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Information gaps in MSME lending

Savita Shankar | Updated on July 21, 2020 Published on July 21, 2020

Prompt support: Financing must grow at a rapid pace to meet MSMEs’ needs   -  Getty Images/iStockphoto

Availability of digital data and the entry of more lending entities are positives. But risk-assessment infrastructure must be improved

The Covid-19 crisis and the consequent lockdown have posed especially tough challenges for micro, small, and medium enterprises (MSMEs) in India, stemming from their limited financial resources. As a result, the government has announced several measures to help the country’s 63.4 million MSMEs due to their important place in the economy — they employ 110 million people and account for 29 per cent of GDP. The largest initiative announced was the provision of ₹3 trillion ($40 billion) in credit guarantees for the sanction of additional facilities to 4.5 million MSMEs, which currently avail themselves of facilities from banks and non-banking financial companies (NBFCs). While providing such a respite to MSME borrowers is important, it only helps the small proportion that have been able to access financing from banks and NBFCs.

A 2018 IFC-Intellecap study estimated the credit gap of Indian MSMEs at ₹25.8 trillion ($340 billion), implying that MSMEs’ financing needs to grow at a rapid pace in order to meet the sector’s needs. Given the importance of the sector from the employment generation viewpoint, investing in infrastructure to support MSME lending is important and is likely to reap rich returns in the future.

A major challenge in MSME lending arises due to the difficulties of carrying out credit appraisals of firms in the sector on account of their lack of credit history and reliable financial statements. Lenders need to invest considerable time and effort in making their assessments, resulting in high transaction costs. Some lenders prefer to lend on the basis of the mortgage of the entrepreneur’s personal property, a policy that results in exclusion of high-potential MSMEs that are unable to offer such security.

However, lenders are increasingly tapping into recent developments in digitisation that have resulted in the availability of new sources of information regarding MSMEs. The availability of digital public infrastructure such as the Goods and Services Tax (GST) data and India Stack, a set of open application programme interfaces, are especially useful for such lending. In addition, the Trade Receivables Discounting System or TReDs, an electronic platform for auctioning trade receivables, is also helpful for MSME lenders.

Digital data

While lending to this segment has traditionally been associated with higher cost to serve, the use of digital data could reduce costs significantly. Such data are also useful for MSME lenders who lend based on the enterprise’s cash flows due to the non-availability of reliable financial statements.

These developments have led to the emergence of digital financiers (often fintech start-ups) who provide online loans based on digital information and artificial intelligence. Yet another nascent trend is the emergence of online marketplaces where MSME lenders and borrowers can transact. Fintech start-ups (incorporated as NBFCs) contribute to the growing diversity of the MSME lending space. While at one point, MSME lending was dominated entirely by public sector banks — their share stood at 49.8 per cent as of December 2019 — with private banks and NBFCs accounting for the balance (data based on TransUnion CIBIL, April 2020).

The licensing of small finance banks in 2015 has brought in a new category of private banks with a special focus on the MSME sector. Many of these entities were microfinance organisations before receiving banking licences and are in the process of expanding their portfolio beyond microfinance loans. Several types of MSME-focussed NBFCs have also emerged. While formerly, most NBFCs focussed primarily on the truck-financing end of the market, newer NBFCs are exploring other areas such as the financing of specific textile clusters and agricultural value chains.

Default rates

The diversity of lenders in the segment is reflected in the stark variation in default rates on MSME loans based on lender categories. Between December 2017 and December 2019, the non-performing asset (NPA) rate on MSME loans of public sector banks varied between 16.6 per cent and 18.7 per cent, well above the range of NPA rates of private sector banks (which was between 3.5 per cent and 5 per cent). The NPA rates of NBFCs during this period ranged between 4.9 per cent and 7.6 per cent.

It is concerning that the lender category accounting for the majority share of MSME lending — namely public sector banks — had an MSME NPA rate of 18.7 per cent in December 2019, which was higher than that of the overall commercial lending NPA rate in India of 17.3 per cent (data based on TransUnion CIBIL, April 2020). These numbers indicate that there is a need to invest in infrastructure to support MSME lending in the country so that lenders are able to make more informed decisions. For instance, lenders and borrowers in the MSME space could benefit from a policy initiative providing incentives for lenders to pool the large amounts of data being generated at various lending institutions. A basic form of such pooling could be building up a store of financial statement data of MSMEs, enabling lenders to develop benchmarks for different types of MSMEs, many of which may be new to the formal financial system.

A more advanced form of pooling could collate financial performance, loan, and default data across lenders to enable the development of credit models that are likely to be more robust and superior in making predictions, as compared to lender-level models. An example of such an initiative is the “Credit Risk Database” in Japan, through which member banks share SME data relating to financial performance and default. The database was set up specifically to encourage bank lending to SMEs. The names of the customers are not shared to protect privacy, but entries relating to the same entity are clubbed by the use of algorithms.

Risk assessment

In the Indian context, it may be useful to set up a credit risk database that involves both banks and NBFCs, as the latter are also important players in the MSME lending space. The cost of setting up the model will need to be borne by the member organisations. Mandatory reporting, as required in the case of credit bureaus, will help in building up the database. Such an initiative would be in line with the 2019 Sinha Committee’s recommendation on the creation of additional information sources for MSME lending.

Availability of digital data, entry of new types of MSME lenders, and new approaches to lending are positive developments that are likely to result in loans to many first-time MSME borrowers. Yet, the process inevitably entails risk, as lenders are entering uncharted territory and learning about new customer segments. It is imperative now to invest in building infrastructure to collate and share learnings. This will enable MSME lenders to navigate this rewarding, yet challenging sector and achieve higher lending volumes and better loan portfolios.

Savita Shankar is a CASI Non-Resident Visiting Scholar. This article is by special arrangement with the Centre for the Advanced Study of India, University of Pennsylvania

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Published on July 21, 2020
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