A Comparative Analysis of Conventional Creditworthiness Assessment Methods and AI-Based Assessment Methods

This research paper aims to investigate and compare conventional creditworthiness assessment methods with emerging AI-based approaches in the context of lending and financial decision-making. The conventional methods typically rely on historical financial data, credit scores, and collateral evaluation, while AI-based methods leverage machine learning algorithms to analyze alternative data sources and behavioral patterns. The paper will explore the advantages and limitations of each approach, considering factors such as accuracy, transparency, bias, scalability, and regulatory compliance. Additionally, the study will discuss the ethical implications and potential societal impacts of transitioning towards AI-based creditworthiness assessment methods. Through a comprehensive analysis, this research aims to provide insights into the evolution of credit risk assessment practices and inform stakeholders about the implications of adopting AI in lending decisions.