Three insurance challenges being addressed by AI
Artificial intelligence or AI stands out as one of the hottest technologies in the insurance industry in 2018. We are seeing more insurers identifying use cases, partnering, and investing in AI. 85% of insurers are investing time, money, and effort into exploring the AI family of technologies. The focus is not so much on the technology itself as on the business challenges AI is addressing.
- For companies looking to improve internal efficiency, AI can assist through machine learning.
- For those working to create a dynamic and collaborative customer experience, AI can assist with natural language processing and chatbots.
- For those seeking an edge in data and analytics, AI can help to gain new insights from images with the help of machine learning.
Through our annual SMA Innovation in Action Awards program, we hear many success stories from insurers throughout the industry that are innovating for advantage. AI was a key technology among this year’s submissions. The near-ubiquity of AI was even more obvious among this year’s insurer and solution provider winners, many of whom are leveraging some type of AI to solve widely variant business problems. They have provided some excellent use cases of how insurers are applying AI and how it is helping them to succeed.
Two AI technologies, machine learning and natural language processing, fuel Hi Marley’s intelligent conversational platform, which West Bend Mutual Insurance Company piloted in claims with outstanding results. The Marley chatbot lets West Bend’s customers text back and forth to receive proactive updates, ask and answer questions, and submit photos. Its use of SMS messaging means that communication can be asynchronous and done on a customer’s own schedule, eliminating endless rounds of phone tag.
- Natural language processing allows Marley to communicate with customers in plain English – both to understand their needs and to respond in a way that they will understand.
- Machine learning enables Marley to continue to improve. The platform analyzes every conversation and uses it to shape how Marley responds to specific requests, refining its insurance-specific expertise for future interactions.
Natural language processing is also a critical tool for Cake Insure, a digital workers’ comp MGA with a focus on making the quoting experience easier for direct customers. One of the hurdles that would-be customers had to overcome in obtaining workers’ comp coverage was answering a multitude of questions regarding very specific information that a layperson is unlikely to know about or understand.
- NAIC codes, for example, are required for every workers’ comp policy, but the average small business owner would be baffled if asked about them. Cake circumvents this by asking the user to type in a description of their company in their own words. Natural language processing parses this plain-language description and searches for its approximate match in the NAIC data sets. This back-end process occurs without the user’s awareness and without exposing potentially confusing content.
- As with Hi Marley’s chatbot functionality, natural language processing is paired with machine learning to improve its ability to respond to specific phrases and content.
Machine learning can also be deployed in conjunction with other AI technologies. Image analysis and computer vision are combined with machine learning in Cape Analytics’ solution, which can automatically identify properties seen in geospatial imagery and extract property attributes relevant to insurers. The result is a continually updated database of property attributes like roof condition and geometry, building footprint, and nearby hazards.
- Computer vision helps turn the unstructured data in photos and videos from drones, satellite, and aerial imagery into structured data.
- Machine learning allows the solution to train itself on how to do that more effectively, as well as higher-level analysis like developing a risk condition score for roofs.
We are only scratching the surface of how AI can be applied across the value chain. The incredible variety of AI’s potential applications in insurance is difficult to overstate. QBE knows that well: they won a company-wide SMA Innovation in Action Award for their wide-ranging activities in emerging technologies and partnerships with InsurTech startups, but AI in general, and machine learning specifically, are their top priorities. In addition to partnering with dozens of InsurTechs, QBE has also pushed themselves to deploy each InsurTech’s technology somewhere within their business – meaning they have dozens of different creative AI applications in play at once. For example, in partnership with HyperScience, QBE is improving data capture from paper documents through machine learning and computer vision.
These winners’ stories demonstrate the myriad ways that insurers are applying AI to improve business operations. Notably, its deployment helps them to significantly improve the customer experience – or in the case of data capture – the internal employee experience. The need for this kind of seamless customer experience in the digital world cannot be overemphasized. AI, which struck many as a science-fictional concept, has proven its real-world worth by enabling insurers to transform their customer journeys and experience.
With full scale implementations popping up across the insurance industry, as well as the pilots and limited rollouts that we have seen in previous years, it is easy to lose sight of the fact that we are seeing only the very tip of the iceberg in terms of how AI can transform the business of insurance. Applications of more advanced and advancing AI technologies, as well as the combination of AI with other emerging technologies such as drones, new user interaction technologies, autonomous vehicles, and IoT are unexplored territory that is bright with promise.
This much is clear: AI will change the face of the insurance industry. In fact, it’s already happening.