Beyond Theoretical Data Science

Significant changes in technology, regulation, markets, customer behaviour, the environment, and other global trends are influencing the actuarial department. The increasing availability of big data, the availability of technical data science skills, and the application thereof are changing how insights are being derived and continuing to shape the operating model of the actuarial department.

With a focus on application and use cases within an actuarial context, we performed benchmarking exercises to see how the insurance industry is utilising data science. This involved structured interviews with senior first-line actuarial department representatives from different life and non-life insurance organisations in the United Kingdom, South Africa, Belgium, Luxembourg, and Switzerland.

We investigated the strategy and the operating model within which data science is used, including the types of tools and techniques being used. And within our benchmarking exercise, we included themes around the types of data, the technical nature of machine learning techniques and software being used, as well as wider considerations, such as risks, risk management, governance, and ethics related to data science.

We also checked the trends impacting the skill set required by those working within data science and the barriers to adopting it. We interviewed representatives from first-line actuarial departments, mainly the Heads of Actuarial Reporting and Pricing Departments, Heads of Actuarial Systems, and Heads of Actuarial Transformation and Strategy, including direct insurance organisations and group entities.

In this webinar, we summarise the findings from this actuarial data science benchmarking exercise. Furthermore, we explore actionable steps and recommendations to optimise the use of data science within insurance and non-insurance industries. We also explore the changes that we are expecting to see in order to extract value and how those changes could be managed and implemented.

Part I. Introduction to benchmarking exercise performed

Part II. How do insurers create value using data science? What are the most deployed data science use cases in insurance?

Part III. What is the level of maturity with regard to data?

Part IV. Which tools and techniques are deployed that enable the application of data science?

Part V. How to ensure optimal team performance when it comes to applying data science? Where are the upskilling opportunities?

Part VI. What are the main challenges and opportunities in adopting data science?

30th June 2021

• Anja Friedrich, Manager, Synpulse
• Valerie du Preez, Managing Director, Dupro
• Xavier Maréchal, CEO, reacfin