Assessing FinTech Adoption and Credit Risk in Indian Microfinance Institutions

Thesis Abstract

This study addresses the rapid rise of FinTech-enabled credit provisioning in Indian microfinance institutions (MFIs) and the concomitant implications for credit risk management, financial inclusion, and systemic resilience. The problem centers on limited empirical understanding of how FinTech adoption influences portfolio quality, default dynamics, and risk mitigation in MFIs operating within diverse regulatory and socio-economic contexts across India. The aim is to assess the relationship between FinTech adoption intensity and credit risk indicators in MFIs, and to identify mechanisms through which FinTech tools affect risk management practices and borrower outcomes. Specific objectives include (i) measuring the extent and heterogeneity of FinTech adoption across a representative sample of Indian MFIs; (ii) evaluating the impact of FinTech usage on key credit risk metrics (portfolio at risk, write-offs, and provisioning coverage); (iii) examining the mediating role of underwriting practices, digital credit history, and borrower profiling on risk outcomes; (iv) analyzing the moderating effects of regulatory compliance, governance, and operational scale on the FinTech–credit risk relationship; and (v) deriving policy and managerial implications for scalable, inclusive, and risk-aware digital lending in MFIs. The methodology adopts a concurrent mixed-methods design, integrating quantitative analysis with qualitative insights to capture both measurable risk outcomes and contextual process factors. The population comprises registered MFIs in India that have deployed FinTech-enabled lending platforms, microfinance lenders, and non-banking financial companies operating in microcredit segments. A stratified random sample of 120 MFIs is drawn to ensure representation across tier-1 and tier-2 cities, rural outreach, and varying funding sources. Within each MFI, 600 borrower accounts are analyzed for micro-level credit outcomes, yielding a borrower-level dataset of approximately 72,000 observations. Data collection instruments include structured financial statement templates, risk dashboards, and platform usage logs, complemented by semi-structured interviews with 20 senior risk officers, 12 regulators or policy advisors, and 10 FinTech product managers to capture governance, process changes, and perceived challenges. Secondary data on macroeconomic indicators, credit bureau signals, and regulatory events are integrated to enhance contextual interpretation. Analytical techniques comprise descriptive statistics to characterize FinTech adoption and risk profiles, panel data regression to estimate the impact of FinTech intensity on credit risk metrics while controlling for size, rurality, and client complexity, and propensity score matching to address selection bias between MFIs with varying levels of FinTech deployment. A multivariate regression framework will test hypotheses about the direct and indirect pathways linking technology-enabled underwriting, digital KYC/credit scoring, and automated collection processes to portfolio quality. Mediation analysis using the causal steps approach and Sobel tests will explore the roles of underwriting rigor and borrower profiling as mechanisms. Additionally, thematic analysis of interview transcripts will identify institutional, regulatory, and operational factors shaping FinTech adoption and risk management practices. Robustness checks will include alternative specifications, fixed-effects models, and difference-in-differences where natural experiments exist due to regulatory changes or platform rollouts. Expected findings include evidence that higher FinTech adoption correlates with improved provisioning adequacy and reduced portfolio at risk, particularly when accompanied by standardized digital underwriting and timely data sharing with credit bureaus. However, heterogeneous effects are anticipated across urban-rural contexts, institution size, and governance maturity, with potential short-term increases in detected risk due to enhanced data granularity. The study contributes to knowledge by integrating FinTech technology adoption into the credit risk management literature for MFIs, providing a context-specific framework that links digital lending tools with risk-adjusted performance. The theoretical contributions draw on Technology-Enabled Risk Management theory and the Resource-Based View to explain how digital capabilities translate into enhanced risk controls and sustainable reach. Policy implications include recommendations for standardized risk metrics, Augmented due Diligence for third-party platforms, and regulatory guidance on data interoperability and consumer protection. The conclusion emphasizes a balanced integration of FinTech capabilities with prudent risk governance to support scalable and inclusive microfinance in India. Practical recommendations target MFIs, FinTech providers, and regulators to harmonize innovation with robust credit risk discipline, improve borrower outcomes, and strengthen systemic resilience.