How to Improve the Chances of Getting an SME Loan Approved

Alternative credit scoring based on digital data provides lenders a more holistic view of a borrower's creditworthiness and associated risks for credit underwriting

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MSMEs form an important component of the economy and contribute significantly to GDP, exports, industrial output and employment generation. According to data published by CII, there are close to 63.4 million MSME units in the country that employ about 120 million people across various sectors. The MSME share to the total Gross Domestic Product (GDP) is about 37per cent and they contribute to 45per cent of India's exports

"Small and medium units together have a potential of taking USD 70 billion informal credit from banks", as per ET. MSMEs account for over USD 55 billion of lending currently, there is still a huge gap that can be addressed by financial institutions in the near term. Due to limited access to finances, India's Micro, Small, and Medium Enterprises (MSME) had their growth truncated in the past.

Typically, lenders want to extend loans to businesses, which have a sound financial position with capability as well as intention to pay back the debt along with interest in a timely fashion. Hence, if one is applying for an SME loan, the following factors increase the chances of approval:

∙ Duly filled in the detailed application form

∙ Stable business with experienced management

∙ Stable and good financials

∙ Healthy Banking transactions

∙ Good Credit Scores

However, lack of comprehensive credit score, cumbersome application process and extensive documentation has resulted in a financial exclusion for many of these SMEs. Furthermore, the situation was extrapolated by the fact that for most banks, MSMEs were not very favoured customers to lend to because of the perceived high-risk portfolio.

New age startups saw the existing gap in the market and developed a technology-driven solution aimed at helping India's MSMEs access easy credit. In the current situation, a substantial number of MSMEs are leaning on these platforms for credit. While the Fundamentals of lending haven't changed much, new age NBFCs evaluate MSMEs not only traditional methods but also on alternate data. Analytics, machine learning, and automation provide a strong foundation for the new-age credit underwriting models, thereby ensuring faster and improved access to credit for SMEs located even in the tier-3 and tier-4 cities of India.

Alternative credit scoring based on digital data provides lenders with a more holistic view of a borrower's creditworthiness and associated risks for credit underwriting. A lot of this alternate data could also be an outcome of SME's regular payment transactions be it statutory liabilities like tax, PF etc. or utility bills like electricity, phone etc. to assess their creditworthiness. These non-traditional data sources are then supported by a mix of conventional data points on the enterprise's financial performance such as sales, revenue, income tax returns, GST data, books of accounts, the latest cash flow statements and outstanding invoices, depending on the size of loan under consideration and overall Risk profile of the customer basis scoring done through basic data.

Hence, digital push coupled with GST implementation will lead MSMEs towards a better & organized set up, hence improving credit in terms of coverage as well as ease of access.