S&P Capital IQ Risk Solutions Signs Standard Bank South Africa

Johannesburg - 1 November 2011

S&P Capital IQ Risk Solutions today signed an agreement to provide its default recovery data and Loss Given Default (LGD) model to the South African operations of Standard Bank. The agreement includes access to the LossStats recovery database and S&P Capital IQ Risk Solutions’ market-leading LGD modeling framework.

S&P Capital IQ’s recovery data and modelling allows banks, analysts and investors to estimate Loss Given Default across exposures backed by a variety of collateral. Coverage includes a wide range of industries from the most senior to most junior tranches of debt.

“Risk Solutions provides us with another tool to use in our risk management framework,” says Henry Edwards, Head of CIB Model Development, Standard Bank. “We see the partnership as key for refinement of our risk measurement and management of our emerging market and African portfolios, where data is limited and more reliance is placed on expert models.”

“In leveraging our well known LossStats Database of publicly-available loss data, we are able to help institutions enhance their internal credit and exposure management processes with loss estimates based on sound analysis of both ultimate recovery and trading prices. The LGD models represent an effective and robust solution to cases where an insufficient amount of data makes it difficult to build a statistical model, and clients that have adopted these models have received Advanced Internal Ratings Based (AIRB) approval by several regulators.” says Sunil Rajan, Director at S&P Capital IQ Risk Solutions EMEA. “This can provide significant benefits, as Basel II encourages the adoption of datasets and modelling capabilities developed by credit assessment institutions.”

S&P Capital IQ Risk Solutions offers a comprehensive set of Loss Given Default and Exposure at Default data, models, and services and has become a global leader in managing default and loss data consortia for banks that need to supplement their internal data.

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