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The role of credit rating in addressing gaps in micro and small enterprise financing : The case of India
Abstract
We describe the common financing challenges faced by micro, small, and medium-sized enterprises (MSMEs) in India and some important measures taken to address them, with a focus on the credit rating scheme implemented in 2000. We examine the usefulness as well as the limitations of the scheme, drawing on interviews with rating agencies and MSMEs. With credit rating being an expensive exercise, the availability of government subsidies under the scheme has been an important factor in encouraging MSMEs to get themselves rated, thereby reducing information asymmetry with banks and enabling access to credit. Given the large number of unbanked MSMEs in the country, leveraging the data generated by MSME lending and credit rating in the country through the creation of a credit risk database is necessary. Lenders will then be able to tap into the collective data generated to make more informed credit decisions with regard to MSMEs without relying on subsidies.
Over 63 million micro, small, and medium-sized enterprises in India generate lending and credit rating data. How can lenders leverage these to make informed credit decisions?
... Credit ratings offer necessary information on bonds' riskiness, making the Indian corporate bond market more transparent (Bose and Coondoo, 2003). They also address information asymmetries, especially in the micro, small and medium enterprises (MSME) sector (Shankar, 2019). Bond ratings assume importance as the Indian corporate bond market is nascent. ...
Purpose
This study attempts to identify fundamental determinants of bond ratings for non-financial and financial firms. Further the study aims to develop a parsimonious bond rating model and compare its efficacy across statistical and range of machine learning methods in the Indian context. The study is motivated by the insufficiency of prior work in the Indian context.
Design/methodology/approach
The authors identify the critical determinants of non-financial and financial firms using multinomial logistic regression. Various machine learning and statistical methods are employed to identify the optimal bond rating prediction model. The data cover 8,346 bond issues from 2009 to 2019.
Findings
The authors find that industry concentration, sales, operating leverage, operating efficiency, profitability, solvency, strategic ownership, age, firm size and firm value play an important role in rating non-financial firms. Operating efficiency, profitability, strategic ownership and size are also relevant for financial firms besides additional determinants related to the capital adequacy, asset quality, management efficiency, earnings quality and liquidity (CAMEL) approach. The authors find that random forest outperforms logit and other machine learning methods with an accuracy rate of 92 and 91% for non-financial and financial firms.
Practical implications
The study identifies important determinants of bond ratings for both non-financial and financial firms. The study interalia finds that the random forest technique is the most appropriate method for bond ratings predictions in India.
Social implications
Better bond ratings may mitigate corporate defaults.
Originality/value
Unlike prior literature, the study identifies determinants of bond ratings for both non-financial and financial firms. The study also experiments with modern machine learning techniques besides the traditional statistical approach for model building in case of relatively under researched market.
... As one of the main objectives of the PCRS is to reduce the information asymmetry prevailing in the MSME sector and to shorten the time required by banks to complete loan appraisals of MSMEs, the rating reports about MSMEs are more detailed than those of large corporations. The reports are typically six to seven pages long and provide detailed information on the enterprise's operations including the exact location using GPS coordinates (Shankar, 2019). ...
Despite being the focus of numerous policy initiatives, the credit gap of the MSME sector
in India has been persistent. An investment in tapping the data being generated by
lenders to build a database to inform future lending is likely to improve the quality of
lending decisions over time. This, in turn, has the potential to further expand MSME loan
access and reduce MSME borrowing costs. To realize this objective, a credit risk database
(CRD) has been found to play a useful role in catalyzing collateral and guarantee free loans
for SMEs in Japan. The availability of the OCEN network and the account aggregator
framework offers an opportunity to create a CRD involving banks and NBFCs at relatively
low incremental cost. The CRD’s role will be distinct from that of credit bureaus and rating
agencies as it is based on financial and default data for the sector as a whole, rather than
for individual entities. The main benefits of CRD include the development of credit
scoring models based on nationwide data and the availability of benchmarks for different
segments of the MSME sector. Additional benefits are that the credit scores from the
models could be used to develop a more sophisticated pricing mechanism for guarantees
and for potentially aiding MSME loan securitizations.
Access to finance (A2F) for micro, small and medium-sized enterprises (MSMEs) is critical for their survival and development, yet such access remains constrained for many MSMEs worldwide. In this article, the authors identify some of the key issues on the supply side of MSME finance globally, such as the lack of a credit registry/over-reliance on collateral, over-reliance on debt finance, high transaction costs of serving MSMEs and lack of non-bank/specialty financial institutions. They pinpoint the root causes and trace their impacts using concrete examples. The authors then share policy prescriptions over the short, medium and long terms that have been collected from around the world to address these concerns. They also provide case studies as inspiration to policymakers and other stakeholders in MSME finance. This compilation is a handy resource for policymakers and other stakeholders in MSME finance to understand and overcome barriers on the supply side.
Small and medium-sized enterprises (SMEs) play a significant role in Asian economies as they contribute to high shares of employment and output. However, SMEs generally have limited access to finance compared to large enterprises. Given the bank-dominated financial systems in Asia, banks are the main source of financing for SMEs. For financial institutions, it is crucial to distinguish sound SMEs from non-healthy ones in order to avoid the accumulation of non-performing loans. Information asymmetry in this sector can be reduced by using accumulated data on SMEs and by employing credit analysis techniques, allowing lending institutions to recognize healthy SMEs. It is crucial for governments to collect SME data and prepare rich databases, such as the Credit Risk Database (CRD) of Japan. This will also help governments to formulate economic policies. In this paper we define and describe in detail the role and characteristics of Japan’s CRD in SME development and explain how it can be an example for other Asian economies to establish similar soft infrastructure that can make important contributions to SME development and boost economic growth.
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