Analyze and manage risk associated with borrowing
Credit risk is the potential for a loss when a borrower cannot make payments as obligated to a lender. Credit risk is commonly measured and communicated as the likelihood or probability of an individual borrower’s default. Most lenders employ sophisticated models to analyze risk, rank customers, and decide on appropriate strategies for managing this risk.
Effective techniques for managing and analyzing risk include:
- Understanding the risk and nature of each counterparty
- Modeling credit scorecards
- Performing Monte Carlo simulations
- Simulating credit risk based on probability of default or credit migration matrix
- Mitigating risk using credit derivatives
- Analyzing scenarios to assess risk exposure arising from borrowing or lending.
For more information, see Statistics and Machine Learning Toolbox™, Financial Toolbox™, and Risk Management Toolbox™.
Examples and How To
- Credit Risk Modeling with MATLAB (53:09) - Webinar
- Using MATLAB for Risk Modelling: Two Practical Applications (38:20) - Webinar
- Credit Portfolio Simulation with MATLAB (25:44) - Webinar
- Banche Popolari Unite Analyzes Credit Risk - User Story
- Forecasting Corporate Default Rates - Example
- Modeling Correlated Defaults with Copulas - Example
- Counterparty Credit Risk and CVA - Example
Software Reference
- Getting Started with Risk Management Toolbox - Documentation
- Simulate Credit Rating Migration Risk - Documentation
- Credit Risk Analysis - Documentation
- Credit Scorecard Modeling Workflow - Documentation
- Estimate Transition Probabilities from Credit Ratings - Function
- Estimate Transition Probabilities from Preprocessed Credit Ratings Data - Function
- Bagged Decision Trees - Function
See also: Risk Management Toolbox, Statistics and Machine Learning Toolbox, Financial Toolbox, risk management, Monte Carlo simulation, counterparty credit risk, credit derivatives, credit scoring model, IFRS 9
