The Confluence of Actuaries and Data Science: Challenges and Opportunities

Many refer to actuaries as original data scientists. They work extensively with data to provide meaningful insights to the entire insurance ecosystem at large. The confluence of actuaries and data science aims to bring transformation by developing innovative products, lowering distribution costs, introducing better risk management practices, and ensuring dynamic reporting concerning local and international regulations.

Traditionally, actuaries used the Actuarial Control Cycle framework to identify problems, develop solutions, and monitor them, end to end. However, this confluence has led to the evolution of the “Actuarial Data Science Control Cycle.”

Figure 1: Framework at a glance

Challenges that come with this confluence

Some of the challenges faced by an insurance carrier are elaborated in Figure 2. 

Figure 2: Challenges faced by insurance carriers

How to leverage the confluence

Developing an operating model that integrates the two domains is the key to leveraging the best of both worlds.

• Data scientists are included in each department (actuarial, new business, underwriting, claims, policy servicing), which provides them with the business knowledge to develop sustainable solutions.
• Clearly defining data science roles along with career progression helps to attract the best talent in the industry. Organizations should make efforts to pair data scientists with business units and actuarial support teams.
• Data scientists are more equipped with the latest technological know-how across industries. They can use unstructured data to derive meaningful insights and solutions. Their ability to handle a large volume of different types of unconventional and unstructured data like image, voice, text, etc., can be used to develop sustainable solutions.

The areas where the early confluence can be witnessed are distributions, pricing of insurance products, risk management techniques and mitigation, claims, in force creation, and liability valuations.

Benefits to realize with this confluence

• Marketing and distributions: Many data science skills are helping insurance companies by bringing innovation to their conventional product portfolio. The products are now more aligned to customer needs, with an increased ability to revisit coverage-based circumstances. Expanding reach, designing personalized products, and ensuring value for money with absolute transparency are some of the highlights of the confluence.
• Pricing and underwriting: Actuaries and data scientists together are building pricing models with more emphasis on unstructured data. The focus is on using predictive modeling tools for customized pricing, where insurance companies intend to profit. Mortality investigations and the premium setting process will become more granular for individual pricing.
• In force analytics: The use of non-traditional data sources, seamless integration of peripheral systems, frequent movement reconciliation, and detailed assumption settings will provide meaningful insights from in force creation process.

Reserving process will become more streamlined, and the frequency of reports will increase. It can quickly provide a quicker analysis of changes and their impact on capital management and profitability matrices.

• Risk management process: The confluence will lead to a better understanding of risk factors, assumptions setting, granular changes, and real-time data availability for efficient capital management and risk mitigation measures.

Enhancing value for insurers

Insurers are increasingly looking to use data science techniques in the actuarial domain. Today, actuaries can access more data to work with and more information about people and society than ever before. The use of non-traditional data sources like social media, wearable devices, POS scanners, drones, etc., will play a dominant role in setting real-time assumptions, thereby increasing the granularity and frequency of reporting. The robust techniques will provide a suitable data architecture for seamless and on-the-call reporting.

Wipro is committed to providing high-quality business solutions and consultancy services, coupled with proven cutting-edge, data science expertise to bring transformational change that can solve the challenges that this confluence poses. Our narrative focuses on strengthening your delivery excellence, designing value creation models, and lowering your Capex significantly.

For details, connect with our experts here.

Reference:

IFOA Data Science Committee, John Ng

Modular Framework of Machine Learning (2020)