A Study of Integration of Artificial Intelligence in Modern Credit Assessment

Abstract

Credit scoring is one of the most critical procedures in the financial field; it help banks, credit card companies, and other lenders determine whether an individual or business is likely to repay borrowed money. Credit scoring has historically been based on statistical modelling techniques (e.g., logistic regression) and has only taken into account a limited amount of information, such as income, prior repayment history, and current loans/debts. Although these traditional credit scoring methods have worked well for many years, they are not always sufficient to represent the unstructured or complicated financial behaviours of borrowers today. Furthermore, traditional credit scoring is very inadequate when it comes to issuing credit to people who have little or no formal credit history, also referred to as "thin-file" customers.

In recent years, the growing availability of data sources, paired with exponential increases in processing capability, can fundamentally affect the way credit scoring processes operate. Artificial Intelligence (AI) - often understood as any data-mining technique - machine learning and deep learning techniques have become increasing useful for helping a lender to assess credit risk. AI can rapidly sort and analyze large amounts of data. It is capable of identifying patterns that may not have emerged through traditional channels of analysis. A key characteristic of AI is its ability to leverage additional sources of information (or alternative data). AI can look at social media activity, online shopping, cell phone use, digital payment history and other alternative data, presenting a more complete and nuanced view of a borrower's financial behavior and reliability.

This paper examines the various ways AI is being used in credit score models and benefits associated with AI, like accuracy, speed of decision making, and expanding credit to those not previously eligible. By providing a comprehensive review of the literature on AI and equity-based credit scoring, as well as primary data collected in interviews with banking professionals across the United States, this paper highlights the potential benefits of integrating AI in credit scoring processes, together with the obstacles. Major ethical issues such as data privacy, fairness, algorithmic bias and transparency of AI-based decisions must be overcome before most organizations will fully adopt AI for credit scoring.

The study's results indicate that there is likely a consistent difference in predictive accuracy and processing speed of AI models over traditional credit scores; however, the use of AI responsibly and ethically is crucial in the financial services industry to continue developing fair and trusted services. This paper aims to inform financial service organizations, policymakers, and technology developers about the best way to implement and develop AI in credit scoring so that the advancements in development lead to more fair and inclusive financial services solutions. The paper concludes with recommendations for establishing regulatory frameworks and best practices for the use of AI in credit decision-making practices.