Year: 2024
Type: article
Abstract: This paper shows the impact of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing credit scoring and loan approval processes, focusing on their ability to provide more accurate, inclusive, and transparent assessments than traditional methods. Non-traditional borrowers have less access to financial services since standard credit scoring systems frequently rely on strict criteria like income and credit history. However, AI and ML models utilize the potential of non-linear correlations across a range of datasets, including transactional and behavioral data, to predict creditworthiness more accurately. The models use neural networks, Explainable AI, and complicated ensemble approaches to assess risk, guaranteeing fair and transparent decisions. Using a comparative analysis that illustrates AI's significant improvements in accuracy, fairness, and processing efficiency, the paper additionally highlights the opportunity for real-time risk assessments, fraud detection, and personalized customer experiences made feasible by AI.
Author: Sri Hari Chitturi
Language: English
Subfield: Accounting
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: diamond
OpenAlex ID: W4404283629