How the insurance industry can leverage AI to enhance efficiencies
This second machine age has seen the rise of artificial intelligence (AI), or intelligence that is not the result of human cogitation. AI is now ubiquitous in many commercial products, from search engines to virtual assistants.
The massive amounts and the speed at which structured and unstructured (e.g., text, audio, video, sensor) data is being generated has made speedy processing and generation of meaningful, actionable insights imperative.
The insurance industry segment has been conservative in adopting AI across the value chain, but P&C /Life/Reinsurance companies have accelerated the pace of AI adoption and initiated deployment of AI use cases across the value chain.
Here are few of the use cases leveraging AI for the insurance industry:
Personalised customer experience: redefining the value proposition
Many insurers are already in the early stages of enhancing and personalising the customer experience. Exploiting social data to understand customer needs and sentiments about products and processes (e.g., claims) are some early applications of AI.
The next stage in robo-advisor evolution is to offer better intelligence on customer needs and goal-based planning for both protection and financial products. Recommender systems and “someone like you” statistical matching will become increasingly available to customers and advisors.
Up next will be understanding of individual and household balance sheets and income statements, as well as economic, market, and individual scenarios to recommend, monitor and alter financial goals and portfolios for customers and advisors.
Automated and augmented underwriting: enhancing efficiencies
This involves automating large classes of standardised underwriting in auto, home, commercial (small and medium business), life, and group using sensor (IoT) data, unstructured text data (e.g., agent/advisor or physician notes), call centre voice data, and image data using Bayesian learning or deep learning techniques.
The industry will also model new business and underwriting process using soft robotics and simulation modeling to understand risk drivers and expand the classes of automated and augmented (i.e., human-performed) underwriting.
We will also see augmenting of large commercial underwriting and life/disability underwriting by having AI systems (based on NLP and DeepQA) highlight key considerations for human decision-makers. Personalised underwriting by a company or individual takes into account unique behaviours and circumstances.
This will help build predictive models for expense management, high value losses, reserving, settlement, litigation, and fraudulent claims using existing historical data. It will also help analyse claims process flows to identify bottlenecks and streamline flow, leading to higher company and customer satisfaction.
Building a robo-claims adjuster by leveraging predictive models and building deep learning models that can analyze images to estimate repair costs can change status quo. In addition, use of sensors and IoT to proactively monitor and prevent events can reduce losses.
A claims insights platform that can accurately model and update frequency and severity of losses over different economic and insurance cycles (i.e., soft vs. hard markets) can help the industry. Carriers can apply claims insights to product design, distribution, and marketing to improve overall lifetime profitability of customers.
Emerging risks and new product innovation
Identifying emerging risks (e.g., cyber, climate, nanotechnology), analyse observable trends, determining if there is an appropriate insurance market for these risks, and developing new coverage products in response historically have been creative human endeavors.
Man and machine learning
Artificial general intelligence (AGI) that can perform any task that a human can is still a long way off. In the meantime, combining human creativity with mechanical analysis and synthesis of large volumes of data – in other words, man-machine learning (MML) – can yield immediate results.
For example, in MML, the machine learning component sifts through daily news from a variety of sources to identify trends and potentially significant signals. The human learning component provides reinforcement and feedback to the ML component, which then refines its sources and weights to offer broader and deeper content.
Using this type of MML, risk experts (also using ML) can identify emerging risks and monitor their significance and growth. MML can further help insurers to identify potential customers, understand key features, tailor offers, and incorporate feedback to refine new product introduction.
AI implications for insurers
Improving Efficiencies: AI is already improving efficiencies in customer interaction and conversion ratios, reducing quote-to-bind and FNOL-to-claim resolution times, and increasing new product speed-to market. These efficiencies are the result of AI techniques speeding up decision-making (e.g., automating underwriting, auto-adjudicating claims, automating financial advice, etc.).
Improving effectiveness: Because of the increasing sophistication of its decision-making capabilities, AI soon will improve target prospects to convert them to customers, refine risk assessment and risk-based pricing, enhance claims adjustment, and more. Over time, as AI systems learn from their interactions with the environment and with their human masters, they are likely to become more effective than humans and replace them. Advisors, underwriters, call centre representatives, and claims adjusters likely will be most at risk.
Improving risk selection and assessment: AI’s most profound impact could well result from its ability to identify trends and emerging risks, and assess risks for individuals, corporations, and lines of business. Its ability to help carriers develop new sources of revenue from risk and non-risk based information will also be significant.