Data strategies for risk management
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Effective data strategies are important for risk management. Surveys from 2008 and 2010 show that companies have made progress in clearly defining objectives and risk appetite, and better integrating risk management into strategic planning. Companies are also moving from defensive to more proactive risk management aimed at business value. Technology has enabled more automated risk identification, analysis, quantification, reporting and monitoring. Experts emphasize the importance of board commitment, dedicated executive leadership, a risk-aware culture, stakeholder engagement, transparency, and using risk data to inform decision-making rather than just avoid risks. Both internal and external data from multiple sources are needed for risk management, but data quality, governance, availability and standards must be addressed.
Data strategies for risk management
- 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 - IQuestion: To what extent do you agree or disagree with the following statements regarding ERM?20082010Take AwayCompanies 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 2008Source: 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?20082010Take AwayCompanies 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 forcesSource: 2008 and 2010 Treasury and Risk ERM Survey
- 8. Survey Results - IIIQuestion: To what extent is technology used to enable the following elements of the risk management process?20082010Take-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 2010For 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 valueSource: 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 FIExternal ReportingInternal ReportingUsersFEDSECOTSFDICT&FBOARDALCOCRMPCDATA TO IN FORMATIONBasel II/IIIInvestor RelationsEconomic CapitalBudgeting & ControlCustomer & ProductProfitabilityFTPBudget & ForecastOps RiskCreditALMMarketBusiness CalculatorsIntegrated Risk EnginesDATAGuarantorMarketCollateralContractREFCustomerSourcesProductExternal WebsitesGLIn-HouseRating
- 22. Typical Pain Points – A Business User’s perspective
- 23. Primary Attributes of Quality DataAccurateFit For UseTimelyCompleteDATA
- 25. Data management approach for effective Risk management Combination of top- down and bottom-up approachData availability in various source systems - risk, financeOwnership of data problemsDefining Target StateMethodologyPillar II, Basel III, liquidity risk and stress testingData classification issuesEstablish data governance frameworkIdentifying periodicity, latency issues and data flowSourcing of DataPre-cursor to ImplementationOrganization StructureData RequirementsImpact of non-availability of data Data standards/ definition/MetadataIdentifying appropriate data sources/owners of dataDesign of data architectureData GovernanceData quality assessmentPolicies, processes and standardsData Gaps, reconciliation Roadmap for implementation
- 26. Data Quality SolutionA combination of Business Processes/Methodology and Data Quality ToolsCollateral /Limit Management SystemsTransaction SystemsMultiple Customer Information StoresRisk & Reporting EnginesIdentify data sources
- 27. Identify data source champions
- 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 dataData WarehouseMultiple Product Information StoresReconciliation Process
- 34. Feedback
- 35. ResolveMultiple Issue Rating SystemsMultiple GL Systems
- 36. Thank You