Indifi FinTech loan application records (India, restricted access)

Confidential single-counterparty data: not exercised here

This is confidential data from a single FinTech lender (Indifi Technologies). It is not for sale, not publicly downloadable, and not re-obtainable without the same agreement: it carries no provenance badge because there is no access path we can run here. Researchers obtained it under a confidential agreement with the lender. The page documents what the data is and the gotchas; treat it as unverified. This is the honest grade under the institute’s Verified discipline.

The Indifi data is the loan-level application record of one Indian FinTech lender: complete loan applications with applicant characteristics, the applicant’s payment-transaction history (digital payments flowing through the business), merged credit-bureau scores, and loan outcomes. The payment history is the distinguishing feature, because it lets the lender (and the researcher) see cashless-payment flows as a credit signal. A paper we distill uses it: Ghosh, Vallee & Zeng use the Indifi application data (316,719 complete applications, September 2015 to November 2022, with payment records, applicant characteristics, credit-bureau data, and outcomes) to study how cashless-payment data changes FinTech lending.

• Cost: not for sale. Confidential single-counterparty data.
• Source: Indifi Technologies (an Indian FinTech lender).
• Coverage: applicants to this one lender, not the Indian borrower population; loan-level applications and outcomes over the sample window.

Access (restricted)

• No public download and no resale. The data is the lender’s confidential application records; it cannot be purchased or redistributed.
• Through a confidential agreement with the lender. Access required a research agreement. There is no standing way for a third party to reach the same data.

Gotchas (the ones that bite pipelines)

These are the failure modes to expect; they are documented, not verified here.

• One lender is a selected applicant pool. Applicants self-select into this FinTech and into its product (small-business lending with payment data); they are not the Indian credit market. Do not generalize approval or default patterns.
• Outcomes are observed only for approved loans. Repayment is seen for funded loans, so default analysis conditions on approval; correcting for the approval decision requires the application stage. Model selection into funding.
• Payment history is platform-specific. The cashless-payment signal reflects flows the lender observes, which depend on the merchant’s payment setup; coverage of a borrower’s total turnover is partial. Do not read it as full revenue.
• Credit-bureau merge. Bureau (Cibil) scores are merged in and follow Indian bureau definitions and history-length rules; the merge has match error. Verify the join.
• Policy and platform changes over 2015 to 2022. Demonetization, the growth of digital payments, and the lender’s own model changes shift the data-generating process across the window. Control for regime and vintage.
• Not redistributable. Results can be reported but the microdata cannot be shared and cannot be re-pulled by others. Plan for non-reproducibility of the raw inputs.

Citation

Cite the source and arrangement, e.g.: Indifi loan-application records (Indifi Technologies, India), confidential; used under agreement, YYYY-MM-DD. State the sample window, the approved-versus-applied scope, and the credit-bureau merge.