Artificial Intelligence (AI) has been in almost every technology-based headline over the past 24 months. If an incumbent technology provider or a newly emerging InsurTech organization wants to grab attention – well, just insert AI in the first few lines of the description. Or, better yet, insert AI in the product or organization name. In fact, AI does hold exceptional business promise, and there are numerous proven use cases. But AI is a complicated topic.
There are many sub-categories of AI, and one of the first steps in choosing the appropriate technology is to break down AI into consumable bites. SMA finds that there are six primary AI technologies in play within commercial lines organizations: machine learning (ML), computer vision, natural language processing (NLP), user interaction technology, voice technology, and robotic process automation (RPA).The big question is – which AI technologies drive the most value for commercial lines?
Not surprisingly, there is a tug-of-war between AI for transformational purposes and AI for tactical purposes. According to commercial lines executives and managers, ML, RPA, computer vision, and NLP (in that order) will transform commercial lines the most. Given the general need for transformation across the insurance industry, one could conclude that the previously stated order of technologies would be where the industry is heading in terms of investment. But that is not the case.
The actual investment order is new user interaction tech, machine learning, RPA, and NLP, with the remaining technologies following. Does this mean commercial lines insurers have gotten it wrong? The answer to that question is “no” with possible shadings of “could be.” Much of the framing for this answer lies in the product mix.
For the small business and workers’ comp segments, new user interaction technologies such as chatbots and text messaging have been invaluable in contact centers. This impacts underwriting and claims by clearing tasks from work queues, thus freeing up technical expertise for more complex interactions. Billing benefits as well. Collaboration platforms and real-time videos proved highly valuable during the pandemic’s height and continue to be highly worthwhile.
Machine learning has universal value across product lines. Whether it be ML to improve straight-through processing for less complex lines, such as small business and workers’ comp, or to provide decision support for complicated product lines, ML can contribute in all areas. The great thing about this is that investment in effectively adopting ML skills pays off across the enterprise.
RPA is a technology that not only improves operational efficiency and expense management – important internal goals – but it also enhances customer and distributor satisfaction through rapid request fulfillment. Policy service, underwriting, and claims all gain value through RPA adoption. Because almost all commercial lines segments have repetitive processes, RPA skills are utilized universally.
The “could be” warning stated earlier comes in terms of computer vision and NLP. Both technologies have significant transformational value in commercial lines, ranging from turning aerial images into information to digitizing paper-based information sources. Prioritizing these technologies sooner rather than later is critical across all product segments.
More than almost any other technology, AI technologies work best in combination. For example, NLP with RPA to increase process penetration. The industry is in early days when it comes to AI usage, and skill sets are still advancing. The “getting it right” discussion is frequently dependent on product segments. But over time, value will be universal regardless of product complexity, albeit for different reasons.
This blog entry was reprinted with permission
