Advanced AI-Driven Credit Risk Assessment for Buy Now, Pay Later (BNPL) and E-Commerce Financing: Leveraging Machine Learning, Alternative Data, and Predictive Analytics for Enhanced Financial Scoring

Abstract

The increasing adoption of Buy Now, Pay Later (BNPL) and other financing models in e-commerce presents new challenges in credit risk assessment. Traditional credit scoring models often fail to capture the financial behavior of unbanked or underbanked consumers, necessitating innovative AI-driven approaches (Abbott, 1991). This study explores the integration of deep learning, alternative data sources, and reinforcement learning to enhance credit risk analysis for BNPL financing. By leveraging non-traditional financial indicators such as transactional data, digital footprints, and behavioral analytics, AI-driven credit assessment models can improve predictive accuracy and mitigate default risks (Barakat et al., 1995). The research employs a hybrid methodology combining supervised deep learning techniques with reinforcement learning algorithms to refine credit decision-making (Medvec et al., 1999). Findings indicate that AI-powered financial scoring significantly enhances risk assessment precision compared to conventional models, reducing default rates and improving financial inclusivity. These insights contribute to the ongoing discourse on AI applications in financial technology, offering practical implications for e-commerce platforms, lenders, and regulatory bodies.