For several years, experts have touted artificial intelligence and the\u00a0closely related field of machine learning\u00a0as the latest solutions for the complex needs of the insurance industry. While AI promises a wide range of applications, it also raises a host of concerns. Some of these fears\u2014for example, the displacement of human workers\u2014are common to many innovations. Others, such as the perpetuation of discrimination via biased training data sets, are unique to this technology.<\/p>\r\n\r\n\r\n\r\n
The debate among the general public, insurance professionals, and\u00a0now regulators<\/a>\u00a0continues as AI moves into ever more areas. So how can we as an industry thoughtfully address these concerns and develop best practices to maximize benefits and minimize harm?<\/p>\r\n\r\n\r\n\r\n AI-powered applications for our industry generally take one of two forms. First, they offer the ability to shift a multitude of mundane interactions in the insurance ecosystem from people to automation. The appeal here is obvious. An increasingly competitive\u2014and therefore expensive\u2014labor market no longer constrains the ability to provide 24\/7 access to expert help. Customer service chatbots are the most widely known example of this use. These applications vary in sophistication. Some are little more than automated FAQs and price comparison tools. Other, more complex algorithms vet sales prospects and even handle routine claims.<\/p>\r\n\r\n\r\n\r\n The second use group relies on AI to manage and analyze the flood of data now available to inform decision-making. Notably, many of these solutions promise to avoid human cognitive biases by replacing intuition with data-driven insights. Examples range from resume scanners and video interview analysis tools to telematics and algorithms that offer highly granular, real-time risk assessment to guide underwriting, pricing, and loss control decisions.<\/p>\r\n\r\n\r\n Looking for more information on the current uses of artificial intelligence applications in the insurance industry?<\/em><\/p>\r\n\r\n\r\n\r\n Mike Thomas shares\u00a0<\/em>25 AI Insurance Examples to Know<\/em><\/strong><\/a>.<\/em><\/p>\r\n\r\n\r\n As mentioned previously, concerns about the displacement of human workers by technology are nothing new. In the past, however, the displaced could re-train to work\u00a0with<\/em>\u00a0the new technologies and move into more lucrative careers. AI directly targets these knowledge workers, however. While AI will likely create new jobs, the tasks being automated mean that the educational investment needed to secure these jobs is much higher. This, in turn, amplifies the disparity of opportunity between traditionally advantaged and disadvantaged populations. Additionally, as\u00a0Harry J. Holzer recently pointed out<\/a>, \u201cIn general, automation also shifts compensation from workers to business owners, who enjoy higher profits with less need for labor.\u201d<\/p>\r\n\r\n\r\n\r\n A second concern is the loss of the \u201chuman touch\u201d as automation becomes more deeply enmeshed in insurance processes. While insureds surely value the accessibility that automation provides in response to \u201croutine\u201d claims; when faced with major claims such as those arising from traumatic situations such as CAT events, the human ability to understand the emotions at work and respond appropriately vastly improves the customer experience. While\u00a0artificial empathy<\/a>\u00a0does exist, its effectiveness is still being evaluated. Additionally, AI systems may be more constrained in their choice of responses than their human counterparts who can understand when it is appropriate to seek novel solutions to complex problems.<\/p>\r\n\r\n\r\n\r\n Perhaps the greatest concern is a perceived (or actual) lack of transparency for AI systems. Beyond overt biases built into AI\u2019s decision-making processes, those affected by their choices often have a limited understanding of the criteria used to make them. As machine learning advances, even application developers may not fully understand how \u201cevolved\u201d algorithms operate. Add to this the fact that humans often make mistakes about which factors lead to the best decisions, and the gap between organic and synthetic thought patterns could expand into a chasm. Without transparency, individuals negatively impacted by AI choices may feel less able to challenge them\u2014or even be unaware of the need to do so.<\/p>\r\n\r\n\r\n\r\nAI in the Insurance Industry<\/h2>\r\n\r\n\r\n\r\n
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\r\n\r\n\r\nThe Concerns About AI<\/h2>\r\n\r\n\r\n\r\n
De-Personalization<\/h3>\r\n\r\n\r\n\r\n
Transparency<\/h3>\r\n\r\n\r\n\r\n
Developing Best Practices<\/h2>\r\n\r\n\r\n\r\n