The Role of AI and ML in Alternative Credit Scoring in Fintech Lending

Abstract

This research examines the influence of artificial intelligence (AI) and machine learning (ML) on alternative credit scoring in fintech lending, highlighting the effect of alternative data on financial inclusion and confidence in AI-based credit assessments.  The study, using a sample size of 384 and evaluated via quantitative methodologies with SPSS and Structural Equation Modeling (SEM), emphasizes the need for ethical frameworks and transparent governance to guarantee fairness and accountability in AI-driven credit assessments. Conventional credit assessment techniques often exclude persons with sparse credit histories, whereas AI/ML-driven models use digital footprints, utility payments, and behavioral data to provide a more thorough credit review.  The results demonstrate that alternative data substantially improves the perceived precision of AI/ML credit rating, resulting in heightened confidence in automated conclusions.  Moreover, increased consumer understanding of AI/ML enhances trust in digital lending systems, hence promoting broader financial inclusion.