Regulatory and Ethical Challenges in AI-Driven and Machine learning Credit Risk Assessment for Buy Now, Pay Later (BNPL) in U.S. E-Commerce: Compliance, Fair Lending, and Algorithmic Bias

The integration of artificial intelligence (AI) and machine learning (ML) in credit risk assessment for Buy Now, Pay Later (BNPL) services has transformed the U.S. e-commerce landscape. However, these advancements present significant regulatory and ethical challenges, particularly regarding compliance, fair lending practices, and algorithmic bias. This study examines the legal framework governing BNPL credit assessments, including adherence to the Equal Credit Opportunity Act (ECOA), Fair Credit Reporting Act (FCRA), and other consumer protection regulations (Federal Trade Commission [FTC], 2022; U.S. Consumer Financial Protection Bureau [CFPB], 2023). Additionally, the paper explores the implications of algorithmic bias in AI-driven credit decisions, highlighting the potential for disparate impacts on marginalized communities (Bartlett et al., 2022; Bragg, 2021; Zarsky, 2016). The ethical concerns surrounding transparency, explain ability, and consumer rights are also discussed (Kroll et al., 2017; Pasquale, 2020). A comparative analysis of current regulatory approaches and proposed reforms is conducted, with a focus on mitigating bias and ensuring equitable access to credit. This research concludes with recommendations for policymakers, regulators, and financial technology firms to foster responsible AI deployment in BNPL services while safeguarding consumer protection and financial inclusion.