Data competences in the insurance industry
Creators: name: Becker, Johannes
name: Efstathiades, Alecos
name: Portmann, Jürg
name: Zeier Röschmann, Angela
Publisher: name: ZHAW Zürcher Hochschule für Angewandte Wissenschaften
Year: 2024
Subjects: Insurance
Artificial intelligence
AI
Digitization
Competence
Skill
Training
005: Computerprogrammierung, Programme und Daten
332.38: Versicherungen

description: The availability and utilization of data for risk quantification and pricing is the cornerstone of insurance. While the effective aggregation and analysis of data has always been a key competence of the industry, the significance of data as a value-generating asset has increased significantly with technological advancements. Furthermore, we have noticed a shift in the focus of data analytics from evaluation to prediction, as well as a shift from an analytic approach to a more action- and decision-oriented one (prescription). Additionally, the significance of unstructured data has increased, particularly with the emergence of generative artificial intelligence and large language models. Overall, insurance companies are facing the potential for radical changes in customer interaction, processes, and working methods.
Our findings are based on 25 interviews with experts from the insurance industry and consulting area, a survey of insurance employees, and an analysis of job postings from the largest primary insurers in Switzerland. We examined how advancements in data analytics and artificial intelligence and increasing methodological and technological complexity are affecting insurance companies, specifically in terms of data competencies, roles, and work methods.
The study results indicate that the industry is making significant investments in methodology, tools, and systems. However, there seems to be a hesitancy to innovate or make drastic changes. Nevertheless, the Covid-19 pandemic and advancements in generative artificial intelligence, such as ChatGPT, are viewed as catalysts for a greater emphasis on data in all business functions. These catalysts are effective because they both work at a strategic level (top-down) and have a direct influence on the way employees work (bottom-up).
Our analysis reveals that insurers share a fairly similar vision. The target state is not a fully automated insurer, but rather a business with a comprehensive, real-time understanding of risks, an end-to-end customer view, as well as cost-effective, data-based decisions and actions. Achieving this vision requires a combination of insurance business expertise with data management, data analytics, and artificial intelligence. A key enabler is seen to be the “democratization of data management and use”. This means that business functions have ownership of their data and can create analyses tailored to their specific business requirements.
Now, more than ever, it is crucial that applications and instruments are easy to use and can be flexibly customized. Future infrastructure must support real-time data exchange across different interfaces. Data management and governance are key to ensuring both sufficient levels of data quality, granularity, and volume as well as to fulfilling legal, regulatory and ethical requirements. In consequence, there is a growing need to expand the number of staff authorized and enabled to access and use data.
Insurers face a range of challenges, including modernizing legacy systems, improving data quality and data governance, providing tailored, needs-based training for the various functions, changing working methods and organizational structures. Significant change efforts are required by those functions that have not yet been data-driven and hence lack an adequate data basis and respective working methods. At the same time, artificial intelligence offers great potential for areas such as sales, customer service and operations. Standard solutions offer the potential for cost-effective and rapid implementation.