Skip to content
#federated learning Review Open access

Privacy in India’s digital credit ecosystem: a systematic literature review and policy directions

Oct 2026 · Cogent Business & Management · 67 references
FinTech, Crowdfunding, Digital Finance

Abstract

This study examines India’s digital credit ecosystem, exploring the relationship between technological efficiency and user rights, particularly data privacy. It aims to identify emerging trends, key concerns, and potential regulatory and technological interventions. The PRISMA framework guided a systematic review of scholarly literature on digital credit services, data privacy, and global benchmarks. A filtered collection of 198 documents formed the final corpus for analysis. Through bibliometric analysis (inductive approach), we identified dominant research themes, while content analysis (deductive approach) classified the literature by keyword frequency related to data collection, storage, and breach impacts. The study highlights three key research themes: (1) India’s rapidly expanding digital credit ecosystem, (2) emerging financial data privacy developments, and (3) international approaches to privacy in financial services. India’s digital lending is experiencing exponential growth, driven by FinTech innovations and data integration. However, concerns persist regarding data accuracy, transparency, and privacy. Emerging technologies such as Blockchain and Federated Learning offer promising solutions to mitigate privacy risks and enhance compliance. The findings provide context-specific directions for strengthening privacy governance within India’s digital credit ecosystem. Specifically, they offer practical guidance for regulators, policymakers, financial institutions, and FinTech firms seeking to promote responsible digital lending and sustainable financial inclusion.

Read PDF

Similar papers

#machine learning Review Open access Oct 2014

Software development in startup companies: A systematic mapping study

The results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.

Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al. · 394 citations · ⚡54
#machine learning Review Open access Jun 2014

Why Early-Stage Software Startups Fail: A Behavioral Framework

This state-of-practice investigation was performed using a literature review followed by a multiple-case study approach and presents how inconsistency between managerial strategies and execution can lead to failure by means of a behavioral framework.

Carmine Giardino, Xiaofeng Wang, P. Abrahamsson · 175 citations · ⚡19
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#machine learning Review Open access May 2016

Key Challenges in Software Startups Across Life Cycle Stages

It is found that what perceived as biggest challenges by software startups do vary across different life cycle stages, even though its significance decreases when the learning focuses of the startups move from problem to solution and their products mature.

Xiaofeng Wang, Henry Edison, Sohaib Shahid Bajwa et al. · 62 citations · ⚡6

Related blog posts

Microsoft Research Blog Oct 12, 2026

Simulating student learning for better AI tutoring

StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.