In South Africa, MATLAB® is the tool of choice for risk managers and analysts because of the ease with which they can build sophisticated risk models and integrate these models into their organisations. Opti-Num Solutions has trained risk managers and analysts around the country to use MATLAB to:
- Import historical data from multiple sources, including data feeds and databases
- Visualise results with intuitive graphs
- Fit distributions to historical data to estimate value at risk
- Calculate economic capital
- Build credit scorecards using logistic regression or other machine learning techniques
- Calculate credit risk and counterparty risk metrics, such as Loss Given Default (LGD) and Probability of Default (PD)
- Build operational risk models using internal and external loss data
- Build and deploy financial risk management tools to be used by other parts of the organisation, or by clients
We work with risk management professionals in the fields of market risk, credit risk, operational risk and liquidity risk. MATLAB is the analytics environment of choice in these areas because of its high-level language and built-in function libraries, which enable fast prototyping of models. MATLAB’s array-based environment lends itself to Monte Carlo simulation, allowing you to run and analyse hundreds of thousands of scenarios, with the option to utilise additional memory and processing power using Parallel Computing Toolbox. For those building applications to be used by other parts of the business, or by clients, MATLAB enables a streamlined workflow, allowing you to take a model from research to production, without the need for re-coding. Our consulting team has worked extensively in the risk management space, helping our customers build and optimise their risk models, and creating user-friendly graphical user interfaces as front-ends to complex risk models.
Discover how your organisation can adopt MATLAB.
Interested in MATLAB training? Complete our Risk Management Learning Path.
Learn how Italian bank Banche Popolari Unite monitors firm-specific and industry portfolio credit risk using a value-at-risk (VaR) model developed with MATLAB.