CRILC Framework: A Tool for Financial Institutions to Monitor NPAs

In January 2016, the Reserve Bank of India (RBI) imposed a penalty of INR 1 crore on the State Bank of Travancore (SBT) for deficiencies in reporting to the Central Repository of Information on Large Credits (CRILC).
Could this have been caused due to a compliance oversight or lack of awareness?

Non-Performing Assets: Are these Identifiable?

For any financial institution (FI), the conversion of a performing asset to a non-performing one does not happen overnight. There are some early signs of distress that if ignored, can lead to delinquency. Furthermore, cumulative non-performance across institutions can have a snowballing effect and lead to a credit crunch, paralyzing the economy.
With strong systems in place, warning signs can be recognized well in advance to prevent problems and also alert other players within the system, to curb the negative effects.

But this is easier said than done.

Monitoring non-performing assets (NPA) is one of the biggest concerns of any financial institution as well as that of the regulator. In the pre-digital reporting era, this was an even bigger problem. The limited and often untimely exchange of information across stakeholders did not provide enough time for corrective measures. With the recent boost to digital reporting, the situation has improved, but challenges around the efficiency of tracking and exchange of information are still a concern.

To help overcome some of these challenges, the banking regulator of India, the RBI, introduced guidelines for credit risks on large exposures of banks and financial institutions. Subsequently, in 2014, a collaborative system called the Central Repository of Information on Large Credits (CRILC) was built to help banks and financial institutions evaluate their NPAs and share information with other institutions as alerts.

Central Repository of Information on Large Credits (CRILC)

The RBI came up with guidelines for individual financial institutions to highlight the status of stressed borrowers (based on their repayment status) and furnish the details to the RBI to be stored in a central database: CRILC. Participating institutions are required to submit quarterly reports on all the borrowers with an aggregate fund-based and non-fund-based exposure of INR 50 million or more under the CRILC guidelines. The guidelines also require institutions to segregate borrowers as Special Mention Accounts (SMA) of various levels to gauge their probability of going delinquent.

What do Financial Institutions have to Report under CRILC?

All the financial institutions under the RBI have to separately report the following to CRILC:
I. CRILC-Main, a quarterly submission that comprises four sections:
a. Section 1: Exposure to large borrowers (Global Operations),
b. Section 2: Reporting of technically/prudentially written-off accounts (Global Operations),
c. Section 3: Reporting of balance in the current account (Global Operations) and
d. Section 4: Reporting of non-cooperative borrowers (Global Operations).
II. CRILC-SMA 2 and Joint Lenders Forum (JLF)* Formation: Apart from regular quarterly submissions, reporting institutions are advised to submit these two reports on an ‘as and when’ basis, i.e. whenever a large borrower’s account becomes overdue for 61 days (SMA2) and/or a Joint Lenders Forum (JLF) is formed in respect of an SMA 2 classified borrower. Borrowers are classified as SMA1 or SMA2 based on the following criteria:
Source: https://rbi.org.in/scripts/PublicationReportDetails.aspx?UrlPage=&ID=715

*JLF: Separate empowered group of lenders that can speed up decisions to resolve stressed assets.

What are the Filing Dates for CRILC Submissions?

Reporting under CRILC was started in the quarter that ended in June 2014. The RBI has mandated banks and financial institutions to submit borrower-wise exposure data to CRILC-Main and CRILC-SMA 2 and JLF Formation through the XBRL-based reporting system every quarter. The CRILC-Main report is required to be submitted within 21 days from the close of the relevant quarter.

The consolidated CRILC data including details of SMA 2 borrowers are shared back with reporting entities so that they might use them while evaluating loan applicants.

Quality CRILC Submissions

The importance of ensuring robust data quality hardly requires elaboration. The RBI analyses the reported data for correctness based on past submissions. On identification of any discrepancies in the reported data, the RBI asks reporting institutions to resend the corrected data. If any institution repeatedly reports incorrect data, it may penalize for non-compliant submissions. While preparing quality CRILC submissions is not difficult, it can be a challenge for large FIs with enormous data sets to handle, and checking for their conformity with previous quarters adds to the reporting burden.

IRIS’ CRILC Analytics Solution helps large reporting institutions to prepare quality CRILC submissions. The application allows you to analyze CRILC reportable data with the past three quarters reported data and also enables you to verify CRILC SMA-2 data with CRILC Main data for the current quarter. IRIS’ application is flexible to add any additional validations as per the financial institution’s specific requirements and can also enhance its cross-verification of CRILC main data from four quarters to six quarters.

The use of the IRIS CRILC Analytics Solution assures structured data submissions to the RBI by eliminating all the discrepancies in the reportable data by evaluating and comparing current reportable data with past submissions.

How Can Banks Benefit from CRILC Data?

The RBI shares the CRILC data on stressed borrowers with all lending institutions. The CRILC reports help institutions look at and tackle their NPA or near NPA exposures. The RBI has asked FIs to make use of credit information provided by CRILC for opening current accounts. FIs should use the data available in the CRILC database to verify whether the customer is availing/has availed of a credit facility from another FI.

The CRILC system is an innovative data-sharing framework that collects data on non-performing assets from all Indian FIs and then advises the rest of the sector about the state of impairment. This is a mechanism that dramatically improves the stability of the banking sector in India and is an initiative that many other countries are following closely.

You may contact me at gautam.mahanti@irisbusiness.com for further details.

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