Using actuarial skills to solve wider challenges across insurance businesses.

The role of actuarial teams is evolving. Skilled actuarial professionals aren’t just working within traditional actuarial roles but are also using their capability and judgment to help solve a broad range of complex challenges across a variety of business areas and sectors.

At Capita, we have a highly skilled team of actuaries and analysts who can use modelling to help our clients respond to challenges and fluctuations in demand. Our team recently demonstrated how actuarial data modelling could be used to help insurance companies to predict future customer demand and plan their resourcing more effectively. This insight could ultimately ensure that companies are able to better support their customers during a stressful and worrying time. 

To highlight how effective this modelling can be, we analysed data from the most challenging period in recent times – the coronavirus pandemic. The team used modelling to allow them to predict Covid deaths 1-2 weeks in advance - they demonstrate what data sources they used and what data model best predicted the future. This kind of modelling is vital, as the pandemic significantly increased demands on insurance claims teams. 

The report explores the models we used in more detail, the impact they could have on insurance claims team and the key questions to consider.
In this report we cover:

  • Predictive modelling during the coronavirus pandemic
  • Using predictive modelling to support claims teams
  • Comparing linear regression and random forest modelling
  • Our considerations and choices


The latest UK Customer Satisfaction Index for the Institute of Customer Service found that
14.9% of customers experienced a problem with an organisation in the first half of the year, the
highest rate since 2009, And almost a quarter of customers believe that some organisations have
used Covid-19 as an excuse for poor service *

*UK Customer Satisfaction Index (UKCSI), Institute of Customer Service - July 2021

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