Operational Risk Evaluator

The Operational Risk Evaluator* offers

  • Support for multiple operational risk management frameworks
    1. Risk control self-assessment (RCSA) 
    2. Key risk indicators (KRI) 
    3. Loss data capture
    4. Loss data modelling
    5. Issue and action planning
  • Enables self-assessment across geographically-dispersed risk entities based on centrally-defined framework
  • Enables configuration and monitoring of KRI module at granular and aggregated levels
  • Captures and analyses loss events
  • Facilitates estimation of operational value-at-risk
  • Calculates regulatory and economic capital for operational risk

Technology features

 
  • Web-based software
  • Easily configurable
  • User-based access controls

Value-added features

 

  • Built-in statistical approaches for rating model
  • Audit trail of rating changes
  • Ability to develop institutional knowledge via access
    to scores of all rated companies
  • Ability to customise financial templates

 

* Modules can be procured separately or as a comprehensive package.

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Operational Risk Consultancy

CRISIL Risk Solutions reviews, recommends and designs operational risk management frameworks.

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

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