The insurance industry – one of the least digitalized – is not surprisingly one of the most ineffective segments of the financial services industry. Internal business processes are often duplicated, bureaucratized, and time-consuming. As the ubiquity of machine learning and artificial intelligence systems increases, they have the potential to automate operations in insurance companies thereby cutting costs and increasing productivity. However, organizations have plenty of reasons to resist the AI expansion; the fear of unemployment and the lack of trust in cognitive systems are among them.
But these are hardly justified concerns. According to Accenture, two out of three CEOs of insurance companies expect job net gain, even though AI insurance advocates claim that the time of insurance agents made of flesh and bone has gone. The truth is somewhere in the middle: Insurance companies can achieve synergy combining human and AI efforts. Interestingly, employees are optimistic about AI implementation. The Accenture report mentioned suggests that over 60 percent of insurance industry leaders surveyed by the consulting firm believe that AI adoption will boost their carriers. So, let’s talk about the main opportunities of AI adoption in the insurance industry.
Imagine that you plan to personalize health insurance quotes for people having various heart conditions. This requires precise, real-time, individual heart-tracking. To access this data, insurers can fully or partly cover the price of a wearable device (e.g. Apple Watch or other) that would track heart rhythm and stream collected data to a server. Then this data must be analyzed against the insurant’s electronic health record (EHR) dataset to infer predictions on whether a given person may shortly need medical attention. The higher the risk, the higher the monthly or annual quote is.
And this is a rather simplified and “one-of-many” model. Currently, AI algorithms can classify clients – by monitoring their health records – into hundreds of groups depending on various risks, which is a win-win relationship. Most customers enjoy a personalized approach as they seek fair quotes. On the other hand, insurance carriers can better manage risks and margins.
So, how does this work?
In general terms, artificial intelligence (AI) is a computer system capable of analyzing data in a nonlinear way, making predictions about it, and arriving at decisions. Advanced systems are able to continuously learn and enhance themselves.
Usually, machine learning (ML) builds AI systems using methods that employ statistical analysis of existing records to make predictions on new data. For example, if we have extensive health data on previous clients, we can predict the likelihood of this or that client looking for medical care and how soon this may happen. Unlike traditional, rule-based algorithms, machine learning doesn’t require engineers to explicitly map various input-output scenarios. This allows for forecasting and making decisions based on numerous, intricately connected factors, the thing that traditional programming can’t achieve.
NB: Some experts prefer to narrow down the AI term to describing a distinct and independent agent that handles inputs, analyzes them, and makes decisions. In our article, we use AI to refer to any smart system that leverages data science techniques to either make decisions or just augment human workflow.
While AI systems aren’t smart enough to fully replace humans, they already suggest several tangible improvements to a carrier’s operations. Let’s a have a look at these opportunities.
So far, we see seven main areas in the insurance industry where AI can be helpful.
Every day an average insurance agent spends up to 50 percent of their time manually filling in various forms to handle claims. Natural language processing (NLP) and speech recognition algorithms can transcribe and even interpret human speech to streamline this cumbersome routine.
