A country’s growth is highly dependent on its economic activity. And the foundation for a robust economy is a strong lending system. While the financial institutions ensure a healthy and continuous flow of money into the economy, the central bank is entrusted with the responsibility of maintaining its financial stability. The regulator establishes rules and regulations around lending procedures including risk identification and reporting, to safeguard the position of financial institutions.
Though this helps in monitoring, increased rules and compliance requirements result in added burden on central banks that have large data sets that need to be analyzed regularly. They also have the added responsibility of actively sharing the data with financial institutions as a means to mitigate the risk.
This not only requires easy availability of data but also data that is timely and of top-notch quality. For this, the process of validating every data set has to be defined and updated from time to time. This is where the need for structured data arises.
Many central banks and financial regulators across the globe have adopted XBRL, the business, and information reporting standard. Some of the value propositions of XBRL that attract regulators are standardization, pre-validation, the ability to provide quality, and flexibility to capture granular data.
Let’s take a look at how the Reserve Bank of India, the country’s central bank has used XBRL to its advantage.
RBI Uses Structured Data to create Robust Lending System
The Indian economy today is witnessing a prolific start-up culture with strong M&A activity and solid private equity infrastructure development. While the economy is on a sound growth trajectory and businesses are flourishing, there has been an upsurge, with no proper exit mechanism. The balance sheets of corporates as well as banks, therefore, remain equally stressed.
This calls for better recognition and resolution procedures.
In 2014 the Reserve Bank of India (RBI), came up with guidelines for Early Recognition of Financial Distress[1]. The objective was to systematically track the performance of large exposures to identify credit risks at an early stage, take prompt steps to resolve them, and ensure fair recovery for lenders and investors.
The kind of quality expected from this data called for the use of a machine-readable, structured data format that left no room for ambiguity. With RBI already having implemented its XBRL platform in 2008 for accepting returns from reporting institutions, the Central Repository of Information on Large Credits (CRILC) based on XBRL seemed like a natural extension.
With CRILC, reporting institutions are required to submit information on large credits with special mention to stressed assets every quarter and in turn, use the pool of collated data across all lending institutions for their lending purposes.
What Information Does RBI Collect through CRILC?
CRILC, a repository of data submitted by reporting institutions is the holy grail of the country’s leading information on stressed assets.
Structured to capture elemental data at the borrower level, CRILC identifies all the borrowers across the country through their Permanent Account Number (PAN), a unique identification number issued by the Indian Income Tax department.
The reporting institutions capture finer details of borrowers across the following parameters:
- Quantitative Information: This includes various information points about every lending relationship of the borrower such as the amount of exposure, outstanding amount, overdue period, etc.
- Qualitative Information: Based on the repayment track record of the borrowers, they are classified as Special Mention Accounts (SMAs) that are further categorized based on the regularity of their interest payments. The reporting institutions need to report qualitative information for every lending relationship of the borrower such as credit rating and SMA classification. In case a lending relationship is stressed, the asset classification such as sub-standard, doubtful, restructured or loss also needs to be specified.
Additional qualifying information such as wilful defaults, under litigation cases, penalties imposed by other regulators, etc. also need to be reported. You can access the XBRL taxonomy for the CRILC system to get a list of all data sets to be reported.
The data submitted by individual reporting institutions are then collated and stored in the central repository. Every institution has access to this pooled data to assess the overall status of any borrower. The repository provides an aggregate view of the credit exposure for the borrower along with the asset classification and assists member institutions in gauging the borrower’s repayment capabilities. For highly leveraged customers, risk profiling becomes easier and the lending institution gets prior information that helps in planning any mitigation techniques.
You can read more in our blog on CRILC, filing dates, and applicability.
A Break-through Idea for Monitoring Financial Distress
The CRILC system went live in April 2014. RBI has certainly benefitted by leveraging the core principles of XBRL i.e. standardization and data accuracy. The quality and depth of data enable RBI to monitor the lending exposures of reporting institutions to a great extent. This in turn also helps reporting institutions manage their credit appraisal process.
Within a year of implementation, RBI also recommended banks use the CRILC data while opening current accounts. This clearly shows the relevance of the CRILC system in the overall operations of financial institutions.
The CRILC framework has been one of the finest applications of XBRL in the banking domain. It was recognized in January 2016 by XBRL International, the not-for-profit consortium that maintains and governs the XBRL standard, for the innovative use of XBRL.
CRILC: A Path for Other Central Banks to Follow
Lending and monitoring of NPAs is a universal problem faced by most central banks and hence the need for systems such as CRILC. Having a pooled repository of the country’s lending and stressed assets data available in a structured format can help central banks to regulate the economy in several ways. While they get visibility and access, it also puts them in a better position to resolve distress before it escalates. A framework like CRILC should be looked at by the regulators who are in the process of modernizing their IT systems.
We have been consulting various central banks and other regulators for their XBRL implementations for the last 11 years. IRIS is also proud to have been RBI’s implementation partner since the inception of XBRL based reporting in 2008. Our flagship offering IRIS iFILE has been deployed at the RBI and more than 15 regulators globally.
