By Savita Shankar
The micro, small, and medium enterprise (MSME) sector is estimated to comprise of over 63 million enterprises and contributes 28.8% to the GDP of the country. Most importantly, the sector employs around 111 million people, 26% of the total workforce. Yet, according to a 2018 report by TransUnion CIBIL-SIDBI, credit to MSME entities accounts for only 14% of total formal credit in India. As per the report, an additional 10% of loans go to MSME owners in their individual capacity, often against collateral of property and vehicles. While funding to the MSME segment over the last one year has increased at a rate of more than 20%, the highest growth has come from loans to individual MSME owners. This indicates a need for reducing information gaps regarding MSME entities to enable bankers to make more informed credit decisions without a reliance on the personal collateral of owners.
Many of the policy measures taken in the past have primarily focused on increasing the volume of funds to the sector. The MUDRA Bank initiative sought to increase funding availability to last-mile providers of MSME loans such as banks, non-bank finance companies (NBFCs) and micro finance institutions. The number of last-mile providers has also been increased with the licencing of new banks, especially small finance banks that have a special focus on the sector. Several NBFCs focused on MSMEs have also emerged. However, availability of funds does not guarantee that increasing amounts of loans will be made available to the MSME sector as lenders need to be convinced of the bankability of the loans. A major hurdle for MSME financing is the information opacity prevalent in the sector as many of the units do not have complete accounting records, audited financial statements, or well-articulated business plans. This makes credit assessment by potential lenders very difficult. There is hence a need to take steps to reduce the information asymmetry in the sector.
Several recent positive developments such as the implementation of the Goods and Service Tax (GST) data and the setting up of electronic platforms for auctioning trade receivables (Trade Receivables Discounting System or TReDs) are useful in verifying and cross-checking details provided by MSME owners. In addition, the growth of services providing credit information on individuals, and attempts to assign ranks to MSMEs based on credit history are also useful to potential lenders. Yet, measures to directly link the data generated to loan and default data could be very insightful.
A policy initiative that attempted to help potential lenders assess MSMEs was the Performance and Credit Rating Scheme (PCRS) aimed at enabling registered micro and small enterprises to obtain credit ratings. The rationale for the scheme was that credit ratings would enable such enterprises to obtain cheaper and faster financing from banks. Under the scheme, micro and small enterprises could obtain a 75% reimbursement of the rating fees charged by credit rating agencies for their first credit rating. However, the availability of budgetary funds for the scheme has varied year on year and, as a result, usage of credit ratings by MSMEs has been sporadic. As many MSMEs are unsure about the process and their likely rating, the subsidy acts as an important incentive. Moreover, the scheme has often been used by bankers after completing their own credit assessments with a view to ascertaining risk weights for capital adequacy purposes. A recent paper on the scheme is available at (bit.ly/2UMzTBe). Yet another policy initiative, the credit guarantee scheme, helps reduce risk exposures for lenders for smaller, collateral-free loans but does not help reduce information asymmetry for the lender.
To help get the full benefit of the credit rating scheme, the credit guarantee scheme and other favourable developments relating to MSMEs, the information generated should be utilised to build a large credit risk dataset. The database should utilise data generated by the implementation of the GST, data available at credit bureaus, banks, NBFCs and rating agencies. Participants should submit financial statements well as default data. A similar database with SME data being shared by members has been found to be useful in Japan. The names of the customers are not included when data is shared, but entries relating to the same entity are clubbed by use of algorithms. The database was set up specifically to encourage bank lending to SMEs. The costs of setting up the model need to be borne by potential users and subscribers. Mandatory reporting, as required in the case of credit bureaus, will help in building up the database. MSMEs are a heterogeneous and large group, so such a database can help in understanding the various subsegments in the group. The use of analytics will help in developing scoring models for MSME lending specific to each subsegment.
The database could also help in developing differential pricing for credit guarantees. Once set up, the model will result in a lower unit cost per loan assessment as compared to a credit rating exercise. Currently, some banks have their own in-house models developed with the data available to them, however, a model based on sector-wide data can enable development of more robust statistical credit scoring models. Availability of such a model could greatly reduce the appraisal time and cost for MSME loans as well as reduce risk levels. Moreover, the models will improve with time as more and more data is added. This initiative would be distinct from the MSME databank initiative that the government is pursuing. The latter is useful in developing a census of MSMEs and in enabling public sector entities to meet their procurement obligations from MSMEs.
The time is now right to introduce an MSME-focused credit risk database that will help lenders in developing credit scoring models that will aid appraisal of MSME loan requests. This is the next important step that will greatly help in addressing the financing challenges faced by these entities. Given the large number of MSMEs in the country and their importance in employment generation, the returns are bound to be worth the investment.