Artificial intelligence technologies are everywhere. The great leap forward in AI over the past decade has come along with an explosion of new tech companies, AI deployment across almost every industry sector, and AI capabilities behind the scenes in billions of intelligent devices around the world. What does all of this mean for the personal lines insurance sector? SMA answers this question in a new research report,“AI in P&C Personal Lines: Insurer Progress, Plans, and Predictions.”
The first step toward answering this question is to understand that AI is a family of interrelated technologies, each with its own potential use cases and insurance implications. The key technologies relevant for P&C insurance are machine learning, computer vision, robotic process automation, user interaction technologies, natural language processing, and voice technologies. It’s a challenge to sort through all these technologies, the insurtech and incumbent providers that offer AI-based solutions, and where each insurer will benefit most from applying AI.
The overall value rankings indicate that user interaction technologies fueled by AI are at the top of the list for personal lines insurers. Every insurer has activity underway, mostly by leveraging chatbots for interactions with policyholders and agents or using machine learning for guided data collection during the application process. Insurers see high potential for transformation in policy servicing, billing, and claims – areas where routine interactions can be automated.
Robotic process automation is in broad use across personal lines, although the RPA technology is viewed by many as more tactical. There is high value related to streamlining operations and reducing costs, but most wouldn’t put it in the innovative category. Machine learning and computer vision have great potential for personal lines in both underwriting and claims. The combination of computer vision and ML technologies applied to aerial imagery is already becoming a common way to provide property characteristics and risk scores for underwriting. Likewise, images from satellites, fixed-wing aircraft, and drones are frequently used for NATCAT situations. And AI technologies will be increasingly applied to these images for response planning.
There are many other examples. But for the purposes of this blog, the main question – which technologies are most valuable – has been answered. AI-based UI technologies, ML, and computer vision demonstrate the best combination of high value today and transformation potential for the long-term.
But perhaps the more important question is not which technologies are valuable, but rather where AI technologies are most valuable in the enterprise. The short answer is that there are so many potential value levers and so many unique aspects to different business areas and lines of business that it is difficult to select just a couple of high-value areas. That said, it is relatively apparent that underwriting and claims both present major opportunities, and activities are already underway there. There are great possibilities for AI in inspections, property underwriting, triage, fraud, CAT management, automated damage assessment, predictive reserving, and other specific areas.
There is no shortage of opportunities for AI in personal lines. Fortunately, there are increasing numbers of tech solutions in the market and growing expertise in the industry involving AI technologies and how to apply them. Ultimately, we expect to see a pervasive use of AI technologies throughout the insurance industry. Some will become table stakes. Others will define the winners in the new era of insurance.
This blog entry has been reprinted with permission
