Algorithmics Announces Enhancements to North American Loan Loss Database

TORONTO, Nov. 17 /CNW/ - Algorithmics Incorporated today announced at its Algo Capital and Credit Forum in New York, a number of enhancements to be delivered to banks subscribing to its North American Loan Loss Database. Delivered by Algorithmics' Algo Credit Data Services team, the North American Loan Loss Database provides research, analytics and detailed, empirical commercial loan data pooled from leading market participants. Established in 1992, the North American Loan Loss Database is complemented by the company's recently announced role as the risk data services and analytics provider for the Pan-European Credit Data Consortium (PECDC), the first cross-border industry data pooling initiative for credit risk that provides Basel II compliance assistance to its members.

Based on award-winning Algo Suite technology, the enhanced offering includes expanded analytics and asset class coverage delivered via a dedicated, secure internet portal. In addition to commercial loan exposures to large corporates and small-medium enterprises, the service now also covers banks and specialized lending, including commercial real estate. An online data validation, mapping and collection facility provides unparalleled levels of data quality, throughput and availability. A methodology group, comprised of contributing banks' representatives and hosted by Algo Credit Data Services, will ensure the service content is driven 'by banks for banks.'

"The data, research and analysis we provide our clients is uniquely transparent and directly in response to their requirements concerning advanced
credit portfolio management, economic capital, pricing and liquidity, and of
course, Basel II," said Craig Van Ness, Senior Vice President responsible for
Algo Credit Data Services. "The enhancements announced today are the result of
a major investment programme in staff, methodology and technology. Algo Credit
Data Services provides a new industry standard for collecting, normalizing and
aggregating empirical credit exposure data for advanced credit portfolio

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