Analyze and manage operational risk
Operational risk is the potential for a loss arising from people, processes, systems, or external events that influence a business function. In recent years a hot topic in financial services has been the development of operational risk recommendations and regulations for banking. The Basel Committee on Banking Supervision (BCBS) created the Basel Accords to provide definitions of formal techniques for the quantification of operational risk, credit risk, liquidity risk and market risk.
Until recently, according to Basel II, financial institutions could use a basic indicator approach, standardized approach, or advanced measurement approach (AMA) as a framework to quantify capital requirement for operational risk. Guidance has recently been updated to propose a standard measurement approach (SMA). Effective techniques for measuring and managing operational risk include:
- Assess operational risks using risk control self-assessment (RCSA) approach
- Build customized operational risk models based on the loss distribution of each type of risk
- Simulate operational loss using Monte Carlo simulations or copula-based simulations
- Analyze a variety of scenarios to assess risk exposure arising from financial activities exposed to operational risks
- Develop internal models to quantify operational risk
MathWorks is category leader for operational risk.
For more information, see MATLAB® and Statistics and Machine Learning Toolbox™.
Examples and How To
See also: risk management, Monte Carlo simulation, energy trading and risk management, fraud analytics, MathWorks Modelscape Governance, Modelscape