Managing investment banking risk, Stephane Rio of Opensee

By Puja Sharma

Stephane Rio of Opensee explains how his firm’s platform meets Crédit Agricole CIB’s need to monitor risk more effectively

Stephane Rio, Founder and CEO of Opensee, spoke to IBS Intelligence about Crédit Agricole CIB’s decision to implement the firm’s low/no code, scalable, data analytics platform. On CACIB going live, he said: “We’re really excited to have reached this milestone for our platform. It’s the culmination of our close collaboration with the Crédit Agricole CIB team to integrate and calibrate the platform to suit their needs. Crédit Agricole CIB’s data divers are now fully equipped to embark on their data exploration journey with the freedom and flexibility to extract the value hidden in the depths of the bank’s data lakes.

“The bank’s requirement here was to take an innovative view and a consolidated approach to the way people monitor their P&L. Historically, in most banks, people were working on Excel on their desk, monitoring their position and if you were the manager of 3-4 desks then you would have to create your own view, which may not be consistent, and if you were the manager of the managers, you would again have another view. What was important as a first requirement, was to have the ability for everyone to use the same platform and to have a detailed view at both the desk and consolidated level.

“The second thing that was important was to have the ability to look at the risk from different angles. Typically, a trader would look at his or her market risk. I’m just going to take an example: a trader is going to look at interest rate risk – what is his delta risk on the euro curve per maturity. But if he is trying to understand some other angle to his risk like what are the counterparties that this risk is attributed to, he would not have that before hours or more because traders do not necessarily have easy access to this information.

[Delta is a risk metric that estimates the change in the price of a financial instrument for a one basis point variation.] “If you think about recent financial crises, suddenly it becomes important to know what risk you have against counterparty X or Y or Z because some of the bigger banks might have been in danger of collapsing. So, for the first time, users were having in real time access to different angles of their risk and were saying: ‘I am not just looking at my market risk, but I am looking at my market risk in the context of the counterparty with whom I traded.’ “That’s an example of the granularity and the depth of information that you could extract from our platform. It is also very important as well, to compare and have access to the history so when you are monitoring your risk, as you also want to know how it has behaved in the past, allowing you to extrapolate forward and compare with scenarios in the past.”

How is the bank implementing the platform?

“The roll out of Opensee platform is being done in steps by business types. The platform offers users the ability to bring computation to the data, because we have a integrated Python module where people can code what they own User Defined Functions. Typically, in such case this Python code is used for estimating your P&L. “For a simple desk, P&L is going to be just about multiplying data values times the market move. For more complex desks it is going to be a slightly more complex formula, so you need to adapt these calculations for each desk, and, of course, you can do much more than just monitoring P&Ls. Front-officers and analysts are being trained and gradually they will all be able to leverage Opensee platform. “Another example that is going to go live imminently is the ability for the FX option traders to monitor their risk real time.”

Is this simply all about understanding risk, managing risk?

“I like to summarise it as ‘explainability’. You can call it data explainability or risk explainability, but I think for everyone who produces risk numbers or has been sitting on data, the real next step is to fully understand all the details of where it is coming from because that is how you are going to manage risk. If you don’t understand exactly all the components, all the articulations of your risk, you are not going to be able to take the optimal decisions.

“Some of this risk may come as an output and if you don’t know where it is coming from it is very difficult to anticipate. One action that the users love about our platform is the ability not just to analyse the past but being able to anticipate the future by doing ‘what if’ simulations. So, they have their current live position and before they execute a trade, they can see what the impact of doing that trade would be on their risk, on their capital, or anything they are measuring.”

How important is scalability?

“Having a scalable solution is a very important element. Historically the solutions that were available for real time analytics were in memory, but memory has a number of limitations. The first is the data is transient so if there were any problems you were losing information. The second is that it is not very scalable. The underlying infrastructure is very expensive, and it is very difficult to distribute data on such infrastructure. “Our solution is deployed on standard hardware, typically on the cloud but it can also be on premise, we even have hybrid deployments. It is easily scalable just by adding more standard VMs. So, as you get more data you add a larger disk and, potentially, you add more CPUs and that is it! We have use cases which are really large with 10-plus terabytes with large banks, and other, typically with hedge funds or asset managers that start with more reasonable sizes.”