Operational Risk

EVMTech is a pioneer in operational risk modeling. Our extensive experience combined with our know-how and research makes us a unique partner in developing proven and industry-leading solutions. We design and implement capital frameworks that meet internal and regulatory needs of our clients by covering the following areas:

• Development of robust LDA models for internal and external data
• Design and implementation of firm-wide scenario framework
• Scenario assessment and validation
• BEICF framework based on KRI, KCI and RCSA
• Integration of LDA, scenarios and BEICF
• Dependence modeling and calibration
• Risk aggregation and allocation
• Inclusion of insurance risk mitigation
• Use test including risk appetite, earning volatility, and performance measurement
• Sensitivity analysis
• Stress testing

EVMTech has implemented several high performance AMA models. Our software implementations are custom and reflect the specific modeling needs of our clients. Using our platform Risk# or our Risk Libraries, we are in a position to efficiently implement advanced and custom capital models. Our software solutions have been extensively tested and optimized for performance to meet modeling and sensitivity analysis requirements of our clients.

The implementation approach involves making application specifications, deciding on technology and architecture, coding and testing of the application, user acceptance test, and deployment. Our clients can benefit from service level agreements to ensure continuity in model enhancement and support.

EVMTech provides validation of operational risk frameworks with respect to their regulatory compliance and internal use test. Our deep knowledge in operational risk management combined with extensive experience in developing AMA and ICAAP models makes us an ideal partner for validation and benchmarking of operational risk models. The validation approach typically includes:

• Review of input data including definition of units of measure
• Review of all modeling steps, assumptions, and techniques used
• Review of model robustness and consistency of results over time
• Assessment of regulatory compliance in target jurisdictions
• Validation of model code and outputs
• Comparison with best practice approach
• Recommendations for model improvements