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SAS Rolls Out Enterprise Risk Management Software

Intended to handle stress tests, credit and market risk calculations, and to provide an aggregate view of risk across the enterprise.

To help banks calculate risk and capital requirements across the enterprise and perform stress tests, SAS today is introducing SAS Risk Management for Banking.

SAS says that by using this software, which includes data management, analytics and reporting, organizations can measure exposure and risk across all risk types and books of business. The software helps banks measure and model asset and liability, market, credit and firm-wide risk within an auditable environment.

A fundamental design goal for SAS Risk Management for Banking was a common risk platform for all applications, placing integration at the core. A banking-specific data model, SAS Detail Data Store for Banking, is the single source of all information for the risk data warehouse.

The SAS Business Analytics Framework supports reporting requirements for senior management, regulatory compliance or business unit performance. SAS says its solution integrates easily with third-party risk software.

SAS Risk Management for Banking comprises four integrated applications:

* SAS Asset and Liability Management for Banking — Valuates traditional balance-sheet instruments, such as loans and deposits, and associated (off-balance-sheet) hedges, factoring in embedded options, such as prepayment and withdrawal, as well as credit risk, liquidity risk, etc.

* SAS Credit Risk for Banking — Calculates and stress-tests credit exposures, taking into account the effect of netting, collateral and margining, as well as credit derivatives book.

* SAS Market Risk for Banking — Valuates complex market instruments, perform stress tests and calculate Value at Risk (VaR), expected shortfall and other risks using various methods, including historical simulation, covariance simulation, analytical models and advanced, user-defined models.

* SAS Firmwide Risk for Banking — Calculates the firm's aggregate risk using correlation matrices or correlated copula aggregations of marginal risk distributions.

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