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Data strategies for risk management

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  • 1. Effective Data Strategies for Risk Management
  • 2. Top Nine Risks An FI Fears
  • 3. COSO’s Framework For Enterprise-wide Risk Management (ERM)
  • 4. How Has ERM Progressed?
    Surveys referred to:
  • 5. Survey Results - I
    Question: To what extent do you agree or disagree with the following statements regarding ERM?
    2008
    2010
    Take Away
    • Companies seem to have their objectives/risk appetite and tolerance clearly articulated more in 2010 than 2008
    • 6. Companies seem to have integrated risk management with their strategic planning process more in 2010 than 2008
    Source: 2008 and 2010 Treasury and Risk ERM Survey
  • 7. Survey Results - II
    Question: How important are each of the following in driving improvements in your company’s risk management programs or initiatives?
    2008
    2010
    Take Away
    • Companies seem to be moving away from defensive risk management practice to more pro-active risk management with more of them citing business value enhancement, decreased volatility in earnings, gaining competitive advantage amongst others as their prime driving forces
    Source: 2008 and 2010 Treasury and Risk ERM Survey
  • 8. Survey Results - III
    Question: To what extent is technology used to enable the following elements of the risk management process?
    2008
    2010
    Take-away:
    • Technology in 2010 has enabled more companies in their risk identification, analysis, quantification, reporting and monitoring than in 2008. But , if not designed, implemented and managed correctly, the technology used to measure risk will itself pose a major operational risk!!!
    • 9. Finally, ERM was said to have more room for improvement in 2008 and is definitely seen in positive light now despite a long recessionary period.
    Source: 2008 and 2010 Treasury and Risk ERM Survey
  • 10. After the crisis, what does the Industry believe about ERM?
  • 11. Extracts from an independent survey from 2010
    For a successful ERM Program, the following attributes were recognized to be critical:
    • Board level commitment to ERM is critical for successful decision making and for driving value.
    • 12. A dedicative executive in a senior level position who drives and facilitates the ERM process
    • 13. An ERM culture that encourages full engagement and accountability at all levels of the organization
    • 14. Engagement of all stakeholders in risk management strategy development and policy setting
    • 15. Transparency of risk communication
    • 16. Integration of financial and operational risk information into decision making
    • 17. Use of sophisticated quantification methods to understand risk
    • 18. Identification of new and emerging risk from internal data as well as from information from external providers
    • 19. A move from risk avoidance and mitigation to leveraging risk and risk management options to extract value
    Source: Global Enterprise Risk Management Survey, 2010, AON Analytics
  • 20. What do experts say?
    • “Objective of implementing an ERM is to have a unified platform that operates in a standard framework for risk management that covers all leading financial risks such as market, credit, liquidity and operational risk”
    • 21. “In the past, banks have done the business and then went about measuring risk; now, it is necessary to measure risk and use the knowledge to do the business.”
  • Sources and users of Data in an FI
    External Reporting
    Internal Reporting
    Users
    FED
    SEC
    OTS
    FDIC
    T&F
    BOARD
    ALCO
    CRM
    PC
    DATA TO IN FORMATION
    Basel II/III
    Investor Relations
    Economic Capital
    Budgeting & Control
    Customer & Product
    Profitability
    FTP
    Budget & Forecast
    Ops Risk
    Credit
    ALM
    Market
    Business Calculators
    Integrated Risk Engines
    DATA
    Guarantor
    Market
    Collateral
    Contract
    REF
    Customer
    Sources
    Product
    External Websites
    GL
    In-House
    Rating
  • 22. Typical Pain Points – A Business User’s perspective
  • 23. Primary Attributes of Quality Data
    Accurate
    Fit For Use
    Timely
    Complete
    DATA
  • 24. Data Quality Process
  • 25. Data management approach for effective Risk management
    Combination of top- down and bottom-up approach
    Data availability in various source systems - risk, finance
    Ownership of data problems
    Defining Target State
    Methodology
    Pillar II, Basel III, liquidity risk and stress testing
    Data classification issues
    Establish data governance framework
    Identifying periodicity, latency issues and data flow
    Sourcing of Data
    Pre-cursor to Implementation
    Organization Structure
    Data Requirements
    Impact of non-availability of data
    Data standards/ definition/
    Metadata
    Identifying appropriate data sources/owners of data
    Design of data architecture
    Data Governance
    Data quality assessment
    Policies, processes and standards
    Data Gaps,
    reconciliation
    Roadmap for implementation
  • 26. Data Quality Solution
    A combination of Business Processes/Methodology and Data Quality Tools
    Collateral /Limit Management Systems
    Transaction Systems
    Multiple Customer Information Stores
    Risk & Reporting Engines
    • Identify data sources
    • 27. Identify data source champions
    • 28. Profile data sources
    • 29. Identify Golden Source for each information domain
    • 30. Pick relevant attributes from other available sources
    • 31. Incorporate Data Quality checks
    • 32. Set up feedback systems for review of rejected data
    Data Warehouse
    Multiple Product Information Stores
    Multiple Issue Rating Systems
    Multiple GL Systems
  • 36. Thank You

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