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Impact of Artificial Intelligence and knowledge work automation – how it will affect insurance?

Impact of Artificial Intelligence and knowledge work automation – how it will affect insurance?

Impact of artificial intelligence and knowledge work automation – how it will affect insurance?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.

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:

  • Automated processing of damage reporting calls and the accurate collection of accident information from voice-driven IVR, during which customers provide important factual information. The automated forms fill-in and fully searchable speech-to-text customer conversation protocols are powered by the advanced speech recognition technology. Numbers are recognized reliably no matter how they are pronounced;
  • Semantic interpretation, speech analytics, sentiment analysis, and emotion detection adding specific types of information to verbal exchanges, which can help improve customer service and monitor quality (e.g. adherence to scripts, compliance issues etc.) in real time and on all calls going through the system;
  • Automating enquiry calls and providing answers to customers from the knowledge base: in case the required information is not there for any reason, the follow up call back option is available.
  • Online application password reset using speech recognition and voice biometric identification/verification, as well as the address change and other personal details updating;
  • Many of these options can be combined with or delivered entirely through chatbots or specific chat functions, where input is done via a voice user interface (VUI). It would be possible for customers to find out at any time, e.g. how much of the car insurance deductible is left by asking this question in a chat or voice-driven IVR dialogue, and either get the information straight away or make sure the insurance specialist receives this enquiry soonest and calls back.

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:

  • Multimodal biometrics solutions will increasingly include voice biometric component, following the opti-channel philosophy;
  • Digital identification and system-driven security checks will become standard following the introduction of voice biometric identification working in real time even on very large populations of ‘voiceprints’;
  • BFSI sector (banking, financial services and insurance) will continue to lead in the number of implementations of speech recognition solutions and multimodal biometrics with a voice factor;
  • 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

  • AI personal assistants and chatbots in insurance will rely on voice user interfaces for improved customer experience.

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.

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