Capital Stress Testing: Credit Suisse

Credit SuisseGroup is a is a global investment bank and financial services firm founded and based in Switzerland. It advices clients in all aspects of finance, across the globe and around the clock.

Importance Of

Stress Testing

Since the financial crisis of 2008, banks have been under additional pressure to ensure that they are compliant with the regulations required for stress tests. The idea behind stress testing is to ensure that the bank has enough cash in reserve in the case of a catastrophic market failure. Specifically, banks are mandated to move to a VaR (Value at Risk) model to an FRTB (Fundamental Review of the Trading Book) model. This FRTB model is much more stringent and forces a review on the desk level of the trading positions.


The client we were working with wanted to use reinforcement learning – the same type of learning that was used in game engines such as Alpha Go. They wanted an algorithm developed that could detect the “state” of the market and prompt traders to act accordingly based on historical data.

The client needed to have a system where they could run the FRTB tests required by the government. Due to the complexity of such a system, it was important to not just understand the underlying mathematics behind the models but to also ensure that the testing tools developed were compliant with the requirements.

Our Approach

DataSpartan provided a custom platform that was tailored to the nuances of the client data. Multiples inputs had to be taken into account such as the Volker metrics in compliance with the Dodd Frank act. Custom APIs needed to be built to allow the in house developers to build team specific functionality on top of the platform. Because cross-team data was required, a standarised data capturing tool was built to ensure that teams provided the system with an input that was compliant and made data integration into the calculation engine much easier.

Our Results

The client was satisfied with the work and is using the system internally for stress testing purposes. When it was finished, the system could properly report on tests on a daily and weekly basis as well as flag any activity that was unusual. The system was clearly documented and handed off to an offshore development team to assist with maintenance and feature implementation with DataSpartan still having oversight for the most technically difficult challenges.