Insurance companies

Data quality management for insurance companies

Regulatory pressures and fierce competition have made data quality management a core organisational requirement for insurers and, more importantly, a source of competitive advantage. If the reliability and quality of data is inconsistent it can be potentially misleading and faulty, resulting in harmful conclusions.

Insurance firms intending to seek internal models approval for calculating regulatory capital requirements under Solvency 2 face strict new model validation tests – and data quality assurance is a key part of this validation. Firms will have to demonstrate data quality controls and show the validity not only for input data used at calculation time, but also the underlying data that is used to support the statistical analysis on which the model is based.

Problems in data management often arise from poor definition of roles and responsibilities, and lack of senior management and/or steering committee sponsorship and buy in. Although data quality is not purely an IT issue, it also relies heavily on strong partnerships between business and technology functions.

Common weaknesses in data quality management include:

  • A lack of institutionalised data strategies and governance frameworks;
  • An absence of a vision for data management change;
  • A lack of performance targets or allocation of resources;
  • Data quality enhancement frameworks and policies developed in silos and not spanning across the functions or the various levels within an organisation.
  • Reply help organisations to improve data quality. Reply offerings, which are supported by a proprietary methodology, include:

  • Establishing a data management governance structure by defining roles and responsibilities, and developing and implementing data quality management strategy;
  • Developing, documenting and rolling-out data quality policies and standards;
  • Designing a common data architecture across the organisation, developing and promoting data quality awareness and communication plans, and defining data quality requirements and business rules (for data transformation);
  • Designing, implementing and monitoring operational data quality management procedures, testing and validating data quality requirements;
  • Analysing, profiling, measuring and monitoring data quality, setting data quality service levels, certifying and auditing data quality, identifying, escalating and resolving data quality issues;
  • Planning and conducting data cleansing programmes.


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