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Pensions Data Solutions

We help our clients identify shortfalls in their electronic member data, resolve their data issues and achieve their business needs and objectives.

Pension scheme member data can often be incomplete which leads to complications in the payment of benefits and liability valuations. We help our clients identify shortfalls in their member data, resolve their data issues and achieve their business needs and objectives.

Improving member data is vital to pension scheme management for a number of reasons, including:

  • To improve administration efficiency while satisfying regulatory standards and good governance
  • To improve the accuracy of liability valuations and contribution recommendations by removing the requirement for the actuary to make assumptions about the data
  • To improve the accuracy of cash flow and liability projections
  • To ensure that other business objectives are not hampered by data issues
  • To help clients prepare optimal data for de-risking projects, including annuity purchase and voluntary member option projects, at the right time and, if relevant, at the best price.

Our approach is innovative and has the right blend of leading-edge technology and consulting value. We provide clients with clear recommendations tailored to their needs, a clear sense of direction and forward planning, drawing on our experiences of dealing with the more complex data cleansing challenges of both large and small pension arrangements. We take an independent approach that challenges the status quo and ensures clients realise tangible benefit and value for money.

One of the key pieces of software we use to deliver this solution is our Data Digitisation service. This is a technology-driven solution that identifies and extracts valuable data from inert files into optimal formats to unlock the potential in your data. We use our deep understanding of our clients’ business and their strategic direction within a four step process, starting with identifying what data is needed and where it can be found. The second step is to convert files such as paper, microfiche into a readable PDF file using Optical Character Recognition. The third step is to classify the content and group unordered documents into sets of the same type of document based on content. Lastly the fourth step is to extract the data required from PDF images into consistently formatted output. After the classification stage the client receives a digital ordered index of electronic files making it easy to search. The subsequent extraction stage provides a populated database of key information ready to use – this file can be any format.

Our experience in implementing de-risking projects give us an unrivalled insight into prioritising the data issues that truly impact on such exercises.

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