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:

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

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?

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

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

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

33. Identify mismatches

34. Feedback

35. ResolveMultiple Issue Rating Systems Multiple GL Systems

36. Thank You