Automated Decision-Making in Indian Credit Scoring: Assessing Fairness, Transparency, and Privacy under the DPDP Act and RBI Guidelines

DOI

10.2991/978-2-38476-547-8_5How to use a DOI?

Keywords

Digital Personal Data Protection Act; Credit Scoring; Algorithmic Fairness; Automated Decision Making; Regulatory Compliance

Abstract

The pervasive adoption of Automated Decision Making (ADM) systems within India’s rapidly expanding credit ecosystem presents a complex regulatory challenge. While these algorithms accelerate financial inclusion and operational efficiency for lenders, their inherent opacity risks perpetuating systemic biases and undermining consumer trust. The critical tension lies in balancing the demands of high velocity financial services with the fundamental rights and protections afforded to the data principal.

This paper assesses the new governance landscape established by the Digital Personal Data Protection (DPDP) Act of 2023 against the sector-specific guidelines issued by the Reserve Bank of India (RBI) concerning digital lending and credit information companies. We focus specifically on three interconnected dimensions: algorithmic fairness, system transparency, and personal data privacy. The analysis reveals that while the DPDP Act establishes rigorous requirements for consent and data minimisation, its provisions may lack the necessary specificity to enforce effective algorithmic accountability in credit scoring. Unlike privacy-focused frameworks that address data collection, the Act may not fully address the imperative for explainability or the mitigation of proxy variables that lead to unfair lending outcomes, such as disparate impact based on protected characteristics. The traditional legal standard of necessity and purpose limitation struggles to govern high-risk ADM where the processing purpose evolves dynamically. This resulting black-box problem renders the data principal’s Right to Correction or Erasure practically moot when the input data’s precise contribution to a credit decision cannot be traced to the individual.

The research concludes that effective governance of Indian credit scoring requires regulatory harmonisation. This includes moving beyond data input controls to implementing verifiable output accountability standards for ADM systems, similar to global high-risk AI regimes. Establishing mandatory independent audits of algorithmic impact and bias, along with a clear Right to Explanation for adverse credit decisions, is vital for ensuring compliance and building a responsible, equitable financial future under the DPDP Act.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Pushkar Sejwal
AU  - Meenu Gupta
PY  - 2026
DA  - 2026/03/05
TI  - Automated Decision-Making in Indian Credit Scoring: Assessing Fairness, Transparency, and Privacy under the DPDP Act and RBI Guidelines
BT  - Proceedings of the International Conference on Socio Legal Intricacies of Artificial Intelligence (ICSLIAI 2026)
PB  - Atlantis Press
SP  - 33
EP  - 40
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-547-8_5
DO  - 10.2991/978-2-38476-547-8_5
ID  - Sejwal2026
ER  -