Financial Data Analytics Adoption in Indian MSMEs and SMEs: A Systematic Review, Theoretical Integration, and Future Research Agenda

Abstract

This paper presents a PRISMA-compliant systematic literature review of the adoption, implementation, and measurable impact of Financial Data Analytics (FDA) in India's Micro, Small, and Medium Enterprises (MSMEs) and Small and Medium Enterprises (SMEs). Drawing on 61 peer-reviewed studies published between 2010 and 2025, identified through searches of Scopus, Web of Science, and Google Scholar, the review synthesises findings across six thematic clusters: adoption drivers, adoption barriers, fintech and AI/ML integration, firm performance effects, regulatory context, and sectoral variation. The theoretical contribution is an integrated three-stage adoption model sequencing the Technology Acceptance Model and Diffusion of Innovation theory at the initiation stage, UTAUT and the Resource-Based View at the institutionalisation stage, and Dynamic Capabilities Theory at the capability-building stage. This sequencing resolves a persistent problem: the tendency to apply a single framework to a process that evolves significantly as firms mature analytically. Core findings: perceived usefulness driven by GST reconciliation pain points and affordable cloud delivery is the dominant predictor of initial adoption; the cost of skilled analysts — not software licensing — is the primary barrier to deeper capability development; fintech-enabled ML credit scoring is improving loan access for MSMEs with digital footprints, but field-trial evidence in Indian contexts remains absent; and manufacturing firms in industrial clusters show a 7-14% ROA premium among analytics adopters, though the performance literature is hampered by reverse-causality and non-standardised outcome measures. The Account Aggregator framework and GST data ecosystem represent institutional infrastructure changes that the reviewed literature has barely begun to theorise. Methodological gaps dominate: cross-sectional surveys, urban sampling bias, and concentration in four Indian states limit generalisability. Future priorities include longitudinal panel studies, AI/ML lending field trials, ESG analytics integration studies, and algorithmic fairness audits of fintech credit models serving MSME populations.