5 minute read 21 Jun 2021
Software engineers programmer development coding a solution data

Should insurance be a business driven by data and analytics?

Authors
Corina Gruenenfelder

Financial Services Consulting | Risk & Actuarial | Switzerland

Enthusiastic quant and analytics leader. Passionate about sustainable finance and climate risk modelling. Diversity and inclusiveness advocate. Mountaineer and backcountry skier. Avid traveler.

Sabine Betz

EY Switzerland Insurance Sector Leader, Financial Services

Deep insurance knowledge from serving the industry for more than 25 years. Authority in risk quantification. Team player and female mentor. Dedicated mother of two teenagers. Movie fan.

5 minute read 21 Jun 2021

The EY Data Science in Insurance Survey underlines the sector’s commitment to data analytics across a range of applications.

In brief
  • Insurers are advancing their data science activities in a broad variety of areas as applications become more sophisticated.
  • Insurance companies plan to enhance many business processes using advanced analytics. 
  • The most relevant use cases include pricing and underwriting analytics as well as customer relationship management.

The availability of large data volumes is fueling the importance of quality analysis and insight in in the insurance business. As computing power grows, advanced analytics and machine learning methods are accelerating this trend and enabling stakeholders across the business to make better, data-driven business decisions.

The EY Data Science in Insurance Survey explores the current state of analytics strategy implementation in insurance companies and examines trends in data science and advanced analytics in the industry. The survey covered all types of insurance companies (life insurance, non-life insurance, health insurance and reinsurance) and was structured along four dimensions:

  • General perception of data science in the insurance industry
  • Data science functions
  • Use cases in data science
  • Objectives and challenges of advanced analytics

For the purposes of the survey, advanced analytics is broadly defined as the use of machine learning methods along the insurance value chain.

EY Data Science in Insurance survey

100%

of insurance companies believe that advanced analytics methods will play a very important role in the future.

Unsurprisingly, all participants believe that advanced analytics methods will play a very important role in the future. While 25% of the participating insurance companies already rely significantly on advanced analytics methods for their core business, 75%  of participants want to extend their application of analytics methods due to the future crucial role of analytics approaches. Swiss insurance companies identify the highest potential for advanced analytics solutions in claims and policy administration. Furthermore, advanced analytics solutions are also key to the underwriting and pricing as well as sales and distribution departments.

EY Data Science in Insurance survey

58%

of all companies surveyed have a dedicated data science team

Our survey reveals that data science teams vary in terms of size, allocation to specific departments, and available budget. Well over half (58%) of all companies surveyed have a dedicated data science team. Most of these teams range in size from 21-50 people. Among companies without a dedicated data science team, most employ up to five data scientists in different areas of the insurance company. Typically, data scientists are assigned to IT, marketing and sales, strategy, corporate services, operations, or other departments. Recognizing that advanced analytics will become increasingly important in the future, all participants have seen moderate or strong growth in their data science team over the past two years. Current trends show that 28% of participants plan to grow the data science team by up to 25%, and 14% by more than 25%. This is backed up by a budget of CHF 500,000 or more for data science initiatives in the next 12 months for 50% of participants.

As part of the survey, the participants were asked where they apply advanced analytics methods and in which areas the insurance company plans to further expand their analytics capabilities. Fifty-eight percent of participants already take advantage of advance analytics techniques in underwriting and pricing, as well as churn analytics (58%) and up-selling (58%). In the future, insurance companies plan to build new capabilities primarily for customer relationship management (CRM) (67%), but also continue to expand their analytics capabilities in underwriting and pricing (67%).

Insurance companies are embracing different machine learning techniques across a range of applications.

Most of the participating insurance companies apply different machine learning techniques, mainly including decision trees (e.g., random forests) and natural language processing (NLP) methods. However, other methods such as support vector machines (SVM), deep learning techniques (neural nets) and reinforcement learning are also implemented.

Interestingly, the most common applications are for text processing (58%) and model prediction (50%). Other use cases include outlier and pattern recognition (25%) and image recognition (25%). For the development of these applications, most insurance companies choose open source programming languages such as Python and R as their main development tools.

Insurance companies pursue various goals with the implementation of Data Science. One-third of all participants state that optimizing the customer targeting is their main goal in applying advanced analytics methods. Furthermore, 42% state that they aim for broader use of the models in the different business areas. However, insurance companies face several challenges when implementing data analytics methods. About half of all participants cited a lack of experience or expertise as one of the biggest challenges and 58% of all participants see integration into the existing IT landscape as one of the three biggest challenges.

EY’s uses analytical competency and business experience to support clients in the insurance sector. We help you connect the dots in the face of complex and disparate business issues, data and information. Using scenario modeling and analysis, we enhance the quality of data behind your decisions.  

Summary

EY’s survey clearly shows that insurance companies are preparing for a data-driven future by investing in their data science capabilities, scaling up teams and testing new methodologies to enable customer engagement. However, insurance companies also face many challenges, such as  a lack of experience, data availability and integration in the IT landscape. Choosing the right data and analytics strategy, governance and infrastructure is crucial for any insurer. 

About this article

Authors
Corina Gruenenfelder

Financial Services Consulting | Risk & Actuarial | Switzerland

Enthusiastic quant and analytics leader. Passionate about sustainable finance and climate risk modelling. Diversity and inclusiveness advocate. Mountaineer and backcountry skier. Avid traveler.

Sabine Betz

EY Switzerland Insurance Sector Leader, Financial Services

Deep insurance knowledge from serving the industry for more than 25 years. Authority in risk quantification. Team player and female mentor. Dedicated mother of two teenagers. Movie fan.