Real-time alternative data for holisitic assessment of financial health of businesses that aren’t tax compliant
Small businesses have always faced constraint of capital when it comes to scaling up their business. It is no secret that lenders have been facing challenges in underwriting loans for small businesses for decades because there’s hardly any reliable data to estimate the true health of their business. Furthermore, the benchmark set by banks to estimate borrower’s income in the moderate-income group is too high.Though the owners transact in low value, high volume business, they have limited access to lumpsum money and usually resort to private lenders to scale their business. Just as informal lenders depend on intuition to judge the intent and ability to repay because they have access to the borrower’s circle of influence, so were banks stuck for a large enough time with old age ways of underwriting a borrower before the credit scoring became the norm. Even the traditional ways are becoming a passe.
The traditional approach followed by formal lenders to gauge the ability to pay typically relies on review of tax statements or income tax returns. This approach of estimating a prospective small borrower’s income suffers from major draw backs:
- Tax returns do not reflect the true of health of the business — income statement is mostly understated manifolds
- Date of filing of ITRs are outdated — Information on ITRs are usually 12–18 months old. This does not give lenders the most recent view of financial health of any business.
- No compulsion on quarterly financial statements — a small business unlike any medium-sized enterprise is under no compulsion to to prepare quarterly financial statements
- No compulsion on GST compliance — even recent reforms in taxation system with the onset of GST, nothing much changes for businesses with a turnover of less than Rs.20 lakhs
How do you underwrite loans for such businesses?
An alternate approach to underwrite loans in this segment is to follow a high touch point process of doing on ground estimation of borrower’s income. This is the approach followed by NBFCs and MFIs is not only time consuming but also highly prone to errors. It also leads high cost of underwriting that is eventually passed on to the borrower in the form of higher rates of lending.With rapid pace of evolution in technology, lenders too need to find out more efficient ways of underwriting beyond using tax returns and income statements. There are various emerging supply chain aggregators that can be potentially leveraged by lenders as data source for loan underwriting.Exemplifying the above statement, fin-tech players in real-time micro-payments and remittance services is a large industry where large market places have developed and the web has become a trading platform for such small businesses. These fin-tech companies are prepaid instruments (aka e-wallets) that convert real cash into digital money which facilitates small businesses for transacting with large enterprises such as telecom, railway, DTH and banks (especially for remittance). Therefore, they are a trove of business transaction data. This data is the most rewarding tool for lenders and can be used to assess flow of moderate-income borrowers based on which loans can be underwritten.
These benefits of this data counters the drawback of ITR based under-writing.
- Provides a holistic view of recent financial health of a small business & is potentially more accurate than tax returns
- The supply-chain aggregators are usually technology platforms that are equipped with sophisticated systems that maintain superior quality of data provided by the borrower
- Since the aggregators are technology platforms, the data transmission can be easily automated which further brings downs the cost of acquiring a borrower, the benefit of which in turn can be passed on by lowering the rate of interest of borrowing
Another source which is better evaluation than tax assessment is bank statement analysis that is also available in real-time. Bank statements capture detailed description of credit and debit transaction with much more granularity. We extract business turnover, loan repayment, utility bill payments, point of sales transaction, etc. All these are signals much more valuable than information which is based obtained from historical tax statements. These data points are available in digital format and is fairly easy to extract and analyse if the right technology and data model is build around it.