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Pan-European Credit Data Case Study on Credit Data Pooling by Banks for Banks
RiskCenter.com (June 6, 2006)
Author: Ellen J. Silverman
Date: Tuesday, June 6, 2006
Institutions seeking to apply the internal ratings-based (IRB) approach under Basel II are required to provide accurate estimates of their credit risks. However, most banks do not have enough internal data on default and recovery to calculate reliable statistics. To overcome this problem, a group of European banks has decided to pool its credit data. They formed the Pan-European Credit Data Consortium (PECDC) and hired Algorithmics to provide a platform and services for the collection, processing and delivery of the data.
In its first full pooling exercise in April 2006, 14 members of the consortium contributed data. For many banks the data will not only be useful for Basel II compliance, but will also help meet other business objectives. In fact, it was the desire to meet business rather than regulatory needs that prompted Dutch independent private merchant bank NIBC to initiate the data consortium project. Substantial growth of the investment activities is one of the key pillars of NIBC's strategy. In 2002, NIBC completed the first securitization of a shipping loan portfolio. But despite the fact that the portfolio was one of the best on the bank's loan portfolio, NIBC discovered that its value was not properly recognized in the market price of the collateralized loan obligation (CLO) it issued.
"Banks like to keep their cards close to their chests when it comes to their loan statistics, and the statistics of one bank is not enough to convince the rating agencies of the value of a portfolio," says Jeroen Batema, Head of Portfolio Management at NIBC. Meanwhile, trying to educate institutions about the behavior of shipping banks; how well they know and understand their market, know how to take advantage of the investment cycle, and use loan covenant structures to maximize returns, was a daunting task. The alternative was to provide information on the risk in the form of credit statistics; this being the type of information with which banks and other investors are more familiar. The problem was that NIBC's data alone would not offer sufficient credibility.
NIBC tried to interest other banks in pooling their data to produce combined loss statistics. The first step was to participate in an initiative of the Dutch Bankers Association, but apart from a project with one large bank to collect shipping statistics, the idea did not take off locally. "By end of 2003, we thought we should look abroad, as many of the asset classes in which we are active are in fact international. If we could find interest from banks in other countries, then we thought the large Dutch banks would probably follow," says Batema.
It was through contacts in the UK, France, and elsewhere, expressions of interest in the project came from a number of larger banks such as Barclays, Calyon, JPMorgan, and Royal Bank of Scotland. A meeting was set for June 2004 and took place two days after the central bank governors of the G10 countries endorsed the Basel II framework. The meeting evaluated the European credit data initiatives that were underway at the time, but most banks expressed reservations about the success of these projects. There was a strong feeling amongst the banks that they wanted to control any new project in which they participated. The best solution appeared to be a partnership between a consortium of banks that would contribute data, and a third-party data management specialist.
"The banks were interested in driving the project, but understood that it was not their core competence to pool data, but to be active in risk, so we decided to combine with a third-party that had this [data-pooling] expertise," says Batema. This approach allowed the banks to apply their credit expertise, and to be active in designing the data resources that they required for their business. With a clear objective of creating an inter-bank data pool of credit loss data, and agreement on the best way forward, the meeting formally established the Pan-European Credit Data Consortium, with Barclays, BNP Paribas, Dresdner Bank, NIBC and Royal Bank of Scotland forming the management committee, with Batema as chairman. A date in September was set for the next meeting, and a number of potential data management partners were invited to present proposals on pooling of LGD data, which the banks agreed should be their first priority.
Following the presentations, the PECDC selected Algorithmics as its partner. There were several reasons for this choice. Algorithmics already had over seven years experience of collecting loss data in the US through its North American Loan Loss Database. Algorithmics also had a good reputation as well as a profit incentive. One of the key criteria requirements of a partner was that it should understand the banks' intention to control the project. "Algorithmics understood best of all the potential partners that an industry-led initiative had the best chance of success," says Batema. Algorithmics agreed to abandon its own initiative that it already had underway to collect credit data in Europe, and adopted the PECDC business model for a bank-controlled data pool.
The banks decided to collect the LGD data from 1998 for eight asset classes: three regional-small- and medium-sized enterprise (SME), large corporates and real estate; and five global-project finance, commodities, shipping, aircraft and banks. The observation data would be collected at four points in the lifecycle of each loan-at the date of origination, one year before default, at default, and at resolution. Other information gathered includes the rating of the counterparty, the nature of the collateral and guarantees, the exposure at default (EAD) and value of the collateral and the details of each recovery cash flow following default. "Information on the cash flows between the moment of default and the moment of [resolution] is very important so that you can get a complete view of what kind of payments were made between the obligor and the bank concerning a loan, and the source of the payments, for example from the sale of collateral," says Batema.
Together with Algorithmics, the banks created a single template for all asset classes, greatly increasing the efficiency of data extraction and delivery to the pool. Once the data has been collected, individual exposure, loss, and recovery values are calculated for each loan. Aggregate statistics are then published to participating PECDC member banks by industry sector, borrower type, and country, in accordance with the consortium's rules.
The PECDC established the rules of participation. A bank has to contribute data from its European or global portfolios. It can choose which asset classes its sends data for, and from what period-although the latest acceptable start date was set at 2000. Banks will only receive back statistics for the classes and time periods to which it contributes data.
"It is very important for us to avoid free-riders because it will kill motivation," says Batema. A bank will not automatically qualify for membership if it applies. First, it has to prove that it can meet the consortium's standards in terms of quantity and quality of data. "A bank has to make a significant statistical contribution to the whole pool of data. It is a burden for large banks to deliver their data so they want to make sure that those who receive statistics from their data make a similar contribution," he says. The consortium relies on Algorithmics to interpret and implement the checks on quality and quantity, and to monitor and report on member banks' adherence to consortium standards and protocols.
The first pilot pooling occurred in November 2005, and two UK, two Dutch, and two German banks participated. Although a huge amount of data was collected, the PECDC was not able to publish very granular statistics, because there were fewer than three banks contributing from each country. Nevertheless, the exercise was an important achievement, proving the concept and the process, says Batema. The pilot was followed by the first production pooling of data in April 2006. This was a great success, with 18 banks delivering data representing more than 50 times more default observations on an annual basis than that available from the bond markets.
Most banks will use the data for benchmarking-checking that internal credit statistics are in line with the market. "As the quality of data improves, and as we get more data on defaulted loans, banks will probably use the statistics for calibrating their loss given default and probability of default models," says Batema. Banks are starting to collect more data on their loans than they used to, and so statistics in the future will be more appropriate for setting the parameters of models. For now, the PECDC has two categories for historical data-optional and required. However, for data from 2005 all fields will be compulsory.
"Inter-bank data pooling is an accurate, reliable, and cost-effective way of creating empirical data sets required to help banks estimate Basel II risk components," says Batema. Ultimately, the PECDC's data pooling could help commoditize credit risk. "The more that a bank can decrease the information asymmetry between itself and investors by giving them access to reliable credit statistics, whereby the investors are able to asses the risk they are taking, the more confident they will be about investing and, the more willing they will be to pay a higher price," Batema says.
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