Model Development

• Development of appropriate corporate and retail risk models for portfolio credit risk management, including an estimate of asset correlations

Model Validation

• Development and validation of internal credit rating models for different categories of borrowers

Parameter Estimation

• Installation of data management processes and analytical methodologies for deriving default probabilities, transition matrix and loss given default statistics

Credit VaR Estimation

• Development of framework to estimate portfolio credit VaR based on underlying parameters, as well as inter-sector correlations

Asset Liability Management

• ALM monitoring and reporting framework        
• Liquidity and interest rate risk measurement and management framework
• Framework for stress testing and funds transfer pricing

Asset-liability management (ALM) consulting covers:

• Diagnostic review
  Review of organisation’s ALM-related processes, including risk management structure, underlying policies and procedures, and risk measurement and reporting frameworks.

• Risk assessment & measurement
  Assessing extent of liquidity risk, taking into account gap reports, liquidity coverage ratio, net stable funding ratio and funding portfolio mix.     

• Stress testing
  Includes scenario analysis and sensitivity testing to assess impact of macro-economic and institution-specific stress scenarios on liquidity and interest rate.

• Risk control & monitoring
  A limit management framework is designed, taking into account risk appetite and tolerance levels, as well as underlying and prospective portfolio mix.

• Risk-based decision-making
  Mainly is in the form of funds-transfer-pricing-related consulting, including methodology. In addition, the plan for capital allocation, taking into account the liquidity and interest rate risk impact, is defined.

Valuation

• Valuation of derivatives
• Valuation of other market instruments
• Treasury performance assessment and monitoring framework
  

MarketRisk Rating Value at Risk Model Development

• Assessment of internal and external data (including proxy data elements) used in the model to ensure completeness
• Analysis of model assumptions
• Analysis of mathematical calculations and underlying risk factors
• Back-testing of data (past one year) at varying confidence intervals and sub-portfolios
• Conducting tests on VaR model, based on hypothetical portfolios
• Validation of model vis-à-vis benchmark/industry standard models

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