability. Recent breakthroughs in generative artificial intelligence (AI) provide a new potential pathway to overcome such inadequacies. The paper explores the potential use of generative models such as generative adversarial networks (GANs), variational autoencoders (VAEs), transformer-based language models, and some of the newer diffusion methods to create more advanced credit risk models. Generative AI practices are assessed on their capability to produce realistic borrower data, model rare credit occurrences, credit scoring with multi-modal data, and dynamic stress testing conditions. It also discusses how generative models can support and augment the existing methodologies in portfolio-level fraud detection, anomaly detection, and risk assessment. Given these opportunities, its adoption in practice presents a challenging problem. Compliance with Basel III/IV and data protection regulations, algorithmic bias and unfair lending outcomes, extensive computational needs, and interpretability of black-box models have become of concern. The barriers to these must be overcome by designing explainable generative AI, Just and Equal education systems, and a governing construct that addresses institutional and regulatory requirements. A synthesis of current approaches, applications, and issues is presented in this paper to consider the role of generative AI as not being merely a technical innovation but also a future opportunity in credit risk modeling, as it mandates a fine line between innovation and trust and compliance in the global financial system.