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Effective Data Management Strategies for Solvency II Compliance

Recent announcements by the European Parliament to delay Solvency II implementation deadlines to 2014 are in the headlines as European insurers are seen as being ill-prepared for the minimum capital requirements that will be brought in by Solvency II regulation.  A big reason for this stems from the data requirements and challenges companies face to ensure proper regulatory reporting and accurate risk calculations to guarantee compliance.

Complying with Solvency II has the same level of data challenges as did Basel II in the global banking industry as insurers set out to improve how they monitor and measure risk. Many are investing in risk scoring systems, data warehouses, business intelligence, and analytic applications to support their needs. Unfortunately, years of standalone business units, legacy underwriting, policy management, claims, and pricing systems, lack of proper technology to integrate, govern, and share critical data present critical business issues may further delay companies from meeting even these new deadlines.

Data is the lifeblood for Solvency compliance however companies continue to face a myriad of data issues that increase their risk of complying to the specified deadlines and increase operating costs including:

  •  Inadequate access to required data from legacy systems
  • Invalid and inconsistent client, policy, and counterparty reference or master data
  • Lack of common business terms and definitions of shared data across business lines
  • Duplicate, missing, and outdated information caused by human error, data corruption, and lack of data governance standards
  • Invalid and incompatible data formats and structures

Reducing the risks of non-compliance and the higher operating cost to comply with SII requires having an enterprise data integration strategy, data governance framework, and foundational technologies including data integration, data quality, metadata management, and master data management to access, govern, and share data to the systems and applications used for Solvency II.  

Firstly, an enterprise data integration strategy is important to reduce the costs and risks of integrating data for Solvency II compliance which defines key organizational and operating models, best practices, operational standards, and predefined processes for sourcing and sharing data. The adoption of Integration Competency Centers leveraging Lean Integration principles continues to grow across financial services as companies look to do more with less while increasing the value IT organizations deliver back to the business.  

Second, having a data governance framework and practice is equally important to ensure accountability and ownership of data by the business. Successful data governance frameworks include clear business objectives, data performance measurements, clear roles and responsibilities, data quality management processes, and policies that define what data is required and why to ensure alignment between risk management and IT professionals responsible for delivering the data to the systems and applications used for Solvency II compliance.

Lastly, having the right foundational data integration, data governance, and data management technologies is also important to address the complexities of accessing data from legacy systems across the enterprise to downstream risk data warehouses and risk calculation engines. Enabling an effective data governance framework requires capable data quality, metadata, and master/reference data management technology that reduces the cost and improves organizational effectiveness in managing data quality, consistency, and comprehension.  Are you prepared?

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This entry was posted in Data Quality, Financial Services, Governance, Risk and Compliance, Integration Competency Centers, Master Data Management and tagged , , , , , . Bookmark the permalink.

One Response to Effective Data Management Strategies for Solvency II Compliance

  1. Arvind Srivastava says:

    This is good stuff.

    Regards,
    Arvind

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