Operational Risk Consultancy

Gap Analysis

• Diagnostic review of operational risk management practices as compared to industry best practices and regulatory guidelines.

Policy & Procedure

• Analysis of key business processes, development of workflow charts, identification/grading of possible operational risk areas
• Assess and mitigate operational risk 
• Design control processes to assist in risk mitigation/minimisation

Risk Control Self-Assessment (RCSA)

• Design process-risk-control library to assist risk control self-assessment (RCSA)
• Design framework and template for RCSA

Key Risk Indicators (KRI)

• Design process flow and library for key risk indicators (KRI)
• Design KRI monitoring framework
  

Loss Data Management (LDM)

• Design framework to measure operational risk
• Design processes to analyse operational loss databases
• Design framework for loss data management

Model Validation

• Validate bank's internal models, etc to ensure compliance with advanced measurement approach
  

Operational Risk Consulting develops value-at-risk (VaR) models for operational risk measurement. It entails:

• Loss data collection across Basel business lines and loss event categories
• Loss data modeling
• Conduct "goodness of fit" test to assess strength of distribution
• Conduct simulation analysis
• Estimate operational loss VaR
• Back-testing to assess operational loss of VaR as against actual loss
• Operational risk capital charge estimation
• Estimate unexpected loss
• Scale-up factor, based on results of RCSA and KRI
• Value-at-risk model validation process includes:
  • Assessment of internal and external data (including proxy data elements) used in the model to ensure completeness
    
  • Analysis of model assumptions
  • Analysis of mathematical calculation and underlying risk factors
  • Back-testing of existing data
  • Testing VaR model based on hypothetical portfolios
  • Validation of model vis-a-vis benchmark/industry standard
  • Assessment of reporting to senior management as regards