May 3, 2013

The Power of In-Memory Analytics for Real-time Risk - Then and Now

Allen Whipple, Co-Founder and Managing Director of Quartet FS joins Numerix CMO, Jim Jockle to discuss the evolution of risk technology. Allen draws on the significant enhancements and capital markets use case for real-time data visualization, re-aggregation and reporting in solving an entirely new set of analytical problems – from risk management and credit monitoring, to position keeping and Big Data aggregation challenges.

Allen also expands on Quartet FS ActivePivot’s support for the instant analysis, aggregation and visualization of large volumes of complex and dynamic data for derivatives valuation, Market Risk and Counterparty Credit Risk calculations and the value this can have within a real-time enterprise analytics engine.

Weigh in and continue the conversation on Twitter @nxanalytics, LinkedIn, or in the comments section.


Video Transcript: The Power of Continuous Aggregation for Real-time Risk

Jim Jockle (Host): Hi welcome to Numerix Video Blog I’m Jim Jockle, and welcome to a new series on the evolution of risk technology. Joining me today is Allen Whipple, founder of Quartet FS, and also founder of a system that many people that are watching are very familiar with, Summit, so welcome Allen and thanks for coming on today. 

Allen Whipple (Guest): Thanks Jim.

Jockle: Why don’t we just start very quickly with the elevator pitch around Quartet FS, people may not be familiar, so why don’t you set the table for this discussion.

Whipple: Absolutely, so Quartet FS is a software company focused on capital market in-memory analytical problems, so we effectively produce an in-memory OLAP engine, which is a multi-dimensional analysis component used to solve a whole series of continuous big data analytical problems, from risk management, to credit monitoring, to positioning keeping, etc.

Jockle: So I think one of the things in thinking of evolution of risk management technology and where we are today, a lot of more technical terms are coming into our dialogue like OLAP engines, and in-memory, and cloud, and big data, and I think many of us just want to get back to trading. But, one of the questions I really want to start with was, so founder and original architect of Summit, and now Quartet FS with in-memory, data processing, slice dice, etc., give a little bit of the history of how this world is changing specifically around OTC, for technology.

Whipple: Sure, so if we go back to the 90’s, technology was about, at least from Summit’s prospective; we were producing monolithic enterprise systems, we had to do everything in one product, having all asset classes, front to back capability, the ability to do more and more within a single offering.

The margins of the derivative products were high enough that it could support that type of infrastructure. If we move forward to say the late 90’s, we actually had a technology shift where loosely coupled technologies became much more prevalent, so the ability to glue together disparity infrastructures, it became easier, cheaper and more practical.

Go forward a little bit, and we get into things like 2008, and the big credit crunch, and a transition from the complex derivatives, into more vanilla, a higher volume and a much larger concentration on risk mitigation as well as cost containment, so the ability to see technology and the kind of business in parallel evolving from what could support these massive costly infrastructures into something that’s much lighter weight, more cheaper to run, and much more easier to maintain, and more flexible in how it can adapt to changes in the technology needs, and business needs as we go forward. 

Jockle: So in terms of risk management today, and we can talk about the changing rules of environments and what not, but in your experience with CTO’s, what’s the top five check-list that they’re thinking about especially as it relates to their end user clients internally?

Whipple: Okay, wellwe have a lot of regulatory pressure that we’re seeing; we have a lot of adaptability. So the ability to be agile, so basically manipulate your technical environment or react quickly to changing infrastructures and changing the requirements. We have the ability to refine how easy it is to adapt more complex sets of analytics, and I’ll get into that in a moment, we certainlyhave this issue of being rationalizing the cost of ownership, and utilizing the fact that in-memory technologies, which we’ll talk about a little more, distributed architectures like the CAS kind of calculation environment is much more prevalent today, and much more cost effective to manage. So basically utilizing the fact that I have a very wide disperse set of both legacy and evolving sources of data, the ability to perform much more higher volume complex calculations on a continuous basis, and that continuous is actually important because we now have the technical means to do what use to take us hours, to run on an overnight basis, we can now do on a continuous basis.

I think legislation like Dodd Frank which is fundamental to redefining how we can assess things like credit calculations, so it distributed architecture that has a compute farm that’s able to instantaneously calculate a wide set of scenarios, and then an aggregation engine which ActivePivot effectively is, the ability to aggregate complex sets of disparate scenario results in a multidimensional way and analyze this in a sub-second kind of impact analysis measuring margin in calculations, pre-trade assessment in milliseconds as oppose to many minutes or hours it use to take us.

So these kinds of technologies are enabling the CTO’s to be much more reactive. This suite of technology didn’t even exist five years ago, so it’s been the evolution of in-memory architectures, the fact that memory that’s cheap, addressable, that’s opening up a new suite of problems that can be solved.

Jockle: You used the word cheap. We always throw around memory is cheap, but how cheap is it?

Whipple: That’s a very good question. The first point is that it’s addressable. We can actually move problems into an in-memory representation that even five years ago was impossible. So imagine being able to encapsulate the entire value at risk service of a large top tier bank into a single server, commodity server, something that’s a thirty-forty thousand dollar machine. This use to cost millions of dollars to try to even try to anticipate this years ago, when today, we can make this fit to commodity hardware, running an aggregation or an in-memory representation of that, that is instantaneous in solving these types of questions. So the old days of running big queries against massive databases is no longer a requirement, and the performance improvement is just orders of manufactures by capitalizing the fact that everything is represented on a in-memory representation.

Jockle: So you can manage cost, while gaining agility. Final question for this, in terms of central clearing and introduction swaps, the SEFs, how much impact is, is this the driver right now?

Whipple: What we’re doing together, the ability to represent that, in an aggregated in-memory representation, and apply marginal analytics on top of that, in milliseconds, when you’re talking about data setting problems, that can lead into the 5-10 terabytes of data, it used to take hours just to move the data around, let alone to perform the analytics, and now that we could the analytics calculation engines, and the incremental in-memory processing engines onto the same infrastructure, we effectively can now solve on a pre-trade basis, what use to take us hours to run, so the regulations and the CCP methodologies was mandated by the credit crunch and the regulatory changes that lead us to it, but the technical solution is to apply an incremental analytical environment that allows us to embed that into a pre-trade execution process, the other price into a trade, and that’s where a combination of a distributed calculation farm and an in-memory aggregation structure is the perfect synergy.

Jockle: Allen I want to thank you so much for your time today. The Quartet website is?

Whipple: QuartetFS.com

Jockle: Excellent and if anyone has questions for you can they connect on LinkedIn?

Whipple: LinkedIn is fine. Our website has a wealth of information and we look forward to continuing to work together.

Jockle: Alright Allen. Thank you so much for joining us today. I appreciated it. Again, let’s talk about it. Let’s keep this conversation going in dialogue. Join us on @nxanalytics on twitter and follow our blog. Comments, feedback!

We’re going to dive more into the topics of the evolution of risk management technology as we’re bringing front and middle office and back office operations together in a single platform and solving those real-time challenges. Thank you Allen and we’ll see you next time.

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