Viral V Acharya: A case for public credit registry in India
Theme talk by Dr Viral V Acharya, Deputy Governor of the Reserve Bank of India, at the 11th
Statistics Day Conference, Reserve Bank of India, Mumbai, 4 July 2017.
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Valuable insights provided by Dr. O. P. Mall and Shri Anujit Mitra are gratefully acknowledged.
Good Morning Governor, Deputy Governors S.S. Mundra, N.S. Viswanathan and B. P. Kanungo,
our Chief Guest, Speakers and the team!
Introduction to the Annual Statistics Day
Taking off from Shri Chetan Bhagat's address in the pre-conference dinner last evening, I hope
you are all 'alive' today, rather than just 'existing' or 'fading'. Thank you for coming in large
numbers for the Annual Statistics Day Conference of the Reserve Bank of India. This auditorium
has a limited capacity and many of our younger colleagues are watching this through
videoconferencing at three other venues in this building.
2. This conference of the Reserve Bank is eleventh in the series but, for me, it is the first
Statistics Day Conference. I am eagerly looking forward to being around and witnessing the
deliberations.
3. Statistics Day in India is celebrated on the birth anniversary of Late Prof P.C. Mahalanobis,
who graduated with honours in Physics in 1912 and was subsequently attracted to the realm of
Statistics. In modern management parlance, Prof. Mahalanobis was an "out of the box" thinker.
All his contributions emanated while studying statistical problems of immediate importance. As
the Governor mentioned in his inaugural remarks, Prof Mahalanobis set up the Indian Statistical
Institute (ISI) and the survey lab there subsequently blossomed into the present National Sample
Survey Office (NSSO). The Reserve Bank has benefitted immensely over the years from its
collaboration with the ISI on statistical issues and the NSSO on measurement issues.
4. In today's conference, we are privileged to have with us several distinguished guests. Dr.
Martine Durand, our keynote speaker today is the Chief Statistician and Director, OECD
Statistics Directorate, and a leading voice on global statistical issues. She has flown in early
morning today from Paris. A warm welcome to India and to the Reserve Bank, Martine -
Namaste!
5. Let me also welcome Prof. Chetan Ghate of the ISI-Delhi, who is a member of the Monetary
Policy Committee (MPC) and a regular teacher at the RBI Academy, as well as Prof N.
Balakrishna of the Cochin University of Science and Technology, who will both deliver special
talks later today.
6. Prof. Dilip Nachane, who would join us in the afternoon, is Professor Emeritus, IGIDR-Mumbai
and was a member of the Reserve Bank's Technical Advisory Committee on Monetary Policy
(TAC-MP) and the Prime Minister's Economic Advisory Council (PMEAC) for a long time. He will
be chairing the Panel Discussion on the Conference-Theme. Let me also welcome Dr D.K. Joshi
of CRISIL, Ms. Pranjul Bhandari of HSBC, and Dr. Samiran Chakraborty of the Citibank, who are
the other distinguished panel members. Thank you panelists for devoting your valuable time to
the Statistics Day.
7. Let me now move to the Theme of today's conference, viz., 'New Frontiers on Statistical
Methods and Information Base for Central Banks'. Statistical techniques are an integral part of
economic analysis. An interesting acknowledgement of this is the good share of "method
awards" in award of the Nobel Prize for Economic Sciences. The first Nobel in Economics in
1969 went to Ragnar Frisch and Jan Tinbergen for their pioneering work on econometric model
building, i.e., for their integration of economic theory and statistical methods. Over the years,
Nobel "method awards" have also been awarded for input-output method, national accounts,
micro-econometrics, co-integration and ARCH (to Rob Engle, colleague, co-author and dear
friend when I was at NYU Stern).
8. The central role of statistical methods in economic analysis is also reflected in their constantly
growing share in the curriculum for students in economics and finance. The global financial crisis
and its aftermath has been a big structural break to explain which new approaches and methods
are gaining ground. Macroeconomic forecasters have faced interesting questions during this last
decade, as the outbreak of banking and sovereign crises has led to the most basic assumptions
behind forecasts being violated. This has also necessitated further effort towards methodological
refinements, not just in economic theory but also in statistical methods to test the theory. In many
ways, this is an exciting time in my view to be studying economics.
9. The meeting of the G-20 Finance Ministers and Central Bank Governors in 2009 endorsed the
G-20 Data Gap Initiatives (DGI), which focuses on (a) build-up of risk in the financial sector; (b)
cross-border financial linkages; (c) vulnerability of domestic economies to shocks; and (d)
improving communication of official statistics. After the first phase of DGI was largely
implemented, the second phase commenced in 2015 with the objective to strengthen the global
statistical systems so as to aid deeper economic analysis. India's progress in this regard has
been good so far and we are taking further strides, recognizing that such initiatives help individual
countries and also the global economic system.
The Case for a Public Credit Registry in India
10. I will focus in the rest of my remarks on a topic which I feel is vital for the Indian economy at
this juncture and where I expect the Reserve Bank, and more specifically, the Statistics
Department, to play a rather important role. It concerns the setting up of a Public Credit
Registry (PCR), an extensive database of credit information for India that is accessible to all
stakeholders. Generally, a PCR is managed by a public authority like the central bank or the
banking supervisor, and reporting of loan details to the PCR by lenders and/or borrowers is
mandated by law. The contractual terms and outcomes covered and the threshold above which
the contracts are to be reported vary in different jurisdictions, but the idea is to capture all
relevant information in one large database on the borrower, in particular, the borrower's entire set
of borrowing contracts and outcomes.
11. A PCR, if put in place for India, will help in a) Credit assessment and pricing by banks; b)
Risk-based, dynamic and countercyclical provisioning at banks; c) Supervision and early
intervention by regulators; d) Understanding if transmission of monetary policy is working, and if
not, where are the bottlenecks; and, e) How to restructure stressed bank credits effectively. The
extensive and incisive work of Professor José-Luis Peydró of Universitat Pompeu Fabra on such
issues using the Spanish Credit Register is a testimony to the tremendous value a PCR can
bring to clear understanding of the underlying economy. I encourage you to check out his work.
12. Let me start by explaining the motivation for creating such a database. A vast body of
academic literature advocates transparency in credit markets, arguing that it improves the
efficiency of the market and helps creditors as well as borrowers. One of the reasons the credit
information is termed as a 'public good' is its utility to the credit market at large and to the society
in general. In the absence of a central database of credit information, the creditors are restricted
to the information they have about their clients based only on their limited transactions or
interactions with the clients, and this could lead to suboptimal outcomes.
13. A central repository, which, for instance, captures and certifies the details of collaterals, can
enable the writing of contracts that prevent over-pledging of collateral by a borrower. In absence
of the repository, the lender may not trust its first right on the collateral and either charge a high
cost on the loan or ask for more collateral than necessary to prevent being diluted by other
lenders. This leads to, what in economics is termed as, pecuniary externality - in this case, a
spillover of one loan contract onto outcomes and terms of other loan contracts. Furthermore,
absent a public credit registry, the 'good' borrowers are disadvantaged in not being able to
distinguish themselves from the rest in opaque credit markets; they could potentially be
subjected to a rent being extracted from their existing lenders who enjoy an information monopoly
over them. The lenders may also end up picking up fresh clients who have a history of
delinquency that is unknown to all lenders and this way face greater overall credit risk.
Current Credit Information Systems in India
14. Let us now have a look at the current credit information systems in our country. The private
Credit Bureaus (CBs) operating in India are regulated by RBI under the Credit Information
Companies (Regulation) Act, 2005 and include Credit Information Bureau (India) Limited (CIBIL),
Equifax, Experian, and CRIF Highmark. Each one of these focuses on data analytics to provide
credit scores, and allied reports and services. These analytics are useful for the member banks
for issuing credit cards as well as for taking decisions (primarily on retail loans) as of now.
15. The Reserve Bank has set up the Central Repository of Information on Large Credits
(CRILC) in 2014-15. It is now one of the most important databases for offsite supervision. Here
the Scheduled Commercial Banks (SCBs) in India report credit information of their large
borrowers, i.e., those having aggregate fund-based and non-fund based exposure of INR 50
million and above. It covers around sixty per cent of the loan portfolio and around eighty per cent
of the non-performing loans of SCBs. The reporting is done on a quarterly basis but the
slippages are required to be reported in another format on as-and-when basis. The CRILC is
designed entirely for supervisory purposes and its focus is on the reporting entities' exposure to
the borrower (as individual and/or as a group) under various heads, such as bank's exposure to
a large borrower; the borrower's current account balance; bank's written-off accounts; and
identification of non-co-operative borrowers, among others. However, CRILC captures only
limited detail about the borrowers such as the industry to which they belong and their external
and internal ratings. The pooled information under CRILC is shared with the reporting banks but
is not shared with the Credit Bureaus, larger lender community, or researchers.
16. My colleagues in DSIM are familiar with the Basic Statistical Return - I or BSR1, where
account level credit information (an "account" being a specific loan or facility between a bank and
a borrower) is reported by banks. As the name suggests, it is a statistical return which captures
some metadata for the account such as district and the population group of the place of funds
utilisation; type of account such as cash credit, overdraft, term loan, credit cards, etc.;
organisation type such as private corporate sector, household sector, microfinance institutions,
Non-Profit Institutions Serving Households (NPISH) and non-residents; and occupation type such
as agriculture, manufacturing, construction, and various financial and non-financial services. The
interest rate charged along with the flag for floating vs fixed is also reported here. These details
are not present in CRILC which is a borrower-level dataset rather than an account-level dataset.
Though BSR1 contains a "health code" for each account, it is not comprehensive enough to
cater to the supervisory needs as it is not feasible to aggregate all accounts maintained by a
borrower in the absence of a unique identifier across the reporting banks. Due to a number of
reasons, even bank-level aggregation of delinquency in BSR1 will not in general match with that
reported through CRILC. Aggregated statistical information with spatial, temporal and sectoral
distribution from BSR1 is shared in the public domain for researchers, analysts and
commentators. Account-level data is, however, kept confidential but is shared by the Reserve
Bank with researchers on a case to case basis under appropriate safeguards.
17. These databases maintained in the Reserve Bank are not available to individual banks in real
time to take credit decisions at the micro level. They do not capture fully the credit data at
origination level. In particular, the 360 degree view is not available to creditors in any of the
systems discussed. Individually, some of these systems can be swiftly strengthened with just a
few additional fields. For example, capturing in BSR1 the unique account-holder identifier in the
form of Aadhar for individuals and Corporate Identification Number (CIN) for companies may
make it possible to view all accounts of each borrower across banks.
18. Next, I would like to draw your attention to the company finance databases available with the
Reserve Bank and with the MCA. These contain the audited or unaudited financial results of the
corporates submitted by them at various frequencies. Here again the key identifier is the CIN.
The power of the information can be substantially enhanced if we can make BSR1 and CRILC to
talk to each other and further link them both with the MCA database containing financial results of
the corporate sector.
International Experience with Public Credit Registers
19. Let me now turn to the international scenario. A survey conducted by the World Bank
reported that as of 2012, out of 195 countries that were surveyed, 87 were having Public Credit
Registers - the number must have increased by now. The private credit bureaus are also
functioning well in many of the developed countries and they co-exist with the PCRs. In US, the
Dealscan by Thomson Reuters is a prime example which covers the syndicated loan origination
data including information on arrangers; price and maturity terms; credit lines or term loans; and
loan characteristics such as covenants. Since banks voluntarily provide credit data at the time of
origination itself, it is almost a real-time dataset and one gets to know in a week or two weeks'
time whether there is a change in the credit market conditions.
20. Dun & Bradstreet or DNB in short, is nearly two centuries old and has perhaps the largest
commercial database in the world. Their website claims that they track over 265 million company
records which they derive from 30,000 data sources and is updated 5 million times per day.
DNB's own correspondents gather data on firms by visiting and telephoning the firm's principals.
It is interesting to note that in the 19th century, these correspondents who were often lawyers,
included such luminaries as Abraham Lincoln, Woodrow Wilson and Calvin Coolidge (ref. J.G.
Kallberg, G.F. Udell / Journal of Banking & Finance 27 (2003) 449-469).
21. Let me give a real life example to illustrate the utility of such information systems. In the
aftermath of the collapse of Lehman Brothers in September 2008, there were economists who
asserted that the credit flow in the United States was unaffected by pointing out to the robust
credit growth in bank loans. But a deeper analysis of the Thomson Reuters Dealscan data
quickly revealed that the credit growth was almost entirely attributable to the corporates drawing
down (a form of a "bank run") on the existing credit lines. The origination of new loans had indeed
dried up.
How a Public Credit Registry can help in India
22. Let us now envisage how exactly a public credit registry can help in India. Firstly, it is required
to improve the credit culture in our country. It has been demonstrated in the 'Doing Business
2017' report that credit information systems impart transparency in the credit market, following
which access to credit improves and delinquencies decrease. At present, several Indian banks
burdened with mounting NPAs appear less confident in taking credit decisions. A transparent
public credit registry would help the bankers to rely on objective data for making credit decisions
and also enable them to defend their actions with market evidence when subjected to scrutiny.
23. Second, large borrowers get a preference in credit markets due to their existing credentials in
the public space. They have established credit history, brand value, and supply of collateral. In
contrast, small and marginal aspirants, start-ups, new entrepreneurs, and small businesses in
micro, small and medium enterprises (MSME) sector are disadvantaged as they lack many of
those desired qualifications for credit. Transparency of credit information would serve as a
"reputational collateral" for such borrowers. This would not only help promote financial inclusion,
but also reward the good borrowers thereby imparting credit discipline. We just have to look at
our willingness to transact on eBay to understand how reputation builds up for effectively
anonymous sellers from their transaction records captured on a website. Similarly, public credit
registry would help create a level-playing field among different sizes of borrowers.
24. Third, public credit registers in many countries have gone beyond the credit relationship of
borrowing entities with financial institutions. They tap other transactional data of borrowers
including payments to utilities like power and telecom for retail customers and trade credit data
for businesses. Why might such data help? Lenders in the formal sector often hesitate to extend
a line of credit to new customers due to the lack of credit scores. Regularity in making payments
to utilities and trade creditors provides an indication of the credit quality of such customers. In
turn, credit from the formal sector can become accessible to new borrowers, boosting financial
inclusion. As a side benefit, the extent of financial inclusion will likely become more precisely
measurable for policy makers.
25. Finally, public credit registry can have a profound impact for regulatory purposes. In its
absence, only fragmented images are available of credit behaviour and indebtedness. PCR will
help in getting to a complete picture that is necessary for supervisors and policy makers to
assess credit risk of the entire system. To facilitate this, the PCR must cover the following
aspects of the credit data: First, the bank-borrower loan-level data detailing loan terms at time of
origination along with data on borrower's economic and financial health. Second, the internal and
external ratings (or credit scores) and their evolution, and where applicable, market-based
measures of firm-level and sector-level credit risks. Third, bank-borrower loan-level restructuring
data with all details. Fourth, secondary loan sales and price information. Fifth, borrower-debt level
Default and Recovery (LGD) data. This would be a good start!
Who should operate the Public Credit Registry?
26. Large Public Credit Registers are operated either by the central banks or state authorities in
various countries. They are typically not operated by the private sector, though Credit Bureaus in
some jurisdictions capture many of the items discussed above. In some jurisdictions, the raw
data collected by the central PCR is shared with the CBs, which in turn make value addition by
pooling data from other sources and come up with further analysis such as credit scores /
reports to their clients, typically commercial lenders. Since we are talking about a large database
containing lots of private information, it also needs to be handled by an authority which is
trustworthy in the public eye as well as backed by appropriate judicial powers to ensure timely
and accurate data gathering. Therefore, it is found internationally that with rare exceptions the
Public Credit Registries are managed by central banking or banking supervisory authorities.
In Summary...
27. Let me conclude by restating that a transparent and comprehensive public credit register is
the need of the hour in India. More and more countries are moving towards this with a view to
improving the credit culture in their jurisdictions. Such registers help in enhancing efficiency of
the credit market, increase financial inclusion, improve ease of doing business, and help control
delinquencies. Incorporating unique identifiers for the borrowers (Aadhar for individuals and CIN
for companies), Reserve Bank's BSR1 and CRILC datasets can quickly be converted into a
useful PCR covering customers of SCBs to start with. It can then be expanded to cover other
financial institutions in India. A comprehensive PCR down the road will be even more effective.
28. Setting up a comprehensive PCR will, however, require much team work and vision. It will
demand expertise to handle large volumes and varieties of data assembled from diverse
sources. It will require working with several stakeholders, other regulators and international
agencies with expertise in helping set up such registers. That's a worthy challenge for the pool of
statisticians assembled here on the eve of the 11th Statistics Day. Governor and I hope we can
set up, as a matter of priority, a high-level task force that can provide a roadmap for attaining this
goal of developing and unleashing a powerful credit information system for our country.
29. There are several other "information base" challenges for the long horizon for the team:
Employment Statistics; Household Inflation Expectation Survey in rural and informal economy;
Big-data real-time indicators of prices and consumption; Google images and mobile-phone data
for economic activity indicators; to just a list few. Having a go at some of these will be a fitting
tribute to Prof. Mahalanobis whose contributions were truly long-term and have lived far beyond
his immediate life-span.