Analytics tools span a wide variety of technologies that allow users to examine huge quantities of data in a concise manner. There are increasingly sophisticated tools available nowadays to create dashboards that condense complex metrics into a view that one can read at a glance. The relevance to risk management is obvious - if the risk of an institution (or indeed, the economy) can be represented on a dashboard hotspots can quickly be identified (and presumably addressed).
Complex Event Processing (CEP) has recently been gaining popularity. CEP solutions are designed to capture and analyze data in real-time. For example, CEP may be employed on a funding desk to calculate intra-day liquidity and set off alerts when a threat to the funding situation was perceived.
The article also identifies a critical point underlying these capabilities - the analysis can only be as good as the data. The term "data integration" is used as a catchall phrase describing the ability to present a unified picture of a firm's risk exposures. When a firm wants to understand it's concentration to California mortages, it needs data integration. When it needs to understand it's outstanding exposures to Lehman Brothers the day after bankruptcy was declared, it needs data integration (mistakes can be costly as KFW found).
While the article does a good job tying BI to risk management, it doesn't go far enough in defining the subtleties of data integration in this crisis. One of the key learnings from the credit crisis is the need for an ability to create crisis scenarios and analyze the results. Scenario analysis depends on the ability to bring together data, impute realistic if extreme assumptions on the data and the analyze the results. The ability to perform effective scenario analysis (and it's close cousin - stress testing) is a fundamental capability that risk systems will need in the future.
On the face of it scenario analysis seems easy enough to do. Get the data together, make assumptions about where the factors will be when things go to hell and calculate the value of the portfolio. The devil, as they say, is in the details.
Recently I had a spirited converstation with a friend who ran BI for a large international bank that has had it's share of run-ins with the credit crisis. When we talked about stress-testing, he was vehement that the bank in fact had a robust stress testing regime but said that "it did not help". Why? Because the executives did not believe the stress scenarios. To understand why they did not believe, imagine being presented with a stress scenario where the ABX index was assumed to be 43, when in it's entire history it has only ever traded above 90. This scenario would suffer from, to put it mildly, a lack of credibility if the only frame of reference was the portfolio of CDOs themselves. In fact, out of embarrasment the risk manager would probably only build a "reasonable" stress scenario and write down the index to say 70. In fact, recent quotes for the ABX-HE-AAA 07-2 index (available at the Markit Partners web-site) show the index in the 40s.
Why the incredulity with what has, in fact, turned out to be realistic index values? Because there was no history of the index trading at the stressed levels, partly due to the youth of the index itself. On the other hand, what if the stress test was not done by imputing market prices on the higher degree structured products (such as CDOs and CDO-squareds) but on the underlying sub-prime mortgages? Imagine a system constructed with the portfolio of CDOs, the mortgage-backed securuities underlying them as well as the subprime mortgages underlying these securities. Further imagine that the system was constructed so that we could change the default rates of the mortgages, from which the prices of the MBS and CDOs would be automatically derived. Given enough evidence, a responsible stress test would in fact raise mortgage default rates to multiples of their historical values. The toxic concentration effects of these types of mortgages on the structured portfolio would have at least been recognized if not fully appreciated, giving both risk manaqers and business managers something to work with.
The central issue here is the ability to bring together diverse sets of data while preserving all the complexities involved - complex structured products such as CDOs, residential mortgage securities and underlying mortgages all need to be combined, taking into account the connections with one another. While data integration in a traditional sense is still relevant (e.g. bringing exposures together for reporting), in the case of complex structured products it needs to be taken to an entirely new and sophisticated level. For these products data integration necessitates data consolidation followed by painstakingly constructing all the relevant interconnections. Additionally these need to be maintained on an ongoing basis.
Data integration is being recognized as a key competency in the fight to gain a better understanding of the firm's risk - but it's not, as they say, "your father's data integration". Rather a whole new competency needs to be developed in all financial institutions. The good news is that I'm seeing some companies waking up to this need - let's hope for the sake of the industry that this is a trend that continues - in fact accelerates.
