The area of artificial intelligence is especially exciting, as it might be applied to the FinTech, robo advisor and ensure tech areas. What is an artificial intelligence again? Well, ultimately, it's a description of software which can perform all kinds of tasks that are historically normally associated with reasoning by humans. That could be learning, it could be simulating self-awareness, including potentially the perception of emotions, and insurers have focus on harnessing artificial intelligence. First, with respect to customer interactions, chatbots, for example, or programs that communicate with customers verbally, through text, ultimately guiding decision-making. Allstate, for example, the well-known insurance company, created a chatbot called ABIE, the Allstate Business Insurance Expert. ABIE, specifically residing on Allstate's website, provides answers 24/7, 365 days a year, to answer policy owners' queries. Other insurance companies have followed, specifically focusing on answering customer questions, help them complete applications, or even filing claim reports. Another type of Insurtech is machine learning, which is a subset of AI. Allowing computers and their software algorithms to simulate learning or data augmentation and knowledge augmentation over time. Using rules or models to simulate the way the brain works. This kind of learning, machine learning, allows computers to extract patterns from data and that could be structured data, in the form of numerical data, or unstructured data, in the form of text, or other elements including pictures, rather than following highly specific instructions. Of course, instructions are important in the definition of the algorithms that machine learning represent. But in as far as machine learning can self replicate or operate under a broader set of instructions. Machine learning gives the appearance of being closer to the activities of the human brain. Not many insurance are currently using what we call deep learning, machine learning. Certain insurance companies are applying it to huge amounts of data that they collect. NAIT, so the National Association of Insurance Commissioners, indicates that only about 10 to 15 percent of the data collected by insurance companies is currently actively used. Machine learning could allow those insurers to look at large amounts of data, sometimes known as Big Data, to extract patterns that are useful for their businesses. Some examples could include risk modeling, allowing insurers to analyze their claims data in order to predict risk better. Of course, the historical data in the form of the history of losses may not be the only data that are important in understanding how to price risk and how to predict the risk. Insurers could create models to predict demand for their own products and also develop new products, and therefore understand how to price them, or determine their premium. Fraud, as you might expect, is a potential problem in the insurance industry. Machine learning, including analyzing pictures and looking for certain markers of fraud, could both allow insurers to propagate their business models better, but also potentially decrease costs due to the dead weight loss of fraud. Those may be obvious to human adjusters, but also, of course, it may not be. The automation of claims, which of course could lead to happier policy holders, is another area where machine learning and it's an efficiency and speed may apply, automating, reporting, processing, and speeding along the customer experience. And finally, one of the most important areas of insurance, underwriting. Underwriters are those human decision makers who analyze data and, ultimately, make the decision of how to take on a given risk, and how to price it. Computers can aid that decision making process, they can flag risks in the process, and they can also point out inconsistencies in data that human underwriters may not be able to see. They can also check external sources, like social media, to verify accuracy of input data.
