Defence IT 2012 - Data Quality and Financial Services - Solvency II

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A short presentation to the Defence IT 2012 conference to provide them with a view of how data quality underpins new regulations in Financial Services, e.g. Solvency II. Data quality has a raised …

A short presentation to the Defence IT 2012 conference to provide them with a view of how data quality underpins new regulations in Financial Services, e.g. Solvency II. Data quality has a raised profile in financial services due to mandates from the European Union that companies must demonstrate good understanding and management of data quality.

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  • Solvency II IT & Data have analysed primary requirements for data governance and quality by referring to the legal Directive and the associated implementation and guidance material. New guidance is provided by regulators from time-to-time and will continue to appear into 2012.
  • Solvency II IT & Data have analysed primary requirements for data governance and quality by referring to the legal Directive and the associated implementation and guidance material. New guidance is provided by regulators from time-to-time and will continue to appear into 2012.

Transcript

  • 1. Data Quality Dimension of European Union Regulation - Solvency IIDavid Twaddell AGENDA - Regulatory Requirements - Corporate Strategy - Data Governance - Defining Data Quality - Data Quality Risk Assessment - Data Quality Management solution - Sample Dashboard
  • 2.  Understand the quality of data used in the calculation of technical provisions and in the internal model (completeness, accuracy and appropriateness - L2DIM Solvency II Art 14 and 220) Data Quality Requirements Understand the quality of Solvency II data using other indicators (Consistency, Transparency, Credibility, Comparability, Similarity – L2DIM Art various) Provide evidence of effective data governance (L2DIM Art 246) 04/19/12 (c) 2012 Inpreci – www.inpreci.com 2
  • 3. Vision:Global Insurer: • Business ownership of data• >50k employees worldwide • Efficient data quality procedures along• >$50B Gross Written Premium data lifecycle• >1,000 disparate applications • Trusted DQ operational and• Low confidence in data quality management information • Global solution • High confidence in data quality Strategy: Data • New Data Governance Framework (+ clear Procedures and DQ standards + Governance & communication strategy) • Central management of metadata Quality (including definition, lineage, ownership, etc.) Strategy • New data quality controls, re-using existing controls where possible. • Powerful data tools 04/19/12 (c) 2012 Inpreci – www.inpreci.com 3
  • 4.  Overall management and control Responsibilities and reporting lines Governance, Policies, Consistency across the enterprise Standards and Procedures Clear policies, standards and procedures Education important 4 (c) 2012 Inpreci – www.inpreci.com
  • 5. Defining Data Quality #1 – for example Completeness 2. Reconcile data received against data expected 3. Process to assess if data is available for all relevant model variables and risk modules Accuracy 6. Compare directly against the source (if available). 7. Check internal consistency and coherence of the received/output data against expected properties of the data such as age-range, standard deviation, number of outliers, and mean. 8. Compare with other data derived from the same source, or sources which are correlated. Appropriateness 11. Check consistency and reasonableness to identify outliers and gaps through comparison against known trends, historic data and external independent sources. 12. A definition and consistent application of the rules that govern the amount and nature of data used in the internal model.04/19/12 (c) 2012 Inpreci – www.inpreci.com 5
  • 6. Data Quality Risk Assessment – where to put controls? Define Materiality Actuaries attach a materiality level to data terms within data sets, based on how the data would affect the internal model, and define quantitative and qualitative tolerances for data quality. Document Lineage Document the target business process and the data flow from source to internal model for each dataset: Check Data Controls Identify existing control points that can be re-used. For each control point, document actual controls (i.e. governance controls and data quality checks applied.). Link controls to data quality indicators (completeness, accuracy, etc). Risk Assessment Documented procedure to assess risk along lineage. Assess effectiveness of controls. May recommend additional control points and additional governance and quality checks.04/19/12 (c) 2012 Inpreci – www.inpreci.com 6
  • 7. Component/Building Summary of capabilitiesBlock Name ~ Define business terms ~ Associate terms to Data Domain/OwnerData Definitions ~ Associate terms to Source, Uses, Characteristics ~ Associate terms to DQ rules, weights and metrics ~ Describe business processes that relate to the flow of data into SII.Data flows ~ Describe points of data governance ~ Describe risk assessment of business processes, as it relates to data quality ~ Stores data quality business rulesDQ Rule Repository ~ Stores data control technical rules ~ Extract, transform, load dataDQ Rules Engine ~ Apply data quality rules at appropriate points ~ Write out data quality measurements Data Quality ~ Allow business people to log data governance activitiesDQ Metrics Collector ~ Allow create/modify/delete/read of governance data ~ Provide only relevant questions to specific people Management ~ Maintain history of governance data ~ Qualitative and Quantitative assessments and metrics Architecture ~ Define Key Quality Indicators ~ Relate quality rules/measurements to KQIsDQ Aggregator/Scoring ~ Define data quality scoring methodology ~ Aggregate data quality measurements for reporting ~ Logical data models ComponentsDQ Storage ~ Physical data models ~ Physical storage ~ Present KQI dashboardDQ Dashboard and Reports ~ Drill-down to more detailed reports(Delivery) ~ Slide/dice by agreed dimensions ~ Provide views for Operations, Governance, Stewardship ~ Record data defects, prioritise 7DQ Defect Manager ~ Track defect resolution ~ Interface with data quality measurements (c) 2012 Inpreci – www.inpreci.com
  • 8. 8(c) 2012 Inpreci – www.inpreci.com
  • 9. - Data Risk Assessment- Data Quality- Data Security- Data Governance- Data Infrastructure- Data Complexity- Metadata- Policy, Standards & Procedures- Solutions