(AI) is a disruptive technology dedicated to solving problems. One of the most significant issues that the insurance industry has to face is how mired in the past it has become. Entrepreneur
notes that the insurance industry has been traditionally against
the adoption of new technology. This line of thinking has severely hampered their ability to provide proper service into the twenty-first century. AI is ideally placed to improve the business practices that exist in the insurance industry.
As a mathematically-based, iterative approach, AI can learn from its mistakes and grow with each new set of policies it underwrites. Scholar Works
notes that insurance as a profession was historically a mathematical method
of balancing risk to reward using probability. It’s about time it entered the twenty-first century and adopted modern society’s penchant for simplifying math by applying AI to develop its policies.
The ways AI can be leveraged
AI is an umbrella term that refers to several different fields, each dealing with its own facet of computerized thinking and the simulation of the human thought process. Under this broad classification, there are four major fields of AI that can be used to make the lives of insurance underwriters a lot easier. These are:
defines deep learning
as a method of AI that imitates the working of the human mind. At its core, insurance is a field that requires more than just mathematics. There is a need for incorporating rational thinking into policy coverage because of the topics it involves.
By utilizing deep learning, an insurance company can potentially develop AI that mimics the actions and decisions of an expert underwriter with the added benefit of the AI not needing to be paid, and never retiring from service. Risk assessments can be done using external sensory devices in the form of drones and IoT devices to give the AI even more information to work with than a human underwriter can fathom, much less incorporate into his or her policy decisions.
states that machine learning
is a method of AI that builds on the premise that data can inform systems and by utilizing this data, systems can evolve to offer deeper analytical insights. In the case of an insurance underwriter, there are tens, maybe hundreds of thousands of insurance policies that a single agency has built during its lifetime.
With this massive pool of data and a handful of rules implemented to help the AI understand what it’s doing, the potential for processing and creating a new, more efficient method of underwriting exists. Less complicated cases can be efficiently dealt with and can provide a solid foundation for more complicated cases to be worked on. At current, an underwriter can take as much as twenty days to develop a policy. By using machine learning, this period could be cut down to a much smaller window.
Behavioral data modeling and analytics
says that behavioral data modeling
allows an AI to understand the human patterns of actions in their everyday lives. The major downfall of insurance is that it’s a reactive industry. It can only take measures after something has happened as opposed to before the fact. With behavioral data modeling, there exists the potential for insurers to consider the current behavior of their customers into account and develop incentives that promote safer practices and malign dangerous actions. This, in turn, will lead to cheaper insurance for your local emergency dentist
. The implementation of a system like this becomes more likely as IoT devices spread around the globe and acceptance of data monitoring increases across the populace.
Natural language processing (NLP)
Towards Data Science
mentions that NLP is a methodology of using AI to structure ideas
through words that obey human grammar and vocabulary rules. NLP already exists in many different forms online, ranging from chatbots that are designed to help customers with queries to data mining uses where it can be put to the task of extracting information from printed or written sheets. Speech and text processing algorithms have existed since the earliest days of computing, but the technology has advanced by leaps and bounds. The result is a system that is well equipped to deal with an insurer’s office by both offering a “human” voice to speak to over the phone as well as a technological assistant that digitizes documents for use in a machine learning environment.
How to bring AI into the insurance industry
The term ‘disruptive technology’ isn’t a misnomer in the case of AI. Introducing artificial intelligence into the insurance industry will lead to a lot of changes regarding how companies process policies. However, because of how technology-averse the sector is, introducing the system in a series of smaller use-cases may be the best way to integrate it into the operating procedures of a department. AI is massively scalable, and once a handful of test cases have proven their worth, the solution can easily be scaled up to be used across the entirety of the organization.