• Abstract and Figures
• Public Full-text

IBM Global Business Services Insurance White Paper Shaping the future of insurance with IBM Watson Redefine the next-generation customer experience 2 Shaping the future of insurance with IBM Watson As the insurance market becomes digital, digitally-savvy notes, and adjuster notes), 2) structured information (such as customers, based on their experience with other industries transaction records pertaining to policy and claims) and 3) a such as retail, expect insurers to provide the same advanced combination of structured and unstructured information (such customer experience to them during their interactions. They as claim history and billing and payment history) to come out expect insurer’s to know them in advance when they call, with meaningful and relevant answers to a question or query in proactively engage with them after gathering relevant natural language from a customer in a live chat with Watson. information from data such as social media and call center Watson also has the capability to generate a hypothesis logs, and provide them with proactive advice and guidance. (describe the evidence) that it uses to make a recommendation For this paper, the term ‘customer’ includes all parties that to the customer. Watson understands contextual interactions receive or provide services to an insurance company (for and continuously updates its knowledge with evidence-based example, customer service representatives (CSRs) and brokers, learning. claimants, and third parties such as administrators, independent adjusters, and outside counsel). Insurers can derive the following benefits by deploying Watson: To meet these changing customer expectations, Life and Property and Casualty (P&C) insurers are looking for Benefits Associated key performance indicators innovative solutions that can enable them to shape the future (KPIs) of insurance by improving customer experiences. They are Increased revenue • Number of new customers looking for intelligent automated chat solutions with self- • Number of customer referrals • Price by customer segment service capability at their call centers, with which the • Percentage of policies upgraded customers can chat in natural language just as they chat with a • Policy premium to personnel cost CSR and obtain the required support quickly in real time. • Average policy size IBM Watson delivers this next wave of customer engagement Deepened • Client retention rate capability to insurers through cognitive computing capability. customer • Policy renewal rate relationships • Net promoter score • Number of customer complaints IBM Watson can fundamentally change how customers and Improved efficiency • Percentage of call deflections insurance companies interact and can help increase the and reduced cost • Contact ratio by channel customer’s trust and confidence in the insurer’s capabilities. • Average handling cost of claims • Transfer rate At the core of what makes Watson different are three powerful • Average handling time technologies: natural language, hypothesis generation, and • First call resolution evidence-based learning. Watson is about bringing these capabilities together in a way that has never been done before, resulting in a fundamental change in the way insurers can look at quickly solving customer’s problems in a real time chat with Watson. Drawing upon its ability to navigate natural language, Watson can quickly go through volumes of 1) unstructured information (such as blogs, call center logs, posts, policy documents, declaration pages, endorsements, underwriting IBM Global Business Services 3 The history of Watson What is Watson? IBM has a historical commitment to research - investing $6 IBM’s Watson represents a system with the next wave of billion per year. Watson was a product of this commitment. technologies. It is a cognitive-era system, a system that learns Watson enables humans to chat with it in natural language and understands natural language just like human beings. through a feature called ‘Watson Personal Advisor’ (Powered There is no need to program every output with complex by Watson). programming logic. It is a new species that is taught by design, learns by experience, learns from interactions, gets smarter The ‘Watson Personal Advisor’ capability was tested for the over time, and makes better judgments over time. first time during an American quiz show, Jeopardy, and won against all human competitors present in the first clash Watson reads exponentially growing insurance industry-related between humans and machines in the history of the show. data in the form of thousands of articles, publications, books Jeopardy was selected as the ultimate test of Watson’s and so on. As it reads, it learns and enhances its knowledge. As capabilities because it relied on many human cognitive abilities it learns, it connects the dots with what it just read with what it traditionally seen beyond the capability of computers, such as: already knew. Watson can process thousands and thousands of unstructured documents available in different formats and it • Natural language processing understands the content. Watson provides the capability to • Hypothesis generation interact with the big data in a new way — natural language. • Evidence-based learning Once it understands the content of the various documents it has read, we can ask questions in natural language. It responds in a chat in natural language just as human beings respond to a question in a friendly chat. When we ask questions, it generates System Intelligence a hypothesis (potential answers) with a level of confidence. If it doesn’t get the right answer for the first time, we need to help it to get the right answer and then it learns. It just doesn’t learn from what it knows today, it keeps on updating its knowledge continuously. Watson runs thousands and thousands of Programmatic Cognitive algorithms that run, improve, get better and more algorithms • Search • Discovery are created as it learns. It is about relations and correlations Tabulation • Deterministic • Probabilistic • Enterprise data • Big Data that can be difficult for humans to understand. It understands • Punch cards • Machine language • Natural language the implications of a customer’s questions; it can also ask the • Time card readers • Simple outputs • Intelligent options customer some clarifying questions, where needed. 1900 1950 2011 Enabling new opportunities and outcomes 4 Shaping the future of insurance with IBM Watson Watson is a massively parallel system Learned models help combine and weigh the evidence Models Models Inquiry? Answer sources 1 Evidence sources Models Models Models Models Primary Candidate Evidence Deep Answer 6 search answer scoring retrieval evidence generation scoring 2 Final confidence merging and Inquiry/topic Inquiry Hypothesis Hypothesis and evidence Synthesis ranking analysis decomposition generation scoring 4 5 3 Hypothesis Hypothesis and evidence generation scoring Responses with confidence Watson is trained to understand, discover and learn “Ingest” relevant Understand an Generate Find evidence Rank responses Train and Adapt data across a broad ambiguous English potentially relevant to support with “confidence” through Machine domain to create a language inquiry responses responses factors Learning repository 1 2 3 4 5 6 How can Watson help insurance Insurance customer engagement companies? challenges Innovative insurance business leaders are exploring the Long wait times to speak to a CSR have been commonplace. possibility of driving the next level of customer engagement Technology savvy insurance customers are becoming increasingly with cognitive computing. IBM Watson enables such frustrated with the impersonal, generic customer service they transformational engagement. Innovators in the insurance usually receive from insurance company call centers coupled with industry are leveraging this disruptive technology now. the inability of the CSRs to: IBM Global Business Services 5 Know their customers well the CSR, or 2) They can deploy it in the call center as a tool to • No identifier information at the point of answering — the CSR help their CSRs quickly glance through the enormous amounts does not know who is calling and what products they have of big data relevant to the customer and quickly come out with with the company appropriate recommendations. • No access to social media data — no information beyond what they can obtain by querying different applications (which is Under the first option, individual customers and prospects can mostly structured data) interact with Watson in plain English (similar to the way they • Impersonal — scripted speech interact with a CSR in a chat) directly in a chat through their • Process driven — not personal smart phone mobile app after authenticating and clicking the • Circumspect — not trusting ‘Watson Personal Advisor’ button. They can type in questions in plain English such as “Hey Watson, if I let my neighbor Engage them continuously borrow my car and he gets into an accident will I be covered?” • Insulting — not engaging In seconds, Watson searches through all the relevant • Confusing — conflicting instructions documents and can come back with a recommendation about • Maddening — lack of resolution coverage to his vehicle when his neighbor is driving his vehicle. Empower them After going through the Watson reply, if required, the • Time consuming — long waits customer can chat with Watson and ask ‘Hey Watson, please • Inconvenient — hours of operation, channels supported show me the evidence

Load more

  12

Copy Link