Opportunities and Challenges
With new capabilities, of course, come new challenges. For example, AI programs must be trained to ensure the right information is gleaned from sources used (e.g., medical, prior claims, credit) so that the program can address pertinent underwriting needs. As this training can only be provided by humans, full program optimization will take time.
A second challenge is that new data categories can increase the risk of underwriting bias and unfair discrimination against certain groups (e.g., marrieds vs. singles). Regardless of the amount of new data, its value still depends on how well it reflects reality, and data processed by AI tools is susceptible to inaccuracies. Insurers need to be vigilant in identifying such errors to ensure unfair discrimination does not occur.
The governments of many Asian countries have been developing insurance industry databases that insurers can use to both to check a person’s health history and analyze market data. New automated underwriting products that leverage this new information are currently in development in several countries.
Human Touch Still Needed
The ability to access and use larger and wider datasets that include several new types of data, enabled by significant advances in AI and ML technologies, is opening new opportunities for insurers. At this juncture, insurers must be sure they have in place a strong oversight framework. Rules need to govern what is and is not permissible, and must include factors such as customer consent, user access limitations, storage security measures, and secure data transfer capabilities.
Advanced digital tools and techniques can enable analyses, predictions, and recommendations, or even guide decisions, but as capabilities advance, so must the understanding that being able to do something does not mean it should be done. Technology can create and amplify asymmetry between available data and the ability to manage it, resulting in unfair bias. The industry must therefore pursue innovation in an ethical and transparent manner, accounting for differences in markets, regions, companies, and other relevant factors.
Also, as with any technology, AI-enabled tools bring a risk of overreliance. Insurers must implement processes to guard against oversimplification and balance AI’s imposed simplicity with data accuracy. Are the tools contributing to customers being treated fairly and equitably? Are the interpretations provided by digital platforms being properly analyzed and reviewed by on-staff experts?
Bottom line: Digitization can provide a range of benefits to insurers – automating repetitive tasks, speeding data analytics and discovery, simplifying information mining to providing a broader range of products and services, and more. However, insurers must also keep in mind that digitization is fundamentally a tool, and one that should augment, but not replace, human expertise, experience, and judgment.