Risk Management Toolbox

Develop risk models and perform risk simulation

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).

What Is Risk Management Toolbox

Interactively bin data and export results into a credit scorecard.

Consumer Credit Risk Modeling

Create and analyze credit scorecards, perform predictor screening, explore fairness metrics, conduct stress tests, and model probabilities of default (PD).

Credit Scorecard Modeling Using the Binning Explorer App (6:17)

Documentation | Examples

Plot showing the stress testing scenarios for asymptotic single risk factor (ASRF) and Value at Risk (VaR).

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.

Calculating Basel capital requirements with the ASRF model – Live Script

Documentation | Examples

Plot showing Value-at-Risk violations over time for multiple models.

Backtesting Models for Market Risk

Assess the accuracy of value-at-risk (VaR) and expected shortfall (ES) models.

Value-at-Risk Estimation and Backtesting – LiveScript

Documentation | Examples

Diagram showing steps involved in calculating expected credit loss provisions.

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.

Economic Scenarios and Expected Credit Loss Calculations – LiveScript

Documentation | Examples

ROC plot comparing Tobit model to group means model.

Loss Given Default (LGD) Models

Estimate loss reserves using regression and Tobit models.

Compare Tobit LGD Model to Benchmark Model – LiveScript

Documentation | Examples

Histogram of observed vs. predicted limit conversion factor (LCF) using an EAD regression model.

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.

Documentation | Examples

Plot of estimated ultimate claims from a development triangle model.

Insurance Risk Modeling

Calculate the risk of loss arising from mortality and unpaid claims. Estimate ultimate claims using the chain ladder bootstrap method.

Documentation | Examples

Product Resources:

Documentation Examples Videos Requirements Release notes

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

“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

View more customer stories