The Influence of Credit Scores on Loan Approval: A Review of Financial Lending Practices

Abstract

Credit scoring systems are now essential instruments for assessing a person's or company's creditworthiness in the contemporary financial system. The development of credit evaluation is examined in this paper, moving from conventional rule-based techniques to the use of data-driven models driven by AI and machine learning (ML). It highlights how credit scores influence lending decisions across various loan types, streamline loan approvals, and support risk-based pricing strategies. The role of financial institutions and automated decision-making technologies in utilizing credit scores is also discussed, showcasing how these systems enhance efficiency, fairness, and access in the lending process. Furthermore, the importance of truthful credit reporting and using alternative data in extending credit availability to marginalized groups is examined in this article. With growing innovation in fintech, credit scoring models are increasingly incorporating behavioral and real-time data, enabling a more holistic view of borrower profiles. The integration of AI has improved predictive accuracy and accelerated loan processing. These developments are redefining lending practices and contributing to a more dynamic and inclusive financial infrastructure