So called “weak” AI—such as today’s chatbots—in coming decades will give way to more human-like “strong” AI, which will probably disrupt current business models. How can insurers prepare?
In this era of the primacy of data and information to any insurer’s strategic and operational efforts, it seems just to make sense to treat the management of such a valuable asset in at least the same way as any other strategic asset.
Insurers need to shift from being data “smart” to being able to run intelligent operations that evolve over time and support data-driven decisioning throughout the entire value chain.
We may see an even larger wave of InsurTech capital in the coming years, continuing the upward trend in overall market funding, which has increased for six straight years.
Don’t build your insurance program based on just one technology; instead, embrace openness and leverage open data exchanges to overcome data matching and management issues.
Insurers must start building an interoperable innovation framework that begins to meet the array of emerging risks they face from emerging technology, trends and regulation.
When discussing artificial intelligence CIOs often describe the opportunity to automate manual tasks, to replicate tasks with machine learning, and to produce recommendations based on learned behavior, but there is much more insurers can do with AI, even in the near term.
No matter the approach, a data quality management strategy that includes EDW exception, status and event notification is an important milestone on the way to overall data quality health.
We’re now starting to see the first signs of a new breed of insurance innovation labs, built by those slightly later to the party.
As in the case of other industries, the majority of large insurance incumbents are banding together into consortia to experiment with blockchain.