Aimed at a bank’s CRO and other ‘super-users’ such as quants, the package had an obvious coherence for banks and was formed into what is now known as the SAS Risk Management for Banking solution. Although Credit Risk for Banking (running the RWA calculations) had to date been delivered as a standalone function, SAS started to merge it fully into the Risk Management for Banking solution, ultimately giving SAS ‘a single, integrated architecture’. Under this heading, SAS now offers a range of risk functionality. In operation, each function of this unified offering is intended to look to the user like an individual application, but together creating a series of analytical events that can be run, reported on, distributed, pushed into Excel and so on. As an example, Rogers explained that running a copula calculation (so divining the dependence between random variables) for joint market risk and credit risk was possible because the ‘white box’ technological environment (ie. nothing is closed off to users), treats each event as ‘just another project’, whether firm-wide or separately. Users can ‘break into the process’ to check where the data came from, how it has been modified prior to being loaded, and run further enquiries on the results via the optional SAS BI stack. With the same Base SAS framework across the board, core developments have been rolled out to each environment. In risk management, in-memory processing, for example, while not specific to the space, has nonetheless been particularly effective in decreasing the time it takes to run calculations. Beyond the core architecture, the whole SAS concept


remains essentially a toolset where developments can be made in each application according to need. But in many verticals, with firms increasingly looking at exploring an holistic approach to risk, the idea of governance, risk and compliance (GRC) as a means of standardising risk management processes has gained currency in recent years. SAS announced its own Enterprise GRC offering in June 2010. This is basically an ‘all under one roof’ repository of GRC components covering a range of operational risks, political risks, legal risks and market risks and is intended to give risk managers, compliance officers, auditors and other business owners an overview of these elements so they may see how they relate to each other and the overarching business risk strategy. Although in use already (Swedish energy company,


Vattenfall, Europe’s fifth largest generator of electricity, was live), Rogers described the work as ‘a long-term project’ that would see the measurement and fraud side of analysis, for example, ‘gravitating towards being reported through a GRC structure’. He added ‘traditionally SAS never sat that close to the operational environment’, and so the vendor was still forming the requirements, mainly in the audit space and


126 Risk Management Systems & Suppliers Report | www.ibsintelligence.com


generalising operational risk. Using reporting tools, workflow tools and human interface technology that had not previously been a major part of the SAS offering, alongside aspects of the SAS BI stack, even ‘the non-quant or non-super user’ will be able to interact with the system. This, said Rogers, was all part of SAS’s plan to promote the concept of an enterprise-wide risk culture. In late 2011, SAS launched a real-time analytics solution specifically for large investment banks, targeted at traders as well as risk managers. Based on SAS’s own third generation complex event processing engine (CEP) – built over the previous 18 months – the vendor claimed it could collate and analyse more than one million market, position and risk data points per second and would deliver most large and complex trading institutions from the need to rely on over-night batch data and VaR models.


Duncan Ash, financial services marketing and strategy manager at SAS, explained at the press launch that knowing the level of exposure to certain events was vital (as shown by the Lehman Brothers experience) and if this cannot be seen with any degree of accuracy until the next day it may already be too late. Ash claimed that the mathematics behind position calculations was not complex in itself, but until the release of this solution ‘getting the data aggregated to that level, in real- time has been impossible’. SAS users would now be able to individually manipulate and respond to this data at the level of business line, asset class, currency and geography. By streaming huge volumes of data through the SAS


in-memory system, in addition to real-time stress testing and multiple scenario planning (so traders can make the appropriate trade as and when an event happens), Dale Stevens, head of capital markets for SAS UK, said the solution was also capable of running assumption tests (including macro- assumptions), modelling the correlation, price divergence and volatility of portfolios, and creating alerts when models need to be reviewed or ‘when they’re no long any good’. At the time of launch there were no confirmed takers.


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