Under SIDBI’s Mentorship, FIT Rank for MSMEs Launched by TransUnion CIBIL and Online PSB Loans Limited (OPL)

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Under SIDBI’s Mentorship, FIT Rank for MSMEs Launched by TransUnion CIBIL and Online PSB Loans Limited (OPL)

Under SIDBI’s Mentorship, FIT Rank for MSMEs Launched by TransUnion CIBIL and Online PSB Loans Limited (OPL).

Continuing their mission of supporting banks and financial institutions to sustainably grow their MSME credit portfolios while driving access to finance for underserved and unserved MSMEs, TransUnion CIBIL, in collaboration with Online PSB Loans Limited (OPL), under the mentorship of SIDBI, today launched FIT Rank – a comprehensive ranking model for MSMEs.

FIT Rank leverages the power of Goods and Services Tax (GST), Bank Statements, and Income Tax returns (ITR) information to provide a ranking model for MSME lending. This ranking model uses machine learning algorithms to predict the probability of an MSME becoming a non-performing asset (NPA) in the next 12 months. FIT Rank provides a ranking for the MSME, based on its financial, income, and trade data on a scale of 1 to 10, FIT Rank 1 being for the least risky MSME and FIT Rank 10 being for the most at-risk MSME. Each FIT rank corresponds to a Probability of Default (PD) (see Figure 2: FIT Rank corresponding PD). The lower the FIT Rank, the lower the perceived risk of default associated with the MSME. This is the first time TransUnion CIBIL in collaboration with OPL has developed a credit default predictor model based on financial, income and trade data which has been made possible due to the improved digitization in the credit industry. 

FIT RANK triangulates information from multiple sources to provide a unified view of financial, income and trade data for an MSME, enabling improved risk differentiation and sharpened credit underwriting for MSME loans. In the last financial year (FY 2021-22), 27 lakh MSME’s availed credit, out of which 11.6 lakh MSMEs were new-to-credit. By incorporating GST, Bank statements and Income Tax return data, the model enables intensive risk differentiation of more than three times on the 11.6 lakh new-to-credit MSMEs. Of the remaining 15.4 lakh MSMEs with an existing credit footprint, more than 9 lakh MSMEs (58%) fall into the medium-risk segment (CMR-4 to CMR-6) through the CIBIL MSME Rank (CMR) model. The FIT Rank enables more than five times risk differentiation in this medium-risk segment by ranking MSMEs based on their GST, Banking and ITR data. With banks and credit institutions being able to assess credit risk using this comprehensive ranking model, they can confidently lend and provide financial assistance to many more MSMEs (see Figure 1: Mapping India’s MSME sector through the FIT Rank Lens).

Commenting on the utility of FIT RANK, the Chairman and Managing Director of SIDBI, Mr. Sivasubramanian Ramann, expressed: “Insights show that only about one-third of MSMEs in India are served through the formal credit ecosystem, indicating significant opportunities for increasing credit penetration in this key sector. Credit flow to MSME entities can be further expanded and also made more efficient by widening the risk underwriting parameters of MSMEs based on multiple data sources available in the marketplace. FIT Rank will benefit MSMEs with a satisfactory track record of GST payments, banking behavior and Income Tax Returns to be able to become more credit eligible.”

He also mentioned that, “Appreciating the power of FIT to enhance credit underwriting, SIDBI has launched a new Express loan Product for MSMEs to provide them loans up to INR 50 lakh for purchase of machinery and roof top solar  through straight through processes.”

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