1. Solvency II & DataWhy Data Quality Technology is a Requirement to meet the Solvency II Data ChallengeColin Rickard – Managing Director, DataFlux Europe

2. Financial Services Authority - Solvency II Internal Model Approval Process Thematic review findings February 2011 3.15 Data quality: Few firms provided sufficient evidence to show that data used in their internal model was accurate, complete and appropriate. The Directive text requires data used for the internal model to be accurate, complete and appropriate and that an assessment of data quality should be an integral part of the firm's model validation activity. EIOPA's advice for Level 2 Implementing Measures on Tests and Standards for Internal Model Approval require firms to adopt a data policy that includes a requirement for the firm to specify its concept of data quality. Note that this also holds true for those thinking of adopting the standard model approach. What is the Solvency II Data Challenge? 2

3. The review suggests that progress has been focused on modelling and reporting at the expense of Data Quality/Integrity 6.7 Data quality is therefore a key area for the successful introduction of Solvency II. Most of the firms we observed have overstated their current level of preparedness against Solvency II criteria. Those firms that assessed their preparations as well advanced were generally found to have taken credit for work planned or envisioned as part of their Solvency II implementation projects, but not yet done. It is important that firms ensure they have the resources to meet the challenges of documentation for data management purposes and the ensuing data governance requirements under Solvency II. IMPLICATION 1 – The industry has not yet invested in Data Governance measures or has not yet recognised the key importance of this area. Why is the Regulator concerned 3

4. Embed Data Quality/Integrity monitoring into Business As Usual 6.11 Firms have started to understand the need to have dedicated resources to oversee data management and data quality across the whole firm. While there might be single accountability, it is impractical to expect one person to take responsibility for a firm's whole data policy. Instead, a more practical framework would include several 'data experts' or 'data custodians' throughout the firm as necessary to support the firm's data policies and data framework. 6.8 Similarly, firms should consider their overall strategy to data management and data quality. If their current approach is uncoordinated, a more structured solution may be appropriate given the importance of this area for model approval. IMPLICATION 2 – Insurers should budget for a corporate Data Governance unit that will require dedicated people, data governance process and appropriate technology. What is the Regulator trying to achieve 4

5. Question the spreadsheet culture which pervades Financial Services 6.9 In many firms, spreadsheets provide a key area of risk, because they are typically not owned by IT, but by other business or control areas, such as the actuarial function. They may not be subject to the same general IT controls as the firms' formal IT systems (i.e. change controls, disaster recovery planning, security etc) and firms need to develop a control system around this. IMPLICATION 3 – It will not be acceptable to house key business data items in an uncontrollable spreadsheet environment. Data needs to be subject to the new Data Governance unit policy and procedures to ensure integrity and transparency. What is the Regulator trying to achieve 5

6. Ensure that Data Quality/Integrity is owned at Board level 6.10 We witnessed little challenge or discussion on data quality at board level. We expect issues and reporting on data governance to find a regular place within board and committee discussions. Firms need to ensure that adequate and up-to-date quality management information is produced. It is important that the board has the necessary skills to ask probing questions. IMPLICATION 4 - KPIs should be regularly (monthly?) available at board level. Accuracy, Completeness and Appropriateness scores must be defined, benchmarked and tracked over time. Ability to drill from KPI scores, through business rules and into underlying data exceptions is a must have capability. What is the Regulator trying to achieve 6

7. Ensure that Data Quality/Integrity is owned at Board level 6.10 We witnessed little challenge or discussion on data quality at board level. We expect issues and reporting on data governance to find a regular place within board and committee discussions. Firms need to ensure that adequate and up-to-date quality management information is produced. It is important that the board has the necessary skills to ask probing questions. IMPLICATION 4 - KPIs should be regularly (monthly?) available at board level. Accuracy, Completeness and Appropriateness scores must be defined, benchmarked and tracked over time. Ability to drill from KPI scores, through business rules and into underlying data exceptions is a must have capability. What is the Regulator trying to achieve 7

8. Yesterday – Basel II Did not embed any requirements on Data Quality/Integrity Reporting focused Today – Solvency II Specific language regarding Data Quality/Integrity Accurate, Complete & Appropriate concepts not yet fully defined Tomorrow MiFID II, consultation paper published and contains same Data Quality measures as Solvency II Basel III, likely to contain the same Solvency III, likely to build on Data Governance concepts and may seek to further define Accuracy, Completeness and Appropriateness Dodd Frank likely to have a similar impact on US FS A Data Governance theme is being stitched into the fabric of all Financial Services regulation What does the Future hold? 8

9. Dashboarding KPIs Business Rules Monitoring Auditable Data Management Process Transparency and drill down KPIs Business Rules Data What are the underlying Technology requirements 9

10. KPI Dashboarding 10

11. Leading business insurer to use DataFlux technology to improve the accuracy of data across its European operations to support better business decision-making and operational efficiency while meeting Solvency II reporting requirements London, U.K. (29 September 2010) – DataFlux, a leading provider of data management solutions, today announced that QBE, a business insurance specialist with operations in 18 European markets, has selected DataFlux technology to help it improve the quality of data within its European data warehouse and to enhance its data migration process for systems consolidation. QBE will use DataFlux technology to standardise, improve and control data relating to its network of partner brokers, policies, claims and direct enterprise customer base. These improvements will enable QBE management to trust the results of data analysis and allow them to make improved business decisions based on more accurate data. QBE European Operations Selects DataFlux to Improve the Value of its Corporate Information 11

12. Commercial property insurer selects DataFlux technology to meet data-related reporting requirements of the Solvency II Directive. London, U.K. — DataFlux, a leading provider of data management solutions, today announced that Ecclesiastical Insurance Group, a commercial insurance specialist, has selected DataFlux technology to support the implementation of its data management programme. This initiative will help enable compliance with the Solvency II Directive data requirements and improve operational efficiency.The DataFlux Data Management Platform will be deployed to help control the integrity of data and will provide Ecclesiastical with the means to comprehensively govern its data. The implementation will enable Ecclesiastical to establish a process for monitoring and reporting on the quality of its business data over time, allowing the company to provide the business and regulators with intuitive, auditable metric-based reports. Ecclesiastical Insurance Group Selects DataFlux for Solvency II Data Management Implementation 12

13. Recognized by Analysts as the market-leader2010 Magic Quadrant for DQ Tools The Magic Quadrant is copyrighted 2008 by Gartner, Inc. and is reused with permission. The Magic Quadrant is a graphical representation of a marketplace at and for a specific time period. It depicts Gartner’s analysis of how certain vendors measure against criteria for that marketplace, as defined by Gartner. Gartner does not endorse any vendor, product or service depicted in the Magic Quadrant, and does not advise technology users to select only those vendors placed in the “Leaders” quadrant. The Magic Quadrant is intended solely as a research tool, and is not meant to be a specific guide to action. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. This Magic Quadrant graphic was published by Gartner, Inc. as part of a larger research note and should be evaluated in the context of the entire report. The Gartner report is available upon request from DataFlux.

14. THANKS FOR TAKING THE TIME TO VIEW THIS PRESENTATION!If you want to discuss or know more please feel free to contact me via LinkedInColin Rickard – Managing Director, DataFlux Europe