Turns a renter's bank-paid rent into a record lenders can underwrite
TL;DR Leasemark reads a renter's own bank data, with consent, finds the monthly rent, confirms who received it and turns two or more years of on-time payments into a short report. Banks and NBFCs pay per report when judging young, thin-file applicants. It sells one strong signal rather than yet another credit score.
About 27% of urban Indians lived in rented homes at the 2011 Census, and NSSO data suggested only 5% of rental homes were let on a formal agreement[1]. Rent is often the biggest bill a young salaried person pays every month, yet it never shows up in a credit report because it isn't a loan.
A 24-year-old who has paid ₹18,000 on the 5th of every month for three years can look identical to a stranger with no history at all. Lenders then price that person as an unknown, cap the loan or decline it, which I think is plain unfair to the renter.
What it does, in regulatory terms: an underwriting input for regulated lenders, acting as their lending service provider. Leasemark does not lend, does not keep a credit bureau and does not issue a score of its own.
The renter applies for a loan with a partner lender and agrees to share bank data through the Account Aggregator network, where the lender is the regulated data user[2]. Leasemark's models pick out the recurring rent transfer among hundreds of transactions, check that the receiving account belongs to the named landlord and flag patterns that look staged, like rent going to a relative's account that sends the money back. The output is a one-page report - months paid, months late, amount trend and a confidence level.
I've reframed the seed. A general alternative-data score has to compete with large analytics firms that already sell to over a thousand institutions. Rent is a narrower, provable fact, and a lender can decide quickly whether it changes approvals.
The renter uses it free and the lender pays. Likely buyers are NBFCs and digital lenders that serve first-time salaried borrowers, at a per-report price that still needs testing (I found no public benchmark). For scale, a basic bank-account check costs about ₹3 per call[3], so the report has to earn a premium by changing decisions, not by fetching data.
The Account Aggregator rules now let banks, NBFCs, insurers and GSTN share data on consent, with purpose limits the user can revoke[4]. RBI's Digital Lending Directions of May 2025 spell out what a lending service provider may collect and store[5], and that gives a partner lender a clear compliance path for a product like this.
Nobody publishes a figure for India's rent-data or thin-file underwriting market. The firmer numbers are the renter base, roughly a quarter of urban residents in 2011[1], and evidence from the US that rent counts. one US rent-reporting firm reports an average rise of 53 points for enrolled renters[6], and a bureau's bill-data feature averages 13 points from bill data[7]. Treat those as signs that the signal matters, not as Indian demand.
One Indian analytics firm sells bank-statement and GST analysis to lenders and runs on the Account Aggregator rails[8], but shows no rent-specific product. US rent-reporting firms report on-time rent to all three bureaus[6]. One US bureau lets users add rent paid to approved property managers[7]. So the gap in India is a rent signal built from bank data, with landlord checks, that a lender can use without changing its credit policy.
| Difficulty | Moderate (45/100) |
| Starting capital | ₹60 L–1.2 Cr |
| Time to launch | 9–12 months |
The code isn't the hard part. You need a regulated lender willing to act as the data user, a model that catches staged rent and 12–18 months of loan outcomes before any credit team trusts the signal. And every lender's risk team will want its own back-test.
Yes, this is regulated, though Leasemark itself holds no lending licence. It works under its lender partners' rules.
Building this outside India? The categories above usually have a local equivalent, so check with your own regulator.
AI should never make the lending decision or reject a renter on its own. The lender decides, and every flag needs a human check.
Want to work out the AI layer for Leasemark? Talk to Yash →
I'd take a serious look if you've sold to NBFC credit teams and understand lending service provider rules. If you want a consumer brand then skip it. Prove one thing first - on a lender's past book, borrowers with two clean years of rent default less than similar borrowers without it.
Plan for roughly ₹60 L–1.2 Cr, much of it legal work, data security and the long pilot cycle with lenders.
Not if you work as a lender's service provider under the Digital Lending Directions and the Account Aggregator rules. Get a legal opinion on credit information law before selling.
Yes, and rent reporting firms already exist in the US. The data access and credit rules differ in every market, so start with the regulator.
The free Leasemark validation kit has the line-by-line capital breakdown, the effort scoring, the lending and data-protection checklist, a 14-day validation sprint with pass/fail targets, a 90-day launch plan, 10 interview questions for lenders and renters, and 5 assumptions to test. Download the kit →
Thinking about building Leasemark or something close to it? The make-or-break step is getting one lender to run a back-test on its own past loans. If you need a hand, you know where to find me. Talk to Yash →
For illustration only. Names are fictional and ideas hypothetical; sourced facts are real but may be out of date. Nothing here is legal, financial or professional advice, and Yash Khatri is not personally liable for any use of it. Read the full disclaimer.