Beyond Theoretical Data Science
A benchmarking of actuarial departments’ practices and proposed recommendations based on industry research
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.
We performed benchmarking exercises which involved structured interviews with senior first line actuarial department representatives from different UK, South Africa, Belgium, Luxembourg and Switzerland life and non-life insurance organisations to investigate how the insurance industry is utilising data science, with a focus on application and use cases within an actuarial context. We investigated the strategy and the operating model within which data science is used including the types of tools and techniques being used.Within our benchmarking exercise we also included themes around the types of data; the technical nature of machine learning techniques and software being used; and wider considerations including risks, risk management, governance, and ethics related to data science.
We investigated trends impacting the skill set required by those working within Data Science and the barriers to adopting data science. We interviewed representatives from first line actuarial departments mainly with 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 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 regards 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