THE REGULATORY AMBIGUITY OF ALGORITHMIC CREDIT SCORING IN INDIA
AUTHOR – TULASI RAJESWARI SAHOO, STUDENT AT KIIT SCHOOL OF LAW
BEST CITATION – TULASI RAJESWARI SAHOO, THE REGULATORY AMBIGUITY OF ALGORITHMIC CREDIT SCORING IN INDIA, INDIAN JOURNAL OF LEGAL REVIEW (IJLR), 6 (4) OF 2026, PG. 598-604, APIS – 3920 – 0001 & ISSN – 2583-2344.
Abstract
The early adoption of Automated Decision-Making (ADM) systems in the credit sphere of India has redefined the process of lending, making it more efficient, but at the same time, initiating fears of transparency, subjectivity, and responsibility. Digital lending and AI governance frameworks and the establishment of the Digital Personal Data Protection Act, 2023 (DPDP Act), including briefer and more transparent regulations, are important steps towards regulation and openness. In the present paper, the author critically assesses the claim that these developments – especially, the Significant Data Fiduciary (SDF) classification and new Explainable AI (XAI) requirements are effective in harmonizing the structural black box problem of algorithmic credit scoring.
The discussion has shown that even though SDF requirements like Data Protection Impact Assessment (DPIA), algorithmic auditing, and more robust compliance frameworks are a welcome change to create accountability, the issue of enforceability, explainability, and practical borrower empowerment still exists. The paper holds that the Indian regulatory framework is more input-oriented, with consent and data minimisation as its central points, which do not involve output accountability and fairness of algorithms.[1]
Keywords- Automated Decision Making, Algorithmic Credit Scoring, Digital Lending in India, Explainable Artificial Intelligence, Significant Data Fiduciary
[1] Startup Magazine. (2025). RBI releases final framework for AI-driven credit underwriting