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The Robos Are Coming - How AI will revolutionize Insurance 0117

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The Robos Are Coming - How AI will revolutionize Insurance 0117

  1. 1. © 2016 Graham Clark @ Customer Results LLC “The Robos Are Coming –Artificial Intelligence And The Intelligent Insurance (And Banking And Financial Services) Business” A Discussion Primer For The CIO, COO & CFO BFSI Executive Insight Luncheon Prepared By Graham V Clark, Digital Transformation & Customer Experience Lead The Case For The Insurance Revolution In May 2013, Mckinsey Global Institute forecast in their revolutionary report “Disruptive technologies: Advances that will transform life, business, and the global economy” that of 230m “knowledge workers” or 9% of the global workforce comprising 9t or 27% of global employee cost in 2012 $5.2t – $6.7t or 57% - 74% of these roles could be automated by cognitive, robotic, semantic or interactive AI based software. Intelligent software systems that can perform knowledge work tasks involving unstructured commands and subtle judgment. Intelligent business systems and robots with enhanced senses linked to 1t internet of things / internet of everything devices today with 100m global Machine to machine (M2M) connections, both of which are growing at over 20% per year, growth which is accelerating. The BFSI (Banking and Financial Services & Insurance) industry has been at the forefront of technology driven change for the past 30” years, especially ‘stock trading’ where we have seen intelligent systems capture 80%+ of all global stock trades and famous trading floors like the NYSE in Manhattan and the LSE in London become devoid of humans as trading advantage goes to more sophisticated predictive
  2. 2. © 2016 Graham Clark @ Customer Results LLC machine learning based platforms combined with an obsession with microsecond and nanosecond advantages in infrastructure. However, Insurance will be next. We are here to discuss the business functions and activities that will be impacted (the “What”) and some of the key technology impacts (the “How”) 10+1 Intelligent Business Concepts and Capabilities There are a variety of Intelligent Business Concepts and Capabilities that need to be understood to reimagine the Insurance business, concepts and capabilities that are maturing very quickly to drive this transformation. 1. Intelligent Digital Assistants (IDA’s) – Made famous in the Hollywood movie “Her” and often referred to as chatbots combining digital personalities, Natural Language Processes (NLP), Semantic Interpretation and Machine to Machine Learning (MLM) an industry wave led by platform providers such as SRI (originators of Siri), Microsoft Cortana, Artificial Solutions, Eptica (www.askairasia.com) , Creative Virtual and a growing number of opensource frameworks these assistants bring a number of specific features and functions to business; 1) multilingual – up to 0 languages 2) massive multiscale handling thousands of conversations at once 3) highly personalized – phrasing, preferences, history even friends 4) self learning – every interaction with every person builds the knowledge base whether applied to a customer experience, employee assistance such as underwriters 5) uncaged – these IDA’s can integrate with global data and knowledge inside and outside the industry through technical and business API’s. 2. Unstructured Data – A core to the Intelligent Business conundrum is that the vast majority of data and information in the world is not structured in convenient database tables for software programs to process. Spoken language, freeform text documents and video and visual and even people to people are examples of data sources that provide immense sources of useful information which is not always easy to extract and act upon. 3. Semantic Analysis and Kernels – Semantic Analysis is the process of using software kernels to make sense and interpret language (whether spoken or written) to derive meaning, this meaning can then be added to structured data to aid in decisioning and action. 4. Cognitive & Behavioral Analysis & Decisioning - Cognitive analysis methods focus on the psychological processes underlying a task in order to be able to repeat and even optimize task performance. Behavioral analysis involves the prediction of action based upon personality traits, cultural origins, learned and innate tendencies. Both Cognitive Science and Behavioral Science are evolving rapidly and increasingly drive predictive and prescriptive analytic capabilities from basic (Real Time) Offer Management to experience personalization. The most widely recognized “cognitive” platform globally today must be IBM Watson but many others exist in research and commercial applications 5. Computer Vision Analysis (CVA) – As Semantic Analysis relates to computers understanding of words, text and speech Computer Vision Analysis relates to the application of computer programs to
  3. 3. © 2016 Graham Clark @ Customer Results LLC understand visual experiences, online and offline videos, even facial meaning (which quickly overlaps with behavioral and cognitive analysis given that 70% of a conversation’s meaning is in facial and non-verbal communications. Famous applications of CVA include the identification of various terrorism suspects through video based facial recognition and tracking (e.g. London bus bombers) to UC experience design using facial elements. 6. Augmented Reality / Virtual Reality (AR/VR) – If Computer Vision is the inbound interpretation of visual activities then AR / VR is its opposite, the creation of visuals by computers. Augmented Reality means overlaying visual items on real world experiences (a world brought to the fore recently by Microsoft Hololens) and Virtual Reality means the creating of entire computer generated visual experiences (as demonstrated by Facebook’s Occulus Rift). Making AR/VR work requires deep CVA, Semantic and Behavioral components. 7. Application Program Interface (API) – the way that a computer program interacts with another computer program. API’s increasingly conform to standards which allow programs to connect automatically and with minimal to no customization. One of digital’s great innovations is the focus on publicly agreed and available standard interfaces and the term “plus and play” that allows an iPhone to seamlessly Bluetooth connect to a new wearable or beacon for example. 8. Internet Of Things / Everything (IOT / IOE) – Increasingly devices from wearable computers (e.g. Fitbit, Apple Watch) to beacons and sensors (which allow devices to know where they are and data to be collected such as weather sensors) are being joined by more specific devices such as home monitoring and control (Nest etc.) to Automobile Telematics and Industry specific devices (monitoring chemical composition of factory outflows). The growth of IOT/IOE and especially the open, commonly accessible components will provide one of the deepest impacts on insurance. 9. Machine to Machine Learning (M2M) – Exactly as it sounds this represents connecting two automation systems (machines) together and allowing them to learn as a results of the connection. The most sophisticate M2M models involve two or more connected machines each of which is AI enabled. As human beings gain more sophisticated and preferential capabilities by involving people with different experience and knowledge. M2M learning environments provide better and exponentially richer capabilities. 10. Robotic Process Automation (RPA) – The collision of these concepts has fueled a revolutionary technology ecosystem referred to as RPA. RPA focuses on massively repeatable but less than straightforward processed (processing insurance claims, adjusting product pricing tables, performing contact center related customer service tasks etc). An industry revolution led by globally recognized companies in Contact Center such as Jacada and Openspan (recently acquired by PegaSystems) and more generally by BluePrism and UIPath, RPA is currently focused on automating basic and often low cost processes which have been the focus of offshoring but are quickly elevating to more complex and more sophisticated tasks such as medical assistance and underwriting assistance.
  4. 4. © 2016 Graham Clark @ Customer Results LLC 11. Decision Support Systems I(DSS) to Intelligent Business – Finally it is worthwhile to remember that today’s Intelligent Business world is part of journey which started with the earliest Decision Support Systems (DSS) that provided analytics insights and occasionally actionable recommendations. Impact By Insurance Industry Function Here we discuss, a number of key changes that are happening now in the Insurance Industry as a result of the AI driven Intelligent Business revolution. Customer, Producer/Partner and Employee Experiences – AI based and driven experiences will provide customers with better product recommendations, arm those who serve customers with more powerful tools to serve them better, increase the amount of self service across customer segments, create greater intimacy and emotional resonance with all types of customers and provide those companies who take advantage of these capabilities and take advantage of them with Product Offerings – Intelligent Business capabilities and digital offering platforms allow for significant personalization of product offerings from micro customization to more personalized presentation. Additionally insurance providers are starting to offer products which incorporate M2M and AI Intelligent Business. Telematics enabled driver insurance products are in their infancy, Home insurance products that demand home automation and sensing, commercial insurance that demands the business to install monitoring devices that not only increases mutual visibility but will reduce the risk inherent in the relationship (think of a chemical plant with waste monitoring devises that prove global and national ecological compliance or automated intelligent quality insurance in a hospital reducing medical malpractice risk or automated crop weather monitoring). It is only a matter of time until a life and medical insurance company offers products based on injectable monitoring a patient’s diet, exercise regime, smoking habits etc. This also effects reinsurance as packages of risk managed policies such as this will command premiums in the market. It is also likely that dynamic insurance will become possible whereby insurance changes very fast, and is offered accordingly, think home insurance purchased monthly for a vacation home based on occupancy. Cost of Operations & Product Pricing – As with all things digital this Intelligent Business revolution will have significant impacts on cost of operations. The next years will be about perfecting the Intelligent Business Model but the replacement of highly paid knowledge workers with automation (see McKinney comment at the beginning of this document will have a significant impact on operating cost which initially will lead to profitability improvements but ultimately will be competitively squeezed into a new financial operating model. A great and very recent model is that of “Lemonade” a consumer and small business model of which I have ‘unreasonable’ knowledge. Underwriting – Underwriting is one of the most complicated (and expensive) functions in insurance in underwriting. Underwriting involves deep knowledge and broad experience and has largely been beyond computerization except at the simplest policy level, such as basic consumer auto insurance, which can be founded on simple decision tables. With the volume of data inputs increasing exponentially every quarter to make appropriate decision and the risks associated with those decisions increasing in importance plus with computers are able to think and even act as well as or better than people more underwriting tasks will become digitally handled. Current trials have indicated that AI
  5. 5. © 2016 Graham Clark @ Customer Results LLC based systems can initially help underwriters as knowledge systems but will ultimately replace many underwriters Reinsurance – Reinsurance is the underpinning of the insurance industry. One of my most exciting endeavors has been my recent involvement with the “London Markets” (aka Lloyds). Lloyds Target Operating Model (TTOM) is fundamentally a Digital Operating Model (DOM) which brings Intelligent Business principles with other new global digital revolutions like Bitcoin to fundamentally transform an institution that has been in place since February 1688, when Edward Lloyd’s Coffee House in Tower Street was referred to publicly for the very first time in the London Gazette as a key location for global transportation insurance. Employment – As with most other automation revolution it is projected that (and an underpinning of the McKinney study) automation will ultimately replace a significant portion of sophisticated employment roles. The general yardstick is that “automaton can replace 80% of workers in any job, 20 of the most flexible, best performers will remain” (ask a 1960’s auto worker or 1980’s stock broker) Competition – The most unpredictable, fear inducing and exciting component of the revolution (depending on who you are) is the competitive landscape. The new Intelligent Insurance landscape will welcome new entrants we have not seen before (Amazon and Alibaba being the most obvious elements but others will attack institutional players with new business models. Some of these providers will offer targeted, highly personalized business models (e.g. Lemonade ?), some will offer different business models (e.g. mobile phone based business deal insurance, real-time for a $500m shipping contract) and will also include new margin models. One of my favorite inaccurate but directionally correct examples is Walmart in the USA. Traditionally a local family owned supermarket targeted 8-10% net income. Walmart operates at 3-4% which makes them unassailable from a competitive perspective. Amazon in turn operates at <1% Net Income. How will this work in insurance producer and supplier models?

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