Corporate lending caught everyone’s attention at the beginning of the 21st century and then retail lending became the flavour of the season at the turn of the decade. Banks have primarily ignored micro & small businesses for credit. Now, country’s small and medium enterprises are attracting increased focus mainly from new-age fintech companies. India is leapfrogging a couple of generations in banking. It is due to high penetration and low cost of mobile phones driving the direct shift.
Retail lending has been strongly growing and it is attributed to credit bureaus. In addition to the information provided by credit bureaus, risks in unsecured retail lending have been significantly mitigated because of the social construct of pressure and education that helps in timely repayment.
According to the government, almost all households in India possess bank accounts now. It has asked state-owned lenders to open branches in or near every village. Until the first week of July 2018, 319.5 million bank accounts have opened under the financial inclusion plan, Pradhan Mantri Jan Dhan Yojana (PMJDY), and it has accumulated deposits close to Rs.80,000 crore. This means savings of Rs.2,500 per account, nearly twice the average savings (~Rs.1300) in 2014 when this plan was launched.
India is waking up to a new class of 30 crore borrowers
In most emerging markets such as India, a relatively small number of households and businesses borrow from banks. Most potential borrowers’ requests are too small (Rs.50,000-Rs.10,00,000) for traditional financial institutions. Traditional financial institutions are reluctant to go after it because low-ticket sized loans make it unviable to cover the operational expenses of setting up a branch in geographies with lower-income households. The high cost of origination that involves the cost of collecting and verifying information about them makes it even more challenging to target the emerging population of India.
These borrowers usually have no official credit history or formal financial footprint. Nearly 80% of Indians are dependent on micro & small businesses for their livelihood and they have continued to grow without any formal lending for years. Today, it’s an open secret that India’s little shops are a massive network in the making. Two reasons:
- Just as telecom, if banking has to reach the last mile, small businesses will be the linchpin of disseminating financial services.
- Just as sachet FMCG and telecom products disrupted the last mile with their sachet-sized products, similarly ‘sachetization’ and distribution of financial products via the same channel will create the desired wonders.
‘Sachetization’ of loans that small business borrowers demand from institutional lenders are valued at billions of dollars, if not trillions. This “credit gap” is sometimes filled by friends, family, and local moneylenders at a high cost. They offer amounts which may be inadequate, they place a massive social cost to borrowing, and they often charge onerous interest and security. More commonly, prospective borrowers avoid informal financing altogether, and deplete savings or forego the spending they planned.
The Credit Gap
Micro & small businesses with revenues of up to Rs 3 crore need credit of Rs 13 lakh crore. At under Rs 5 lakh crore, the supply of credit by banks to this segment is barely 36 per cent of total demand. For businesses with a revenue up to Rs. 30 lakhs, 74 per cent of the demand for credit is unmet.
Fin-tech startups in India are unlocking this 8.33 lakh crore market and out-compete informal lenders, but first, they need low-cost, reliable ways to assess and verify borrowers.
Both RBI and credit bureaus agree that alternative data is the need, especially in context of new-to-credit population. To improve credit assessment, better risk-pricing and strengthen supervision, RBI has proposed a Public Credit Registry that goes beyond the purview of existing credit bureaus.
Even credit bureaus aren’t sure whether alternative data and credit bureaus will co-exist. If they do, what will be the rules of interaction between the two.
Alternative data on the horizon
1. Shopping behaviour data:
Businesses and borrowers seeking small loans now begun to use services that capture and store their financial data. For instance, borrowers seeking small loans are increasingly accepting bank and electronic payments.
When a customer pays with a debit or credit card, or mobile wallet, a sale is recorded. Similarly, when a small business pays a supplier electronically, a record of the inventory expense is stored. Hundreds of electronic payment networks are amassing troves of sales and expense data for formerly financially-excluded customers and small businesses. Supplier networks and e-commerce sites are doing the same. If this data is shared after user’s consent with lenders, it could help underwriters assess the creditworthiness of borrowers hidden from credit bureaus. Of 800 million adults in India, only 400 million individuals were on credit bureaus till 2016.
However, this shopping behaviour data is not easily accessible to most lenders, nor credit bureaus are interested in it. Gradually, lenders in some markets are forming bilateral partnerships with data providers. But the slow, uncompetitive nature of the process limits the scale of this lending and excludes both smaller data sources and specialised lenders.
Is shopping data the right measure of risk?
We tend to judge repaying capacity by what is easily visible. Hence the market is inclined to rely on data from their cars, travels, and social media(e.g. Instagram, Facebook, etc). It is 21st century and we’ve easily copied from the west to fake it until we make it. However, it is the bank accounts and investments that are hidden.
In fact, wealth is what you don’t get to see. It’s the cars not purchased, the diamonds not bought, the clothes forgone and first class travel declined. It’s the assets in the bank that isn’t converted yet into the stuff you see.
Repaying capacity is not judged by what one has bought in the past, instead of whether one can repay today and the duration of the loan.
2. Bank statements are the first instrument of interaction with the formal financial system:
Credit scores are focused on consumer and micro-lending; it will continue to play a pivotal role in lending decisions. Incremental information on utilities, insurance, investments and telecom tell us more about sensible expense behaviour of an individual and such data can majorly improve lending in new-to-credit customers. Hence, analysing bank statements and establishing relationships with transactions becomes an impactful risk-mitigating factor for following reasons:
- Treasure trove of information: Bank accounts are a tool that allows citizens to interact with the financial system that has become one of the cornerstones of society previously hidden from the formal financial system. To effectively make use of the information found in bank account statements, an underwriter or credit evaluator should be able to dissect information, and by so doing discover and form relationships with the financial activities.
- Trail of transactions establish link: Bank account statement serves as a accounting record showing in detail the various transactions effected against the account. It establishes the link between source, destination, when it was received, and where it was stored or deposited. This can provide proof of activities such as monthly average balance, running loans, default behaviour, daily closing balance, recurring expenses and sources of income.
- Identify patterns in financial activity: In a modern society, it is difficult to move without an electronic trail. These trails relate to person’s personal or business dealings. Using these trails, patterns of business or human activity can be reconstructed through diligent and informed effort on the part of an investigator.
- Legal document: Analysis of bank accounts can be viewed as the utilisation of various techniques and skills to investigate the financial affairs and position of a prospective borrower. Above all, bank statement meets the legal definition of a document established by law.
Analytical methods and techniques are those methodologies that are applied to the collated transaction data to make it easier to establish behaviour financial activities in the account(s) under examination. Currently, most of the bankers or underwriters examine bank statements visually to try and identify transactions that are relevant to an investigation.
Even those who make use of bank statements, there is no standard method employed to ensure depth & consistency of findings.
- One of the possible reasons for this is that reading bank narrations could be intimidating because there is no standard library to refer and comprehend the abbreviations used by bankers.
- The other could be that they believe credit scores are a proxy to bank statements because these scores are also a result of data shared by banks.
The goal of refining a conceptual model for the analysis of bank account statements model consists of the following processes:
- Collating the Data
- Identifying Significant Payees and Depositors
- Bank Account Statement Summary
- Time Series Analysis
- Charting of expense and income behaviour
- Identifying Patterns and Unusual Activity
- Documenting the Findings
Large borrowers get a preference in credit decisions due to their history with credit bureaus. They have an established credit history, brand value, and supply of bank-identified collateral. In contrast, small and marginal aspirants, start-ups, new entrepreneurs, and small businesses in micro, small and medium enterprises (MSME) sector are disadvantaged as they lack many of those desired qualifications for credit.
Alternative sources of data would serve reputation as collateral for new-to credit borrowers. It would not only help inclusion but also reward good borrowers thereby imparting credit discipline.
Alternative data taps transactional data of borrowers including payments to utilities like power and telecom for retail customers and trade credit data for businesses. Regularity in making these payments is an indication of quality of credit to such customers.
There has never been a better time when combination of low-cost distribution, flow-based data models for underwriting and a trust factor in digital transactions offered a rare opportunity to synchronise, sachetize and specify loans that made informal lending so successful. Even the price-point is attractive enough to unshackle the tens of millions of small-businesses and moderate-income households towards building a robust financial health.
This post was originally published on Medium by Kumar Tanmay.