We live in an increasingly interconnected world.
Everything from shoes to toothbrushes and more is connected to the newest bendy screen smartphone and to the cloud. Combine this data with information about spending habits gleaned from banking, credit card, and mobile wallet transactions, and it’s easy to see how pictures of an individual’s wealth and health can be generated.
As a result, every company worldwide is highly interested in understanding how measurable behavior can impact core business operations and future strategic directions. Most of these efforts are focused on loyalty, customer engagement and streamlining the customer purchase journey.
Life and health (re)insurance companies are no different. Companies have spent the past several years organizing and digitizing existing data assets, making it easier for advanced analytics teams to create management information dashboards and statistical models.
In Asia, life and health insurance company data modeling efforts have mainly been focused on two goals: streamlining underwriting processes to speed policy issuance as well as simplify consumer needs analysis and cross-sell, and improve non-disclosure/fraud detection to maintain premium rates and protect the bottom line. Given the massive underinsured populations in many Asian countries, as well as the fraudulent activity that occurs in several markets across the region, these efforts are strategically wise.
Efforts to date to achieve these goals have been admirable. At this point, however, our industry is merely scratching the surface of what potentially can be achieved by using existing data sources, gaining access to new permissioned data, and leveraging advanced statistical techniques such as machine learning and deep learning.
Let’s delve a bit further into the data sources that life and health (re)insurers are interested in as well as the ways that this data can be utilized in the operations of life and health risk carriers.