Financial Services Data Management

Underlying all Risk Management and Compliance issues is the need to address information and data-related issues. Capgemini’s Data Management framework helps manage your entire data lifecycle including customer, product, compliance, and risk and investment data.

The top data-related issues faced by institutions today include:
 

  • Data is trapped in silos, e.g., after mergers & acquisitions, hiding firm-wide risk accumulations (and potential portfolio off-sets).
  • Inconsistent valuations and reference data exist across different parts of the firm.
  • Few standards have been established for data (e.g., Are there clearly defined definitions/standards for risks/risk types?)
  • Data-governance models are often inadequate (e.g., who owns data and updates? Who verifies data? What policies ensure adherence to protocols?)
  • Risk systems do not allow for proper analysis of firm-wide exposure across risk dimensions, counterparties, etc.
  • Models generate incorrect forecasting of potential outcomes (e.g., models are too backward-looking and not dynamic

Our Data Management framework provides a proven foundation for your Enterprise Risk information system which helps manage your entire data lifecycle. Capgemini’s Data Management offerings address:
 

  • Business Information Strategy, Architecture & Assessment
  • Information Management & Data Integration through offerings for warehousing and reporting
  • Metadata Management
  • Master Data Management including all customer, product, compliance , risk and investment data
  • Data Governance & Stewardship
  • Compliance & Data Privacy through Data Classification and Cleansing services

Learn more about our Data Management services.

The top data-related issues faced by institutions today include:
 

  • Data is trapped in silos, e.g., after mergers & acquisitions, hiding firm-wide risk accumulations (and potential portfolio off-sets).
  • Inconsistent valuations and reference data exist across different parts of the firm.
  • Few standards have been established for data (e.g., Are there clearly defined definitions/standards for risks/risk types?)
  • Data-governance models are often inadequate (e.g., who owns data and updates? Who verifies data? What policies ensure adherence to protocols?)
  • Risk systems do not allow for proper analysis of firm-wide exposure across risk dimensions, counterparties, etc.
  • Models generate incorrect forecasting of potential outcomes (e.g., models are too backward-looking and not dynamic
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