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Data beyond tax returns
8 Mar

Data Beyond Tax Returns: Time To Use Aggregator Platforms To Estimate SME’s Strength

Data beyond tax returns
Data beyond tax returns

For small businesses, the tax returns do not reflect a true picture of the business. The income on the statements is mostly understated manifold; hence the lender is unable to get a clear picture of the true financial position of the business. Lenders need to figure out more efficient ways of underwriting beyond using the tax returns. With a rapid pace of evolution in various industries, there are various emerging supply chain aggregators that can be potentially leveraged by lenders as data source for loan underwriting.

By Siddharth Mahanot

Small business owners have always faced a constraint in terms of capital to scale up their businesses. Promoters have limited access to own funds and hence have to resort to formal and informal loans to scale the business. Lenders or financiers are mostly still stuck to the age old ways of underwriting a small business borrower. Lenders typically try to judge both the intent and the ability to repay of a borrower. The ability of the lender to judge intent to pay has undergone a drastic transformation with the advent of credit bureaus and other contemporary tools like social media profiling, psychometric tests etc.

The traditional approach followed by lenders to gauge the ability to pay typically relies on review of tax statements or income tax returns. This approach of estimating a prospective borrower’s income suffers from certain draw backs, categorically when the underlying borrower is a small business. For small businesses firstly, the tax returns do not reflect a true picture of the business. The income on the statements is mostly understated manifold; hence the lender is unable to get a clear picture of the true financial position of the business.

Another major limitation in this approach are the dates of filing the income tax returns, which is once a year due to which the information on the tax return files are dated and mostly 12–18 months old. This hinders the lenders to take a view of the most recent financial health of any business. A small business unlike a large corporate has no compulsion on preparing quarterly financial statements.

For decades now, the lenders have been encountering challenges in underwriting loans for the small business segment as tax compliance is typically low. Furthermore, the benchmark set by banks to estimate a borrower’s income is too high for this segment. An alternate approach for underwriting loans to this segment is followed by NBFC lenders who follow a high touch point process of doing on ground income estimation to assess borrower income. This approach apart from being high touch point and time consuming is also very subjective and is prone to errors. This approach also leads to high cost of underwriting which eventually gets passed on to the borrower in the form of higher lending rates.

Lenders need to figure out more efficient ways of underwriting beyond using the tax returns. With a rapid pace of evolution in various industries, there are various emerging supply chain aggregators that can be potentially leveraged by lenders as data source for loan underwriting. Exemplifying the above statement, e-commerce is an industry where large market places have developed and the web has become a trading platform for many SMEs.

These marketplaces are nothing but aggregator platforms which facilitate business for in-numerous enterprises and therefore are a treasure trove of business transaction data. Such data can be the most rewarding tool for a lender and can be used to assess income flow of the SMEs or prospective borrowers, based on which, loans can be underwritten.

This approach has benefits countering the drawbacks of the income tax return based underwriting. It provides a view of the most recent financial position of a small business and is potentially more accurate than tax returns. Adding to the advantages is the fact that these supply chain aggregators are large corporates or technology platforms and are equipped with sophisticated ERP systems. This enables the aggregators to maintain a far superior quality of data as compared to any data provided directly by a borrower.

Also Read: 9 Common Tax Mistakes You Should Never Make

The process of data transmission can also be completely automated. Information obtained from such aggregators is far more valuable and provides a more holistic view of the business than tax returns which are fairly uni-dimensional. For instance, in addition to getting the most recent business turnover data, the lender can potentially get information like price trend, growth rates, defect rates, return ratio, buyer rating etc. for a business, which is much more valuable than just looking at tax data. This same method can be replicated across industries for instance income data for a cab owner could be sourced from a cab aggregator, for a grocery store from a FMCG distributor and for a hotel from OTAs etc.

Another source which is better that tax statement from evaluation perspective are bank statements. Bank statements have evolved significantly over the past few years from hand-written passbooks to much more detailed statements which capture a detailed description of each and every credit or debit transaction with much more granularity. Some of the data points which can easily be extracted from a bank statement are business turnover, loan repayment, tax payments, utility bill payments, salary payments etc.

All these signals are much more valuable than the information which is obtained from the tax statements. Bank statements are also available in real time and hence reflect the current health of the business much better than the historical tax returns. Along with bank data, the financiers should also turn towards other forms of data like Service tax and VAT returns, shipping and logistics data, utility bill payment data, data extract from point of sales software and enterprise ERP/ accounting system etc. The data in question is mostly available in digital format and is fairly easy to extract and analyze if the right technology and data model is built around it.

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