Risk Management Toolbox™ provides functions and interactive workflows for mathematical modeling and simulation of credit, insurance, and market risk. You can perform lifetime credit modeling of probabilities of default (PD), exposure at default (EAD), and loss given default (LGD), as well as expected credit loss (ECL) calculations. You can assess corporate and consumer credit risk, create credit scorecards, estimate probabilities of default, perform credit portfolio analysis, and backtest models to assess potential for financial loss. The toolbox lets you identify important scorecard variables using the predictor screening tools and use the Binning Explorer app to automatically or manually bin variables for credit scorecards. It also includes mortality and unpaid claims models to quantify and analyze insurance risk. Market risk can be assessed with backtesting and simulation tools to evaluate value-at-risk (VaR) and expected shortfall (ES).
Consumer Credit Risk Modeling
Create and analyze credit scorecards, perform predictor screening, explore fairness metrics, conduct stress tests, and model probabilities of default (PD).
Corporate Credit Risk Modeling
Analyze corporate default probabilities, simulate credit portfolio value changes due to credit rating migrations and defaults, identify concentration risks, and calculate regulatory capital requirements.
Backtesting Models for Market Risk
Assess the accuracy of value-at-risk (VaR) and expected shortfall (ES) models.
Lifetime Models for Probability of Default (PD)
Estimate probability of default based upon lifetime analysis with macroeconomic scenarios using MATLAB®. PD models include Logistic, Probit, and Cox.
Loss Given Default (LGD) Models
Estimate loss reserves using regression and Tobit models.
Exposure at Default (EAD) Models
Predict the amount of loss exposure for a creditor when a debtor defaults on a loan using regression and Tobit models.
Insurance Risk Modeling
Calculate the risk of loss arising from mortality and unpaid claims. Estimate ultimate claims using the chain ladder bootstrap method.
“Some statistical tools can handle credit scoring models based on multivariate statistics or logistic regression, but are not well-suited to the advanced economic capital models needed for Basel II. With its computational power, matrix infrastructure, and ability to perform Monte Carlo simulations, MATLAB gives us a competitive advantage in performing complex risk analyses.”Dr. Marco Folpmers, Capgemini
Are You a Student?
Your school may already provide access to MATLAB, Simulink, and add-on products through a campus-wide license.