FinTech Lending and Cashless Payments: Ghosh, Vallee & Zeng (2026)

What this is. The paper’s core results, the signaling model it contributes, and the empirical specifications behind each finding: enough to know what it found and how, without reading all 49 pages. To replicate or extend it, read the full source at the original.

TL;DR

Using a dataset of 316,719 loan applications to Indifi, an Indian FinTech lender (2015-2022), the paper shows that borrowers who conduct more of their business through cashless payment technologies obtain better financing outcomes: higher loan approval rates, lower interest rates, and larger loan amounts. They also default less. These patterns are stronger for payment outflows than inflows, for information-intensive than information-light payment records, and for applicants with higher credit scores (an accuracy effect). A within-applicant specification with applicant fixed effects and an instrumental variable strategy exploiting the 2016 Indian Demonetization support a causal interpretation. The research question is motivated by the FinTech lending landscape surveyed in Berg, Fuster, and Puri (2022), and the result that digital footprints complement credit bureau data, documented in Berg et al. (2020), here confirmed for cashless payment records specifically. The authors rationalize the findings with a signaling model in which cashless payment records serve as “digital collateral”: bad borrowers face higher expected costs from posting their records (because discrepancies are more easily detected upon default), so only good borrowers endogenously choose cashless payments.

Core results

Magnitudes and significance are as reported; **/*** = 5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	A one-SD increase in cashless payment share raises loan approval probability by 2 percentage points (full controls)	Table II, col. (2), p. 1067	$\beta = 0.017^{***}$ (SE 0.001); baseline approval ~21%; represents ~8% of baseline
R2	A one-SD increase in cashless payments reduces offered interest rate by 44 bps (full controls)	Table II, col. (4), p. 1067	$\beta = -0.440^{***}$ (SE 0.024); mean rate 25.4%
R3	A one-SD increase in cashless payments raises offered loan amount by 13% (log)	Table II, col. (6), p. 1067	$\beta = 0.134^{***}$ (SE 0.006)
R4	A one-SD increase in cashless payments reduces default probability by 2 pp (11% of baseline)	Table III, col. (2), p. 1069	$\beta = -0.023^{***}$ (SE 0.003); baseline default rate ~18% of matured loans
R5	Payment outflows have 2x larger effects on loan approval and amount than inflows; interest-rate effects are not significantly different by direction	Table V, Panel A, p. 1073	Outflow approval $\beta = 0.022^{***}$ vs inflow $\beta = 0.008^{***}$; outflow default $\beta = -0.021^{***}$ vs inflow $\beta = -0.009^{***}$
R6	Information-intensive cashless payments (individually identifiable counterparty) have larger effects than information-light payments	Table V, Panel B, p. 1073	Info-intensive approval $\beta = 0.050^{***}$ (vs Table II baseline $0.017^{***}$); info-intensive default $\beta = -0.017^{***}$
R7	Within-applicant (repeat borrowers, applicant FE): a firm-level increase in cashless payments raises approval probability; applicant fixed effects absorbed	Table VI, col. (2), p. 1075	$\beta = 0.009^{***}$ (SE 0.003); spending-only $\beta = 0.010^{***}$, info-intensive $\beta = 0.021^{***}$
R8	Cashless payment benefit is complementary, not substitute, with credit score (accuracy effect): interaction term positive and significant for approval and amount	Table VII, Panel A, p. 1077	Cashless $\times$ Cibil Score $\beta$ on approval $= 0.012^{***}$ (SE 0.001); on log amount $= 0.039^{***}$ (SE 0.005)
R9	2SLS IV estimate (Demonetization $\times$ currency chest instrument): a one-SD increase in cashless use raises approval by 12 pp (larger than OLS, consistent with attenuation bias)	Table VIII, col. (4), p. 1082	IV $\beta = 0.117^{*}$ (SE 0.064), second-stage col. (4); full-FE first-stage F-stat = 26.90 (col. 2); banking at chest branch in 2018-19 reduces cashless share by ~10 pp
R10	Online marketplace transactions (BigTech-style digital records) show similar effects to cashless payments and their effects are cumulative	Table IV, p. 1071	Marketplace approval $\beta = 0.061^{***}$ (SE 0.002); conditional on cashless share, marketplace $\beta$ remains $0.016^{***}$; default $\beta = -0.006^{***}$

Overall (paper’s conclusion). The use of cashless payments provides an informational synergy for FinTech lending that operates through a selection mechanism (payment technology choice signals creditworthiness) and an accuracy effect (the signal is most useful for otherwise good borrowers). This provides a rationale for the joint rise of cashless payments and FinTech lending, and for open banking policies that expand access to historical payment records. This complements the open banking and FinTech-bank competition analysis of He, Huang, and Zhou (2023) by showing the informational value of payment records.

Theory / model

The paper develops a three-date model of signaling via payment technology choice, building on Parlour, Rajan, and Zhu (2022). There is a continuum of risk-neutral entrepreneurs (fraction $\alpha$ good, $1-\alpha$ bad) and a competitive lender. Entrepreneurs operate a production technology $n$ times during $t=0$ to $t=1$ (the “production stage”). Output $y_i \in \{0, \theta\}$ with failure probability $\pi_j$ for type $j \in \{G, B\}$ (p. 1083).

At t=0, the entrepreneur chooses a payment method: cash (production outcomes $Y$ are unverifiable) or cashless (outcomes become verifiable payment records $X$). The cashless payment technology generates a record $x_i$ for each production outcome $y_i$ with informational precision $q \in [1/2, 1]$ (p. 1084, eq. 5):

$$\Pr(x_i = \theta \mid y_i = \theta) = \Pr(x_i = 0 \mid y_i = 0) = q$$ $$\Pr(x_i = 0 \mid y_i = \theta) = \Pr(x_i = \theta \mid y_i = 0) = 1 - q$$

If a borrower with cashless payment records defaults at t=2, she incurs a cost $C$ proportional to the discrepancies between production capacity $\theta$ and realized records $X$ (p. 1084, eq. 6):

$$C = \phi \sum_{i=1}^{n} (\theta - x_i), \quad \phi > 0$$

This cost $C$ rises with the number of cashless records $n$, failure probability $\pi_j$ (bad types have more deviations), and informational precision $q$. In equilibrium the lender offers two contracts: $r_{\text{cashless}}$ (for borrowers who committed to cashless at t=0) and $r_{\text{cash}}$ (for those who did not). The borrower type $j$‘s expected utility under each contract is (pp. 1085-1086, eqs. 7-8):

$$w_j(r_{\text{cashless}}, C \mid \pi_j) = (1 - \pi_j)(\theta - r_{\text{cashless}}) - \pi_j E_j[C]$$ $$w_j(r_{\text{cash}} \mid \pi_j) = (1 - \pi_j)(\theta - r_{\text{cash}})$$

Proposition 1 (p. 1086): A separating equilibrium with $r_{\text{cashless}} < r_{\text{cash}}$ exists when two conditions hold:

(i) The cost $\phi n \theta$ is intermediate so that good types prefer cashless but bad types do not (incentive compatibility, eq. 14):

$$\frac{\pi_B - \pi_G}{\pi_B (1 - \pi_G)(1 - q - \pi_B + 2q\pi_B)} \leq \phi n \theta \leq \frac{\pi_B - \pi_G}{(1 - \pi_B)\pi_G (1 - q - \pi_G + 2q\pi_G)}$$

(ii) The average borrower quality $(1 - \bar{\pi})$ is not so high that a pooling contract dominates (eq. 15):

$$1 - \bar{\pi} \leq \frac{1 - \pi_G}{1 + \phi n \theta \pi_G (1 - q - \pi_G + 2q\pi_G)}$$

The selection effect is stronger when $n$ (number of cashless records) is larger and when $q$ (informational precision) is higher, which the model maps directly to the empirical patterns: outflows have higher $q$ than inflows, and information-intensive records have higher $q$ than information-light records.

Proposition 2 (p. 1088, eq. 16): The accuracy effect: in the separating equilibrium, the benefit from cashless payments $(r_{\text{cash}} - r_{\text{cashless}})$ is larger for firms with lower relative default probability:

$$\frac{d(r_{\text{cash}} - r_{\text{cashless}})}{d(\pi_B - \pi_G)} > 0$$

This implies better entrepreneurs benefit more, consistent with the empirical complementarity between cashless payment usage and credit score (Table VII).

Method

The paper is primarily an applied empirical paper (applies-method), but also contributes a signaling model. The econometric approach uses four distinct estimators:

Cross-sectional OLS (baseline, panel-regression): The financing outcome specifications (eqs. 1 and 2) are cross-sectional OLS over the full applicant sample, with a comprehensive set of fixed effects to nonlinearly absorb selection on observable dimensions. Standard errors are clustered at the application-month level throughout.

Within-applicant panel regression (eq. 3, panel-regression): To address time-invariant unobserved heterogeneity, repeat borrowers are used with applicant fixed effects and application-year fixed effects, exploiting within-firm time-series variation in cashless payment usage.

Text classification (text-classification): Payment records from bank statements are classified into cash vs. cashless, and further into information-intensive vs. information-light, using text analysis of payment labels (Appendix A). This covers 75% of all payment records; unclassified records show no meaningful predictive power.

2SLS instrumental variables (instrumental-variables): The share of cashless payments is instrumented by the interaction of an indicator for banking at a currency chest branch and an indicator for the 2018-2019 period when Demonetization cash shortages were most severe (Table VIII). Currency chest branches had better access to new banknotes post-Demonetization, so their clients reverted to cash more than non-chest clients during 2018-2019, creating plausibly exogenous within-district variation in cashless payment use. The cross-district currency chest variation from the 2016 Demonetization used here as identification follows Chodorow-Reich et al. (2020), and the design is adapted to within-district variation in the spirit of Crouzet, Gupta, and Mezzanotti (2023), who also leverage the same Demonetization variation.

Empirical specifications

Baseline financing outcomes (R1-R3, R5-R6, R8, R10)

Specification (1), p. 1065:

$$\text{FinancingOutcome}_i = \beta \cdot \text{CashlessShare}_i + \gamma X_i + \sum_k \theta_{F_k(i)} + \epsilon_i$$

• $\text{FinancingOutcome}_i$ is (i) an indicator for loan approval, (ii) offered interest rate, or (iii) log offered loan amount.
• $\text{CashlessShare}_i$ is the standardized amount-weighted share of transactions in cashless technology over six months prior to application (average of inflow and outflow shares).
• $X_i$ includes log number of payments, credit history length, business vintage, log owner age, missing credit score indicator, and top-up loan indicator.
• Fixed effects $F_k(i)$ include industry (67), application month, Cibil score group (10-point bands), 3-digit zip code, and revenue decile.
• Sample: 311,942-314,538 observations (cols. 1-6 of Table II). Standard errors: clustered at application month level.

Loan default (R4)

Specification (2), p. 1068:

$$\text{Default}_i = \beta \cdot \text{CashlessShare}_i + \gamma X_i + \sum_k \theta_{F_k(i)} + \epsilon_i$$

• Same controls and fixed effects as specification (1).
• Sample: restricted to matured/outstanding loans as of November 2022 (41,227 observations, Table III cols. 1-4).
• Additional columns (3-4) add log offered interest rate and log disbursed amount as controls.
• Columns (5-8) use time-to-first-delinquency and time-to-full-repayment as dependent variables (OLS on the loans that reached the respective event; note repayment regression run internally by Indifi due to data-privacy regulation).

Complementarity with credit quality (R8)

Specification (4), p. 1075:

$$\text{FinancingOutcomes}_i = \beta_1 \cdot \text{CashlessShare}_i + \beta_2 \cdot \text{FirmQuality}_i + \beta_3 \cdot \text{CashlessShare}_i \times \text{FirmQuality}_i + \gamma X_i + \sum_k \theta_{F_k(i)} + \epsilon_i$$

• $\text{FirmQuality}$ proxied by (i) Cibil score (Table VII Panel A, N=277,323) and (ii) weekly outflow volatility constructed from payment records (Table VII Panel B, N=311,938).
• Full set of fixed effects including credit score band FE.

Instrumental variable (R9)

First stage of specification (Table VIII), p. 1082:

$$\text{CashlessShare}_i = \delta \cdot (\text{ChestBank}_i \times \text{Year2018-19}_i) + \lambda \cdot \text{ChestBank}_i + \gamma X_i + \sum_k \theta_{F_k(i)} + v_i$$

• Table VIII layout (p. 1082): cols. (1) and (2) report the first stage (year FE only, then full fixed effects); cols. (3), (4), and (5) report the second stage (approved loan indicator on instrumented cashless share).
• The effective F-statistic is reported only for the first-stage columns: 37.41 (col. 1) / 26.90 (col. 2), above Montiel Olea-Pflueger critical values.
• Column (5) additionally instruments the interaction of cashless share $\times$ Cibil Score.
• Sample: 316,407 (year FE) / 311,941 (full FE) observations.

Datasets used

Dataset	Role in paper	Wiki page
Indifi loan application data (proprietary)	Primary dataset: 316,719 complete applications with payment records, applicant characteristics, credit bureau data, and loan outcomes (September 2015 to November 2022)	No page yet
Indian Cibil (credit bureau) scores	Borrower credit score control variable; credit history length and past loans	No page yet
Indian Demonetization / currency chest branch designations	Identification of IV; Reserve Bank of India currency chest branch list	No page yet

Sample: 316,719 complete loan applications from micro and small businesses, with 152 million transactions; approximately half are repeat applicants traceable via unique applicant identifier. Loan outcomes available for 66,017 approved loans; default outcome for 41,123 matured loans as of November 2022.

When to read the full paper

Use the original if you are: studying the informational role of payment technology in credit markets; designing open banking policies (the model and discussion in Sections VI-VII give a formal rationale); working on FinTech or BigTech lending mechanisms; interested in the Indian Demonetization as a natural experiment for financial digitization; or building on the digital-collateral concept vs. traditional collateral. The Internet Appendix (Tables IA.I-IA.XIV) provides extensive robustness by sector, subperiod, clustering level, and specification.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 81(2), April 2026 (published online December 18, 2025). This distillation was extracted by an LLM on 2026-06-01 and is not human-verified or independently reproduced. The CC BY-NC 4.0 licence permits reproduction for non-commercial use with attribution; the verbatim PDF is not hosted in this batch.

Ghosh, Pulak, Boris Vallee, and Yao Zeng. “FinTech Lending and Cashless Payments.” The Journal of Finance 81, no. 2 (April 2026): 1053-1101. DOI: 10.1111/jofi.70003. (C) 2025 The Author(s). Licensed under CC BY-NC 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made. Non-commercial use only.