Fintech — Legal Accountability for AI-Driven "Buy Now Pay Later" and Algorithmic Credit Scoring

From being an obscure checkout option, Buy Now, Pay Later (“BNPL”) is now one of the fastest-growing categories of unsecured consumer credit in India, with the domestic BNPL market valued at around USD 2.7 billion for 2024 and expected to grow at a compound annual growth rate of more than 34 per cent between 2022 and 2025. At every BNPL "no-big-deal" transaction, there is a credit-scoring algorithm that in mere seconds decides whether a consumer is worthy of credit, its terms and its price, and this decision typically amounts to the responsibility of a trained loan officer with statutory requirements of fair lending. This article explores how this risk is manifested in a lack of accountability when these far-reaching choices are pushed to black-box, self-learning models that are managed by lightly regulated fintech intermediaries. The Reserve Bank of India has taken baby steps, with the Guidelines on Digital Lending (2022), the Default Loss Guarantee Guidelines (2023), consolidated Digital Lending Directions, 2025 and most recently the report of the FREE-AI Committee (August 2025) all failing to directly tackle the specific legal liability issue: who is liable when the algorithmic credit-scoring model used in a BNPL loan denies credit due to discrimination or an incorrect score, what disclosure the consumer is entitled to, and how the algorithmic credit-scoring model is to be audited. Using a doctrinal and comparative approach, the article argues that the Indian stance is justified in the context of the European Union Second Consumer Credit Directive, the classification of creditworthiness AI as high-risk under the EU AI Act, and the high-risk classification of AI systems under the EU AI Act, as well as the evolving position of the United States CFPB following the withdrawal of its 2025 interpretive rule on BNPL. The article highlights three areas of research gaps classificatory ambiguity, algorithmic opacity and fragmentation in enforcement- and recommends a calibrated statutory and regulatory move for India's credit ecosystem: a mandatory regime of disclosure for adverse actions, periodic audits of algorithms, and a clear cut-out of liability between a regulated entity and its lending-service-provider partners.