2. Credit Risk Modelling for MSMEs - defining default

In my last article, I discussed the challenges faced when one uses financial analysis designed for corporate customers and tries to assess the credit risk for MSMEs. Fortunately, due to advances made in data science, it is now possible to customize the financial analysis for MSME portfolios. In this article, I describe the first step for developing a probability of default (PD) model. In the subsequent articles, I will describe financial and other data sources that can be used for creating a PD model. Then, I will describe the logic of variable selection from these sources. That will be followed by the steps of feature engineering, feature selection and model development. The latter steps are common across models. Finally, I will?demonstrate how an integrated model gives a comprehensive credit score for the MSME borrower with a high prediction power and accuracy. MSMEs.

How bad is really bad?

PD models fall in the category of supervised machine leaning. That means that the data scientist has to tell the machine that which are the borrowers, which actually defaulted, and which are those that did not. That brings us to the fundamental question of what is default.

The word default can mean various things to various people. In the world of external credit rating (think of Standard & Poor and Moody’s) a delay of one day in paying the instalment of a loan or the interest on it by even one rupee is a default. So if you do not want to be classified as a Default – pay all the money due on or before the due date. This is a very stringent definition and was designed for bond issuers. Bond markets operate differently than banks loans. In the bond market the investor does not have any direct contact with the issuer. He/ she has bought the bond based on independent credit risk assessment done by a rating agency. Therefore, slightest delay in repayment should alarm him/her. For more details on evolution and role of the external credit rating agencies, may refer to this excellent report by Dr Y V Reddy, Ex Governor of the Reserve Bank of India.[1]

Evolution of definitions – NPA to SMA

Banks by contrast have a complete knowledge of the business of the borrower and their key stakeholders. While banks are also very stringent in the matters of non-payment of dues by a borrower, the impact of this is more pronounced when there is continuous delay of 90 or more days in the repayment by the borrower. In the banking industry, such accounts are called Non Performing Assets (NPA). And, NPA is a bad word for bankers. These are assets that banks hold but they do not generate any income and hence are non performing. The moment as asset is classified as NPA banks have to provision for loss.

In the year 2013 RBI came up with a discussion paper on ways to reduce stress in the banking system and early resolution of stressed accounts[2]. They came up with new category called Special Mention Account (SMA). The idea of SMA had been discussed earlier in the year 2002 in another notification of RBI.[3] This concept was implemented in 2014 and created three categories of stressed account before it could be classified as NPA.

Approaching the problem statistically

We perform a roll rate analysis to identify a good definition of default. Essentially, we look at all the cases which were in overdue category on a given date and observe how many of those correct themselves by repaying the money due to the bank, and how many deteriorate further in the next 5, 30, 45, 60 days and so on. That gives us a sense of when should an alarm be raised.

The graph below shows a hypothetical roll rate analysis.?

Selecting a definition of default

In the above-mentioned hypothetical example, 60+ ever in the next 12 months seems to be a good cut off. By using this cut off, we are not acting too late and we know that if an account is predicted to default there is a 60% chance that it will actually do. At the same time, we are not classifying too many accounts as default and we manage to keep false positive rate low. It should be noted that different banks with different risk appetites, might be more conservative or liberal in the selection of default definition.

In case the borrower has loan above ? 5 Cr, the lending bank has to disclose its name to CRILC and mention the status – standard, SMA 0, SMA 1 etc. In such a scenario banks might want to change the definition of default to SMA 0 (one day delay in the payment of interest or principal). Because once the details are shared with RBI, it is known to all the banks in India and none of them might be willing to take over the account.

It also depends on the business segment. Few business segments, which charge higher interest rate and generate more business volume can be more liberal and vice versa. Thus, selection of default definition is a very important decision and should be made in discussion with team members of business (sales) and credit functions after performing the roll rate analysis as described above.?

(Note - views expressed are personal)

[1] https://rbidocs.rbi.org.in/rdocs/Bulletin/PDFs/13292.pdf

[2] https://www.rbi.org.in/scripts/PublicationReportDetails.aspx?UrlPage=&ID=715

[3] https://www.rbi.org.in/scripts/NotificationUser.aspx?Id=899&Mode=