Overcoming a challenge: when it comes to data, the business strategy perspective is often missing

From a historical perspective, the insurance industry has always operated a data-driven business model. What is often missing is a holistic view of using data as an asset and actively using it to achieve strategic business goals. Individual activities in the fields of analytics, data science and artificial intelligence (AI) have already shown promising success. Examples include:

  • The use of AI for process optimization in input management
  • Increasing the dark processing rate in the claims sector through machine learning
  • Increasing the retention of existing customers.

Unfortunately, however, the efforts often remain selective and local, and a holistic view of the value added by data from the perspective of business strategy is missing.

Business in the spotlight: analytics & AI as a program?

This industry has a wide array of strategic goals. These range from interwoven issues such as a focus on higher customer satisfaction and greater premium stability through savings targets in operations to line-specific goals such as increasing the reinvestment rate in life insurance. In order to invest here while creating as much as value as possible, it is important to identify use cases for Analytics & AI from the perspective of these strategic goals and to consolidate them in a program that is aligned with the business strategy. Individual activities do not contribute to synergies but do increase the amount of effort required at a later stage to implement the solution.

The solution: developing strategic analytics & AI roadmaps

An appropriate program, such as a strategic analytics and AI roadmap, directly supports the business objectives. The starting point for this is an ideation process for finding ideas for possible use cases, including an initial evaluation together with specialist departments. This is followed by detailed work, in which often dozens to hundreds of ideas for use cases are grouped according to their value contribution to specific strategic goals and consolidated into a strategic roadmap.

The target picture: operationalization plan and implementation requirements

An operationalization plan defines which use cases are implemented at what time. Important prerequisites for a successful implementation must also be clarified and, if necessary, created, which may include establishing what IT infrastructure is necessary. How developed is the company’s competence to lead such complex innovation projects successfully from the idea to operationalization? Are the necessary skills, especially in analytics & AI, available or do they need to be developed? Start your strategic AI journey with us now – contact us!

Contact

Dr. Markus Knappitsch
Head of Data Business Consulting Insurance & Banking
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Dr. Knappitsch is responsible for the Insurance and Banking divisions at Comma Soft AG. He also acts as a lead consultant in a controlling capacity. The core of his work focusses on optimizing and digitalizing processes in the insurance environment using statistical methods and machine learning. The emphasis is always on combining relevant domain knowledge with modern data analysis methods.
Contact us!

Dr. Knappitsch is responsible for the Insurance and Banking divisions at Comma Soft AG. He also acts as a lead consultant in a controlling capacity. The core of his work focusses on optimizing and digitalizing processes in the insurance environment using statistical methods and machine learning. The emphasis is always on combining relevant domain knowledge with modern data analysis methods.