AI Powered Credit Scoring: Understanding Lender Perspectives and Adoption Dynamics

Abstract

The integration of artificial intelligence (AI) in credit scoring has the potential to revolutionize the financial industry by significantly enhancing the accuracy and efficiency of assessing borrowers' creditworthiness. This research aims to explore the multifaceted dimensions of AI-based credit scoring through four primary objectives. The study will examine the various benefits and challenges associated with implementing AI in credit scoring, highlighting key advantages such as improved risk assessment, faster processing times, and the capability to analyze large datasets for more informed lending decisions. However, several challenges, including data privacy concerns, algorithmic bias, and regulatory compliance, may hinder the widespread adoption of these innovative technologies in financial institutions. Furthermore, the research delves into the attitudes of lenders regarding the use of AI in credit scoring, recognizing that understanding these attitudes is critical, as they significantly influence the acceptance and integration of AI technologies into financial practices. The study seeks to identify the factors that shape lenders' attitude towards the AI based credit scoring model. These factors encompass organizational readiness, technological infrastructure, and the regulatory landscape, all of which play a vital role in determining the effectiveness of AI implementations within financial institutions. Based on the insights gained from this research, actionable recommendations for financial institutions and policymakers will be provided, aimed at enhancing the adoption and effectiveness of AI-based credit scoring systems. Through primary data collection from lenders across various financial sectors, this research will offer valuable insights into the evolving dynamics of credit scoring in the age of AI, ultimately facilitating informed decision-making in the integration of innovative financial technologies.