It is an established fact that there are things artificial intelligence-based applications can do better than humans. Gathering and doing statistical analysis of structured data, and even browsing through the unstructured data, is one area where cognitive systems are hard to compete with. Interpreting analytics is another emerging field, where machine analysis advantages include greater validity and objectivity of results, as well as sheer speed and unrivalled ability to process massive amounts of information.
Artificial intelligence and software scientists are presently focused on producing software emulating human intelligence even so far as creative abilities are concerned. As David Gelernter, professor of computer science at Yale University, puts it: “suppose you built thought-sequence-constructing software with a “focus” knob? As you twiddled the knob from high focus to low, your software would show a decreasing tendency to be logical and increasingly tend to free-associate thematically, contralogically. At some point between halfway down and the bottom, it would pass through the “creative zone” in which new analogies are discovered.”
This should not be misunderstood in an anti-utopian conception of robots making millions of people jobless. Some professional re-orientation of e.g. call center workers will most likely be inevitable, as a result of knowledge-work automation. It is far more likely, however, that the currently unfolding revolution will lead to new opportunities and more creative jobs, which will be explained in a little more detail at the end of this article.
Assessing risks, as well as placing a unique set of life circumstances of an insurance customer within a risk assessment matrix is another area where there is much untapped potential for AI. According to Deloitte, “psychological biases inhibit sales of life and annuities products. More effective messaging tools and products to overcome behavioral barriers should be developed.” This and other similar tasks are addressed through effective communication with customers and these business processes can be radically strengthened by the use of big data, sentiment analysis, personalization of services achievable through effective speech analytics and other methods.
In fact, InsurTechs or incumbent insurers make their bets on leveraging the existing data to generate risk insights, develop new capabilities in customer service that make it easy and more fun to deal with them, and finally – offer trusted dialogue-based customer relationships with effectively personalized products and hassle-free delivery.
Early adopters among insurance companies have already started to use speech recognition and voice biometrics optimizing their contact centers and are gaining benefits from it. While there is a general understanding that advanced analytics can empower more precise and personalized risk selection, segmentation, and claims management (from simple form-filling automation to emotion detection and compliance monitoring), few reference cases speak directly to the executives’ desire to see business value and cost-saving results. Awareness of greater competition from InsurTech companies is there, but no reliable blueprint for steering the digital transformation down the same path is available. This situation is bound to change, however.
Based on our experience, there are a few specific business cases for the insurance industry, where there is a happy match between a clear need as well as realistic impact definition, and on the other hand – requisite technologies being reliable enough and fully market-ready. Here are just a few examples:
It maybe indeed easier for InsurTechs to implement business models based on these new technologies from scratch, but our experience at the nexus between banking and insurance shows that even the largest corporate entities can relatively easily enhance customer interactions, seeing a tangible cost-saving effect and positive impact on loyalty achieved through a combination of call centre automation and speech analytics.
There are a few clear trends in the sphere of biometrics and identification technologies that will also be affecting the insurance industry in 2017-2020:
An increasing number of successful startups will disrupt the status quo in the market in favour of less expensive, adaptable, and bespoke solutions with a clear-cut business value; and
Eventually, AI-powered analytics in conjunction with speech technologies such as semantic interpretation and emotion detection will be effectively helping contact centre specialists to be even better at persuasion, social understanding, and empathy – the creative qualities that will most likely remain up to humans to excel in. The ability to tap into the unstructured data, which constitutes approximately 80% of the world’s data at present according to IBM Watson specialists, will be yet another dimension that cognitive technologies will be offering companies, helping them to come up with insights and product ideas that meet the needs of their customers best.
It is definitely the right time to proceed from experimenting with speech technologies to a meaningful transformation of business processes in line with newly opened technological possibilities such as those provided by Spitch.