Highlights
- Most small businesses need small-ticket short-tenure loans.
- New datasets enable lenders to make easier underwriting decisions for cash-flow based lending.
- OCEN 4.0 brings together specialized data partners to serve lenders for underwriting short-term small-ticket loans.
Introduction
Over the recent few years, the credit assessment processes and policies of Lending institutions (Banks and NBFC’s) for MSME’s have evolved very efficiently with the availability of newer data sets. Traditionally credit underwriting was done mostly based on the Borrower’s credit bureau reports, Annual financial statements (P&L, Balance Sheet, etc) and verification of Bank Statements. The loan products were also designed with longer tenure repayments similar to consumer loan products.
Short Tenure lending works favourably for Business Loans as compared to Consumption loans
Most small businesses in the unorganised sector have credit requirements for a short tenure of 1-3 months and may not require a 12–24-month repayment tenure. Similarly, they usually need a small ticket loan 20-100k to meet immediate business requirements (viz. buy stocks, cattle fodder, fuel, seeds, raw material). This brings forth the requirement of small-ticket short-tenure loans for such borrowers which meet their credit demands and are comfortable to repay.
For example:
A Cab driver engaged on a Cab aggregator platform needs financing of Fuel (approx. 5-10k) for a week as his payment cycle on the platform is weekly as he is required to refuel the cab everyday to maximise his trips and increase his income. But he may need a 3 month EMI credit to finance his Annual Cab Insurance cost (approx. 15-20k) which is a high ticket one-time annual expense but is a mandatory requirement by the Cab aggregator platforms. In such a scenario, lenders can provide a loan of INR 15-20k, repayable over 12 weeks to facilitate the purchase of insurance for the cab.
A Cocoon (Sericulture) farmer is active for 9 out of 12 months of the year and needs funds to purchase Silkworms and Mulberry leaves. The production cycle of farmers from Worm to Cocoon is 21 days. Input required per acre is around 50-60k, whereas the output per acre is around 1.2-1.3 Lac. In such an underwriting scenario, a lender can look at financing around 30-40k per Acre of farm ownership to such farmers for a tenure of up to 30 days. They can also look at getting an escrow of sale proceeds from the purchasing weaving institutions / mills to ensure repayment of the loan amount.
A Sweet Shop’s owner’s credit underwriting may be assessed based on the quantity of Sugar (Cost Rs.40-50 per Kg) purchased during the last 3-6 months as this primary raw material along with other ingredients produce Sweets which are sold at Rs.400-800 per Kg after the value add. He may need credit to buy more raw material ahead of festivals and religious events due to high demand during such events which would again be a short 30–90 days credit requirement.
Such short tenure loans meet the borrower specific requirements as well as have better repayments aligned with the lenders goal of portfolio quality.
New Datasets make Cash-flow based lending easier
The unregistered small businesses need a sector or segment focused approach as the availability of sector / segment level data and industry information adds as a proxy for underwriting the business loan requirements of the borrowers in these segments. For example, a Cattle farmer in dairy industry, Cocoon farmer (Sericulturist) or a Yarn weaver in the silk industry, Fishermen, Gig economy workers (On demand Cab drivers, Hyperlocal delivery staff, Painters, Plumbers etc), Small local traders & businesses (barber shop, sweet shop, stationary shop etc.), each of these borrowers will have a different kind of loan requirement to meet short term credit needs.
With the ability to capture and utilize multiple real-time alternate data points and large volumes of information, new types of underwriting models have evolved giving way to smart business rule engines for faster and efficient credit decisioning Cash flow based lending to Small businesses.
The decisioning for such credit requirements should be based on a combination of below available datasets:
Assimilation of Derived structured data which would be available from the borrower agents of these borrowers (viz, Average monthly / weekly supply of milk to Dairies, Average weekly / monthly sale of produce to APMC’s, Data from Aggregator platforms regarding weekly / monthly billing, no of trips, no of orders executed etc)
Industry specific data which is related to the industry specific to the particular borrower (Average per day milk produce per cow, Per acre Chilli production in a certain region, Number of haircuts per day per seat in a salon etc, Average count of eggs produced per hen per month, Life expectancy of Cattle / Poultry, Average output of Fish farming per trip during different seasons, etc) along with prevailing market rates of goods and services being rendered. Industry specific data help to formulate credit products and policies specific to each Industry including the ones with seasonal and regional variations. For example, Fishermen go out of business in the monsoon and may default on the Loan EMI’s, but as soon as the monsoon vanishes, they will ride their boat in the ocean and their business is back to usual.
Personal Data in the form of No of Cabs / Vehicles for Drivers, Acre ownership for farmers, No of Cows/ Buffaloes owned, etc.
Formal Data as available in terms of banking, billing, escrow, credit bureau history if any of these borrowers
The loan agent’s knowledge about the MSME borrower’s operations is expected to help structure cash flow-based loans that are low-cost, collateral free and low-risk and draw meaningful insights out of unorganized data creating fresh opportunities for banks and NBFC’s to improve the credit-decisioning models that underpin their lending processes.
New Underwriting Models from Prediction Service Providers (Underwriting Modelers)
With the availability of Bank Statements and GST Data through the Account Aggregator (AA) protocol, Lenders have started to use the data for credit modelling across various parameters to provide business loans with much more accuracy and speed than earlier. Coupled with the Loan agent’s proprietary data about the borrowers, it has now become even easier to underwrite a borrower based on their Banking and GST data signifying their “Ability to pay” clubbed with credit bureau data signifying the “Intent to pay” - which have been the 2 major pillars of credit underwriting by lending institutions.
Multiple credit models will evolve for underwriting a “Borrower or a Transaction”, whether it is in the form of Invoice, Sales Or Purchase Order financing. With the AA revolution, real time GST and Banking data across multiple parameters is now instantaneously available and can be massaged and modelled to create underwriting models to finance Transaction level loans. For example, An invoice raised by a small GST registered business on another small GST registered business can now be financed using the Bureau + GST + Banking + Invoice data.
Multiple parameters across above data sources can be used to quickly underwrite an Invoice by building a scoring model around parameters across a combination of data sources.
Here is a Sample Underwriting Model for an Invoice financing product to get started:
OCEN 4.0 aims at bringing the Borrowers, Borrower Agents, Lenders, Derived Data Partners, Prediction Service Providers, Collection Agents, Escrow Agents on a single platform to facilitate the credit needs of short-term small-ticket loans for the underserved segment of borrowers who lack the access to credit.