Capgemini Helps Clients Achieve Basel II Compliance and Deliver Economic Capital, Risk, and Valuation Models

Capgemini’s clients, which include every leading Dutch bank as well as many other banks throughout Europe, can hold loan portfolios worth several hundred billion euros and comprising thousands of individual loans. To accurately assess credit risk and ensure adequate regulatory and economic capital, Capgemini must take into account the probability of default, loss given default, and exposure at default for each loan, as well as the concentration of the loans in various sectors and regions.

“The problems we are working on do not have analytical solutions,” says Dr. Folpmers. “Instead, we must conduct Monte Carlo simulations for a wide range of market conditions, portfolios, and time frames.”

The banks need to understand the analysis results to make informed business decisions. Because risk management directors do not necessarily have technical backgrounds, Capgemini needed to visualize results for their clients. “For instance, if a portfolio is heavily weighted in energy, we can show them how the level of concentration increases their capital requirement,” Folpmers explains.

Capgemini consultants used MATLAB^® and companion toolboxes to implement valuation models and risk models that enable their client banks to simulate risk scenarios and comply with Basel II standards. Many of the consultants have backgrounds in fields such as econometrics or physics, and typically learned MATLAB in college.

Capgemini accessed client financial data from Teradata products and other data warehouses using Database Toolbox™. To ensure that the data was clean and complete, the team used MATLAB to identify outliers, compute missing values, and exclude invalid values.

The consultants reused components they had developed in MATLAB to implement economic capital models for different banks. For some components, such as copula modeling, they incorporated techniques and code examples downloaded from MATLAB Central.

To calculate economic capital, Capgemini performed Monte Carlo simulations in MATLAB to estimate an empirical distribution of losses. The team used histograms and other graphical tools in MATLAB to help clients visualize risks and capital requirements, and to demonstrate that the simulations had converged, yielding stable results. MATLAB is also used to implement integrated approaches that combine credit, interest-rate, operational, and other risks across client organizations.

For these and other risk projects, the team used Financial Toolbox™ to apply Markowitz and Black-Scholes models to their analysis. With Statistics and Machine Learning Toolbox™, they incorporated cluster analysis, principal component analysis, copula analysis, multivariate hypothesis testing, and statistical validation.

For clients who do not have MATLAB installed, Capgemini used MATLAB Compiler™ to create standalone executables. In one instance, Capgemini generated an executable file that its customer incorporated into a graphical risk management dashboard.