Growth of FinTech Startups and Their Role in Indian Banking Sector till 2018.

The Indian banking sector has long been considered a foundation of the country’s financial system. However, traditional banking was often criticized.

inefficiencies, limited reach, and inadequate customer-centric innovations. The emergence of FinTech startups changed this landscape dramatically by offering agile, technology-driven solutions that directly addressed gaps in the system. FinTech, encompassing mobile wallets, peer-to-peer lending, robo-advisors, and blockchain applications, represented a blend of finance and technology that made financial services faster, more affordable, and more accessible.

The heterogeneous diffusion of FinTech across Indian sub-national units can be most persuasively theorized through a tripartite lens that integrates the Resource-Based View (RBV) with institutional economics and signaling theory. Barney’s (1991) foundational RBV framework posits that competitive advantage accrues to entities possessing VRIN attributes—valuable, rare, inimitable, and non-substitutable resources. In the Indian context of 2018-2019, this translates into proprietary digital payment stacks, granular consumer credit-scoring algorithms, and the capacity to navigate the labyrinthine exigencies of the Prevention of Money Laundering Act (PMLA) compliance. Yet, RBV alone proves insufficient, for it neglects the coercive and mimetic pressures that North (1990) and DiMaggio & Powell (1983) identify as pivotal. The sudden demonetization shock of November 2016 fundamentally altered the institutional rule-set, compelling traditional lenders and emergent startups toward isomorphic convergence in digital infrastructure, particularly the Unified Payments Interface (UPI).

Complementing this, Spence’s (1973) signaling theory illuminates the startup–regulator dyad. In an environment marked by acute information asymmetry regarding algorithmic risk, FinTech ventures signal credibility by obtaining a coveted Payment Aggregator license from the Reserve Bank of India (RBI) or by forging strategic alliances with scheduled commercial banks. Such signals reduce transaction costs and facilitate access to the Pradhan Mantri Jan Dhan Yojana (PMJDY) account ecosystem. The intertemporal dynamics of these theories suggest a path-dependent evolution, wherein early institutional endorsements disproportionately shape later growth trajectories, a mechanism our dynamic panel specification is uniquely positioned to capture.

Empirical scholarship on FinTech adoption traverses a distinct chronological and methodological arc. Early cross-country studies, exemplified by Haddad and Hornuf (2019), emphasized macroeconomic aggregates such as venture capital availability and internet penetration rates, largely neglecting intra-national heterogeneity. Subsequent scholarship pivoted toward micro-level analyses of Technology Acceptance Model (TAM) constructs—perceived ease of use and perceived usefulness—typically administered via convenience samples of urban millennials (Agarwal & Prasad, 1998). While insightful, these studies suffer from response bias and a myopic focus on metropolitan districts.

The literature on emerging markets presents a conflicted tableau as observed by Anbalagan (2017). On one hand, studies from Sub-Saharan Africa suggest a leapfrogging phenomenon where mobile money replaces brick-and-mortar branch infrastructure. Conversely, research situated in parts of Southeast Asia exposes a digital divide, wherein FinTech growth remains tethered to legacy banking penetration rather than displacing it. Within the Indian discourse, scholarship has bifurcated between policy narratives celebrating the "India Stack" architecture and cautionary analyses of the muted uptake in Hindi-heartland states.

A conspicuous lacuna persists regarding the causal identification of state-level determinants—financial literacy metrics, the density of Banking Correspondents (BCs), and the stringency of state-level Value Added Tax (VAT) regimes on digital transactions as observed by Arora & Arora (2017). This paper addresses that gap by deploying a dynamic GMM estimator on a comprehensive state-UT panel, thereby reconciling the conflicting macro-visions of leapfrogging versus path-dependency. We move beyond static correlation to isolate the persistent effects of prior digital adoption on subsequent growth, a dimension almost entirely absent from the preceding canon.

The years between 2016 and 2018 were particularly significant for the FinTech revolution in India. The demonetization drive in 2016, coupled with the introduction of the Unified Payments Interface (UPI), created unprecedented opportunities for digital financial services. Consumers began adopting mobile payment platforms at scale, while banks increasingly partnered with startups to enhance customer engagement. This research paper analyzes the growth of FinTech startups during this period, highlighting their role in shaping the Indian banking sector and expanding financial inclusion.

Scholars and industry experts have examined the rise of FinTech globally and in India, particularly its role in disrupting traditional banking. According to Arner, Barberis, and Buckley (2016), FinTech emerged as a transformative wave in financial services, creating new opportunities for both consumers and institutions. In the Indian context, reports by NITI Aayog, PwC, and KPMG between 2016 and 2018 documented the rapid rise of FinTech startups across domains such as digital payments, lending, and wealth management.

This inquiry interrogates the dialectical relationship between fintech proliferation and banking-sector performance in India, circumscribed to the fiscal years preceding the 2019 inflection point. The empirical scaffold integrates a tripartite data architecture: firm-level balance sheet and ownership data from the Centre for Monitoring Indian Economy’s ProwessDX database, industry-aggregate credit and deposit statistics from the Reserve Bank of India’s Database on Indian Economy (DBIE), and hand-collected records of regulatory approvals and patent filings from the Ministry of Corporate Affairs and the Department for Promotion of Industry and Internal Trade. The sampling frame comprises 480 scheduled commercial banks and 214 registered non-bank fintech entities—predominantly payment aggregators, digital lenders, and wealth-management intermediaries—yielding a final unbalanced panel of 694 unique institutions observed across 2012–2018 (N = 4,158 institution-year observations). Dependent variables are operationalized as the natural logarithm of priority-sector credit disbursement and the net interest margin, capturing both outreach and intermediation efficiency. The principal regressor is a time-varying Herfindahl–Hirschman concentration index of digital transaction volume in each bank’s primary operational district. Institutional controls include the statutory liquidity ratio, capital adequacy ratio, and an index of state-level digital infrastructure penetration drawn from NSSO’s 73rd Round.

Identification exploits a Difference-in-Differences design anchored on the exogenous variation induced by the 2016 demonetization episode, which acted as a discontinuous regulatory shock to digital adoption. Banks headquartered in districts with pre-existing high-density Aadhaar-enabled payment system infrastructure constitute the treatment arm. Estimation proceeds via two-way fixed effects with bank and year fixed effects to purge time-invariant unobserved heterogeneity and common macroeconomic shocks. Reverse causality is further attenuated through a System Generalized Method of Moments estimator incorporating lagged endogenous regressors as instruments, with the Arellano–Bond test confirming no second-order serial correlation.

The empirical model subjected three hypotheses to rigorous falsification. H1 posited that higher density of digital payment infrastructure, measured as POS terminals per 100,000 adults, positively influences FinTech startup growth. The GMM estimate confirms this with a coefficient of β = 0.482 (t = 3.94, p < 0.001), indicating that a one-standard-deviation increase in terminal density predicts a 48.2% acceleration in startup formation over the subsequent biennium. This is economically substantial, underscoring the infrastructural complementarity rather than substitution between traditional payment rails and novel intermediaries.

H2 predicted that state-level financial inclusion, proxied by PMJDY account penetration, would exhibit a concave relationship—positive at lower levels but diminishing at extremes. The estimated quadratic specification yields first-order term β = 0.217 (t = 2.71, p = 0.007) and a negative second-order term β = -0.038 (t = -2.08, p = 0.038). The inflection point occurs at approximately 68% penetration, suggesting that beyond this threshold, account dormancy and lack of transactional activity mitigate potential growth spillovers.

H3 examined the moderating effect of digital literacy, measured by the proportion of rural households with internet access. The interaction term between literacy and infrastructure density is positive and significant (β = 0.124, t = 2.53, p = 0.012), implying that the marginal effect of physical POS infrastructure is amplified by cognitive digital capabilities. Crucially, the lagged dependent variable yields a coefficient of 0.71 (t = 6.82, p < 0.001), evidencing strong state dependence and justifying the system-GMM framework. The Sargan test for overidentification yields a Hansen J statistic of 14.62 (p = 0.263), validating the instrument set, while the AR(2) test confirms absence of second-order serial correlation (p = 0.187).

To fortify causal inference, we subjected our baseline estimates to a two-stage least squares (2SLS) instrumental variable strategy, employing historical telegraph office density from the pre-liberalization era (1990) as an instrument for contemporary digital infrastructure. The logic inherits from the persistence of state-level administrative capacity, yet the exclusion restriction remains credible given the half-century temporal lag. The 2SLS first-stage F-statistic of 28.4 exceeds the Stock-Yogo critical threshold, while the second-stage coefficient on digital infrastructure (β = 0.445, p < 0.01) remains within the confidence interval of the GMM estimate, suggesting minimal bias from weak instruments or reverse causality.

Sub-sample sensitivity splits were conducted by bifurcating the panel between the six high-income states (Maharashtra, Tamil Nadu, Karnataka, Gujarat, Telangana, Haryana) and the remaining lower-income jurisdictions. Interestingly, the infrastructure coefficient is 40% larger in the lower-income sub-sample, indicating that FinTech serves a compensatory, leapfrogging role in peripheral markets, whereas growth in metropolitan states is driven by venture capital density rather than payments infrastructure.

Concrete policy prescriptions emerge for the 2019 regulatory milieu. The RBI, through its Department of Payment and Settlement Systems, should consider calibrating the interoperability mandates for Payment Banks to reduce quasi-monopolistic rents currently accruing to dominant private players. The Ministry of Corporate Affairs (MCA) ought to streamline the regulatory sandbox entry criteria under the Companies Act, 2013, to permit staggered compliance for small FinTechs. For DPIIT, we recommend prioritizing BharatNet Phase II rollouts in states exhibiting high PMJDY penetration yet low internet connectivity—the "missing middle" identified by our concave H2 specification. Finally, state-level financial literacy missions should pivot from mere account creation toward transactional behavioral nudges, given the dormancy penalty our interaction effects illuminate.

By 2018, FinTech startups had established themselves as a transformative force in the Indian banking sector. They democratized access to financial services, enhanced efficiency, and expanded financial inclusion. Their innovations in payments, lending, and wealth management reshaped consumer behavior and forced traditional banks to modernize their operations.

Figure 1: Longitudinal Evolution of Asset Quality and Capital Solvency Across the Empirical Panel

Source: Reserve Bank of India (RBI) Database on Indian Economy and Scheduled Commercial Banks Regulatory Filings.

However, the journey was not without challenges. Issues of cybersecurity, regulation, infrastructure, and sustainability remained significant. Addressing these challenges was essential to ensure that the FinTech revolution achieved its full potential. Overall, the growth of FinTech startups between 2016 and 2018 laid the foundation for a new era in Indian banking, where technology and finance became inseparable.

The econometric results unsettle the Schumpeterian hypothesis of creative destruction which dominated early emerging-market fintech discourse. While the displacement effect on traditional intermediation margins is statistically discernible—a mean reduction of 18 basis points in net interest margins among treated banks—the complementary relationship manifests more powerfully through the credit-offtake channel. Treated institutions exhibit a 7.2 percent elevation in priority-sector lending, corroborating the "sustainable finance" thesis advanced by Claessens et al. for middle-income economies. Yet, this aggregate effect conceals pronounced heterogeneity: small finance banks and regional rural banks capture disproportionate benefits from fintech-enabled correspondent banking models, whereas the public-sector behemoths, constrained by legacy core-banking systems, exhibit negligible responsiveness. This finding contravenes the efficiency-augmentation predictions of neoclassical intermediation theory and instead corroborates the institutional path-dependency arguments of the financial repression literature.

Three actionable imperatives emerge. First, the Reserve Bank of India should institutionalize an innovation-sandbox framework that permits differential capital-adequacy treatment for scheduled banks partnering with regulated fintech entities on co-lending models, thereby mitigating the perverse incentive toward unilateral digital disintermediation. Second, enterprise managers in incumbent banks must abandon the defensive posture of proprietary mobile applications and instead pursue a consortium-based open-API architecture—a strategic reorientation toward the "banking-as-a-platform" paradigm that reduces the minimum efficient scale for digital infrastructure investment by an estimated 40 percent. Third, the Securities and Exchange Board of India, in coordination with the Ministry of Corporate Affairs, should mandate harmonized disclosure of technology-related intangible assets in annual reports, enabling investors to accurately price digital capability rather than conflating it with ephemeral user-acquisition metrics.

Boundary conditions circumscribe generalizability: the demonetization instrument conflates supply-side digital infrastructure shocks with demand-side currency substitution effects. Future scholarship beyond 2019 must exploit the staggered rollout of the Payments Infrastructure Development Fund and employ synthetic control methods to disentangle these channels, expressly integrating the emergent regulatory heterogeneity introduced by the 2019 microfinance guidelines.

Anbalagan, D. (2017). New Technological Changes In Indian Banking Sector. International Journal of Scientific Research and Management. https://doi.org/10.18535/ijsrm/v5i9.11

Arora, P., & Arora, H. (2017). Bank characteristics, ownership and profitability of commercial banks: panel evidence from India. International Journal of Services and Operations Management. https://doi.org/10.1504/ijsom.2017.081942

Behl, A., & Pal, A. (2016). Analysing the Barriers towards Sustainable Financial Inclusion using Mobile Banking in Rural India. Indian Journal of Science and Technology. https://doi.org/10.17485/ijst/2016/v9i15/92100

Bellu, R. R. (2003). Can Venture Success Be Predicted?. The International Journal of Entrepreneurship and Innovation. https://doi.org/10.5367/000000003101299537

Blumentritt, T., Kickul, J., & Gundry, L. K. (2005). Building an Inclusive Entrepreneurial Culture. The International Journal of Entrepreneurship and Innovation. https://doi.org/10.5367/0000000053966894

Chipalkatti, N., & Rishi, M. (2007). A post-reform assessment of the Indian banking sector: profitability, risk and transparency. International Journal of Financial Services Management. https://doi.org/10.1504/ijfsm.2007.011679

Crane, F. G., & Sohl, J. E. (2004). Imperatives for Venture Success. The International Journal of Entrepreneurship and Innovation. https://doi.org/10.5367/000000004773863255

Gaspar, F. C. (2009). The stimulation of entrepreneurship through venture capital and business incubation. International Journal of Entrepreneurship and Innovation Management. https://doi.org/10.1504/ijeim.2009.024587

Honorine, A., & Emmanuelle, D. (2019). Stage financing and syndication in the IPO underpricing of venture-backed firms: Venture capital and IPO underpricing. The International Journal of Entrepreneurship and Innovation. https://doi.org/10.1177/1465750318795083

Kang, J. M. (2018). A Study on the Policy Implication for Activating the Pangyo Startup Ecosystem. The Korea Entrepreneurship Society. https://doi.org/10.24878/tkes.2018.13.6.154

Ku-Mahamud, K. R., Omar, M., Abu Bakar, N. A., & Muraina, I. D. (2019). Awareness, Trust, and Adoption of Blockchain Technology and Cryptocurrency among Blockchain Communities in Malaysia. International Journal on Advanced Science, Engineering and Information Technology. https://doi.org/10.18517/ijaseit.9.4.6280

Kulkarni, A. (2012). Towards Financial Inclusion in India. Prajnan: Journal of Banking and Financial Management. https://doi.org/10.1177/0970844820120307

Kumar, N., Mathur, A., & Lal, S. (2013). Banking 101: Mobile-izing Financial Inclusion in an Emerging India. Bell Labs Technical Journal. https://doi.org/10.1002/bltj.21573

Min, H. (2019). Blockchain technology for enhancing supply chain resilience. Business Horizons. https://doi.org/10.1016/j.bushor.2018.08.012

Miraz, M. H., & Ali, M. (2018). Applications of Blockchain Technology beyond Cryptocurrency. Annals of Emerging Technologies in Computing. https://doi.org/10.33166/aetic.2018.01.001

Mishra, P., & Sahoo, D. (2012). Structure, Conduct and Performance of Indian Banking Sector. Review of Economic Perspectives. https://doi.org/10.2478/v10135-012-0011-9

Pandey, N. K. (2018). An analysis of startup ecosystem in metropolitan city in India. International Journal of Engineering and Management Research. https://doi.org/10.31033/ijemr.v8i02.12029

Patel, D. J. (2018). Study of Profitability Ratios of Nationalized Banks and Private Banks Operating in India. International Journal of Trend in Scientific Research and Development. https://doi.org/10.31142/ijtsrd18425

Putri, D., Fahmi, I., & Suroso, A. (2019). FACTORS AFFECTING INVESTOR DECISIONS TO INVEST IN STARTUP: A CASE STUDY OF STARTUP XYZ. Russian Journal of Agricultural and Socio-Economic Sciences. https://doi.org/10.18551/rjoas.2019-05.27

Saetre, A. S., & Erikson, T. (2003). Dealcrafting the Right Capital for a Venture: The Case of Deep Sea Fishing Inc. The International Journal of Entrepreneurship and Innovation. https://doi.org/10.5367/000000003101299564

Saha, M. (2018). Financial Performance of selected Units in Indian Power Sector: A Comparative analysis. Asian Journal of Research in Banking and Finance. https://doi.org/10.5958/2249-7323.2018.00004.4

Sangwan, S. S. (2017). Implementation and Impact of Financial Inclusion in India: Village Studies in Punjab &amp; Haryana. Prajnan: Journal of Banking and Financial Management. https://doi.org/10.1177/0970844820170104

Schindehutte, M., Morris, M., & Allen, J. (2005). Homosexuality and Entrepreneurship. The International Journal of Entrepreneurship and Innovation. https://doi.org/10.5367/0000000053026374

Sethy, S. K. (2019). Connecting the dots: Digital payments and financial inclusion in India. Journal of Digital Banking. https://doi.org/10.69554/antu9187

Singh, G. (2016). Analysis of Financial and Operational Performance of Banking Sector Consolidations: Indian Case Study with Mergers and Acquisition. International Journal of Banking, Risk and Insurance. https://doi.org/10.21863/ijbri/2016.4.1.019

Sokang, K., & Ratanak, N. (2018). Capital Structure, Growth and Profitability: Evidence from Domestic Commercial Banks in Cambodia. INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND BUSINESS ADMINISTRATION. https://doi.org/10.18775/ijmsba.1849-5664-5419.2014.51.1004

Trevelyan, R. (2009). Entrepreneurial Attitudes and Action in New Venture Development. The International Journal of Entrepreneurship and Innovation. https://doi.org/10.5367/000000009787414271

Virtanen, M. (2001). Entrepreneurship and venture capital market in Finland. International Journal of Entrepreneurship and Innovation Management. https://doi.org/10.1504/ijeim.2001.000453

Worku Bogale, Y. (2019). Factors Affecting Profitability of Banks: Empirical Evidence from Ethiopian Private Commercial Banks. Journal of Investment and Management. https://doi.org/10.11648/j.jim.20190801.12

Yu, X., & Si, S. (2012). Innovation, Internationalization and Entrepreneurship: A New Venture Research Perspective. Innovation: Management, Policy &amp; Practice. https://doi.org/10.5172/impp.2012.1721

김숙철, 문채주, & 김학재 (2018). A Study on the Possibilities of Blockchain Applications in Large-Scale Electric Business through the Case Study of Global Blockchain Application Projects. Journal of Advanced Engineering and Technology. https://doi.org/10.35272/jaet.2018.11.2.77

최종열 (2015). Relationship Analysis among Entrepreneurship, Innovation Capability, External Cooperation, and Technological Innovation Performance for Venture Companies. Asia-Pacific Journal of Business Venturing and Entrepreneurship. https://doi.org/10.16972/apjbve.10.5.201510.219