Reuters Events, in preparation for its Insurance AI and Innovative Tech USA 2023 conference, published a new whitepaper based on the experiences of three U.S. based insurers, entitled Data-Driven Insurance: A Model for Better Decision-Making.
In the report, they point out that artificial intelligence (AI), data and analytics experts have been called to the front lines and given a complex mandate to advance the industry’s modernization drive. Investments in talent and technology must continue, they add.
All told, they say the global insurance analytics market is worth US$12-billion in 2021. It is forecast to reach US$39-billion by 2030.
“There is no shortage of use cases for data and analytics across every line of the insurance business,” they write, adding that data accessibility needs to be at the top of companies’ lists of priorities.
At American Modern Insurance Group, the company is currently working on the development of coursework for employees. “What can really help is when everyone in the organization has the same basic understanding of data and how to use it,” says Elizabeth Barth-Thacker, vice president enterprise data and analytics with American Modern.
The paper goes on to say that boosting organization-wide data literacy is an important strategic objective. It also provides tips for change management and improving data literacy.
For companies, the majority of which say they plan to increase staff in the next 12 months, technology, underwriting and claims roles are proving particularly hard to fill. “If hiring advanced data scientists wasn’t tough enough, Barth-Thacker says there is even more pressure to find the unicorns, those rare individuals who can bridge the gap between business and technology to show what is possible from analytics.”
Going forward, they conclude that fast and effective, data-driven decisions will be a prerequisite for companies’ success. “While some are rising to the challenge, others are struggling to keep up. At a time of global paradigm shifts, the industry must become better at experimenting by building, testing and learning in a low-risk environment. This will only be possible with reliable, transparent, and accessible data. By automating mundane processes, critical thinkers can prepare for the next level of decision making.”
