Digital Innovation - A Quick Overview

Transcription

1 Digital Innovation Talks Asociación de Aseguradores de Chile, AG Abril de 2016

2 Emerging Tech Speeds Up the Creation of New Business Models Accommodation The world's largest hotel chain, without owning a single hotel Taxi Services The world's largest taxi company, without owning a single car Source: UBER; Airbnb; The Wall Street Journal; Mundiventures

3 Agenda Definitions: Digitalization & Millennials Unicorns in Insurance Tech: Zenefits & Oscar & Zhong An Munich Re & Insurance Industry Approach to Digital Innovation Top Global Digital Innovation Trends & Insurance Impact An Overview to Digital Insurance Innovation in Latin America What is next?: Target Picture 2020

4 Definitions

5 Digitalization Innovation: The alteration of what is established by the introduction of new elements or forms. Digitalization Technology: The branch of knowledge dealing with the mechanical arts and applied sciences. ZEJ WOJCICKI/Science Photo LibraryRF Digital electronic device: That makes use of data in digital form. Typically contrasted with analogue. Is the creation of business in the digital world. Source: Oxford English Dictionary Source: Gartner Inc.

6 Millennials

7 Unicorns in Insurance Tech: Zenefits & Oscar & Zhong An

8 Zenefits Source: Zenefits

9 Oscar Source: CB Insights

10 ZhongAn Source: CB Insights; Munich Re

11 Unicorns in Insurance Tech Source: CB Insights KPMG Pulse of Fintech 2015

12 Munich Re & Insurance Industry Approach to Digital Innovation

13 Munich Re: The Digital Innovation Circle Source: Munich Re

14 Innoscouts in Silicon Valley

15 Partnership in London Leading Accelerator based in London looking for 10 Startups to rock the world of insurance!

16 Partnership in Munich

17 Our Own Accelerator in Madrid Source: Munich Re

18 Global Insurance Response to Digitalization Source: Mundiventures; CB Insights

19 Top Global Digital Innovation Trends & Insurance Impact

20 3D Printing Materials Haptic Technologies Industrialization 4.0 The Device Mesh New Payment Models Ambient User Experience Augmented and Virtual Worlds Cloud/Client Architecture Telematics Web-Scale IT Digitalization Smart Home Wearable Devices Internet of Things Loc-based Services Digital Health Services Information of Everything Artificial General Intelligence Advanced Machine Learning Web 5.0 Quantum Computing Smart Machines Autonomous Agents Autonomous Vehicles Predictive Analytics Algorithmic Business Robotics/Drones Smart Dust Data driven Decisions Context-aware Computing Cybersecurity Crowd Sourcing Blockchain Technology User Centered Design Open Data Source: Munich Re Adaptive Security Architecture Digital Identity Collaborative Consumption

21 T r e n d Artificial General Intelligence Description Artificial General Intelligence describes the ability of computers to simulate higher cognitive abilities of humans. Some of the scopes in Artificial General Intelligence are natural language processing, speech recognition and image processing. The development of Artificial General Intelligence is not a technology trend, but serves as a basic technology for trends like algorithmic business, advanced machine learning or autonomous agents. Examples and Initiatives External examples Toyota invests $1 Billon in a Artificial Intelligence Research Institute in the silicon valley focusing on safer driving technologies. Google Deep Mind is capable of learning for themselves directly from raw experience or data, and are general in that they can perform well across a wide variety of tasks Allen institute conducting high-impact research and engineering in the field of artificial intelligence Internal initiatives Benefits Artificial General intelligence will not create a single market (in insurance sector), but will aggregate markets and drive technologies based on Smart Machines and Advanced Machine Learning. Related Trends Algorithmic Business Advanced Machine Learning Autonomous Agents Level of relevance Adopt Trial Assess Hold Source: Munich Re

22 Artificial General Intelligence (Cognitive Learning) Source: IBM

23 T r e n d Wearable Devices Related Trends Description Wearable Devices incorporate advanced electronic technologies and can be worn by the users, mostly on the wrist. They track several body related data, like health and fitness information such as movement, steps and heart rate. Often, they have the opportunity to sync data from the mobile phone, e. g.: messages, phone calls or appointments. Level of relevance Examples and Initiatives External examples Vitaliti is a wearable health system with sensors measuring and supervising body functions all simultaneously, logging to the cloud and a connected smartphone or tablet. The Ralph Lauren polotech shirt works with iphone or Apple watch and transmits real-time workout data to your device. Intelligent working suit recognizes work-overload. Internal initiatives Benefits Wearables serve as means of operational health promotion and should be considered in health insurance products. They offer an additional on demand channel for customer services of primary insurers. CRM-systems can be integrated in wearables and strengthen customer loyalty. Wearables provide an opportunity of collecting data about the insurance holder which help customizing insurance products. Internet of Things Loc-based Services Digital Health Services Adopt Trial Assess Hold Smart Home Source: Munich Re

24 Wearable Devices Source: Vitality Health - Life

25 T r e n d Robotics/Drones Related Trends Description In general, Robots and drones are machines that solve problems using Artificial intelligence (AI). Commonly used in manufacturing, robots are entering the markets for private usage including imitating human behavior. After CES 2015 s Year of the drones in 2016 more functionalities in real estate, law enforcement or disaster relief are introduced. Artificial General Intelligence Internet of Things Level of relevance Adopt Trial Assess Examples and Initiatives External examples AeroVironment developed the Nano Drone to develop a new class of air vehicle systems Pepper is the first humanoid robot with the ability to recognize and react to people s emotions. The Robot is already used in Japan to interact with customers in stores. Skycatch is a drone-meets-industrial-big-data startup that provides unmanned aerial vehicles for autonomous aerial data capture Internal initiatives Benefits Perform well under challenging conditions such as inclement weather. Drones can be usefully applied in major claim settlement: They enable a deeper analysis of major damages as they enter areas or acquire data humans are not able to gather. Drones hold vast potential for streamlining and reducing the cost of insurance-related processes from claims adjustment and risk-engineering, to post-catastrophe claims settlements for customers, to weeding out fraudulent agricultural claims. The data it captures can then be visualized and analyzed by workers on the ground via user interface and data analysis tools Smart Machines Hold Advanced Machine Learning Source: Munich Re

26 Drones Source: CB Insights

27 Relative Experience T r e n d Predictive Analytics Description Examples and Initiatives External examples Benefits The giant amount of produced data offers a wide range of analytical usage not only describing processes using data, but predicting them. Predictive analytics enable forecasting by extending existing data into the future. Predictive Analytics serve as a basis for optimizing and even for automating decision processes. For this purpose, its major challenge is to filter the amount of data provided, keep the analysis process itself lean and generate reliable statements. Chalmers develops a system that forecasts innate behavior in the moment of steering the car in critical situations and corrects them. Mixpanel analyses the behavior of website user. User can be recognized based on statistics and actions can be foreseen. Public Sonar Detect events. Track incidents. Manage crises via real time social media Internal initiatives Best Group Medium Group Worst Group Predictive Analytics make a big contribution to risk prediction for ERGO and Munich Re: increase of confidence, reduction of uncertainty. Implementation of data filters and tracking tools to test effectivity and raise efficiency. Discovering meaningful data patterns, enabling future scenarios and forecasts. Related Trends Algorithmic Business Data driven Decisions Level of relevance Adopt Trial Assess Hold SA 25k 25k < SA 50k 50k < SA 80k 80k < SA 110k 110k < SA 180k 180k < SA 260k SA > 260k Sum Assured Band Prescriptive analytics automates the recommendation and action process and generally is based on machine learning techniques that evaluate the impact of future decisions and adjust model parameters based on the difference between predicted and actual outcomes. Advanced Machine Learning Source: Munich Re

28 T r e n d Telematics Description Telematics combines technologies of telecommunications with transportation. Telematics give insights about the location-based usage of objects, often cars, and open up new possibilities of real-time services like usagebased car insurance. Examples and Initiatives External examples AXA telematics app: the driveability of young drivers is being assessed based on user generated data Generali acquires English start-up My Drive Solutions which uses data analysis for the development of new smart insurance products. Benefits Telematics give the possibility of flexible car insurances based on tracked (and shared) Telematics application data. Through the Telematics application helpful data can be collected. That data improve the products, for example cars and increases the safety for human and material. Internal initiatives New business models like usage based car insurance with realtime sync. Related Trends Internet of Things Loc-based services Smart Home Collaborative Consumption Source: Munich Re Level of relevance Adopt Trial Assess Hold Better analysis of crashes and damage regulation by providing more and richer data.

29 Telematics What information does the box record? The data from a telematics box tells us: the time of day or night you drive the speed you drive at on different sorts of roads if you brake or accelerate sharply if you take breaks on long journeys your motorway miles your total mileage the total number of journeys you make. Source: Insure the box

30 T r e n d Autonomous Vehicles Description Autonomous vehicles are vehicles which can drive themselves without human supervision or input. In addition to unmanned vehicles autonomous vehicles are not just controlled remotely, but control themselves or drive through a controlled environment. Vehicles can also operate semi-autonomously: taking some control of aspects of their driving, whilst a human driver retains control of others. Examples and Initiatives External examples Robottaxis are self-driving cabs that combine selfdriving technology with internet-powered services and are currently operating in Japan. Wepods are driver-less mini-busses circulating within the public transport system. John Deere AutoTrac: Self-driving system for smart tractors makes use of past harvest data for higher harvesting Benefits Higher safety and minimization of damage ( Reduce human mistake interaction ). Transformation of the third-party liability risk is needed. Hacking autonomous systems as a major threat and potential for insurance product development. see also: Cybersecurity Internal initiatives Considerations: Insurance not longer necessary, because of the reduction of damage. Related Trends Context-aware Computing Internet of Things Robotics/Drones Level of relevance Adopt Trial Assess Hold Source: Munich Re

31 Autonomous Vehicles Source: ABC; NHTSA

32 T r e n d Collaborative Consumption Description Technology allows us to make the processes of purchase transparent. Social media boosts the idea of a sharing economy/ shareconomy in which the act of purchase goods is replaced by sharing them with others. Successful shareconomy business models implementing collaborative consumption: transport: carsharing ( Uber, DriveNow ) accomodation ( AirBnB ) finance: crowdfunding ( Kickstarter ) working: coworking spaces ( Betahaus Berlin ) Examples and Initiatives External examples Insurance sharing platform Friendsurance with 6 new management hires till end of Transfercar is a service that helps rental car companies to transfer cars between their branches, while offering a way for travellers to travel for free. Sharing electricity: Yeloha is a Solar Sharing Network. Internal initiatives Benefits Collaborative consumption of goods reduces waste production and raises resourcesconsciousness. Connecting people, goods and services, it will have a deep impact on insurance holders and their needs. Consideration: Will collaborative consumption transform clients of insurance companies? Related Trends Crowd Sourcing The Device Mesh New Payment Models Level of relevance Adopt Trial Assess Hold Source: Munich Re

33 Collaborative Consumption Source: Friendsinsurance

34 Collaborative Consumption Source: Munich Re

35 Collaborative Consumption Source: Guevara

36 An Overview to Digital Insurance Innovation in Latin America

37 Level of digitalization The Critical Path for Digital Insurance Innovation Timeline Source: Munich Re

38 Level of digitalization Digital Back-end: Risk Inspection & Claims Digital Apps Timeline Munich Re can no guarantee that information is accurate as of today or that it will continue to be accurate in the future. Source: Munich Re

39 Level of digitalization Digital Back-end: Drones Timeline Munich Re can no guarantee that information is accurate as of today or that it will continue to be accurate in the future. Source: Munich Re

40 Level of digitalization Digital Front-end: Point of Sale Automatic Underwriting Timeline Munich Re can no guarantee that information is accurate as of today or that it will continue to be accurate in the future. Source: Munich Re

41 Level of digitalization Digital Front-end: Predictive Analytics Munich Re can no guarantee that information is accurate as of today or that it will continue to be accurate in the future. Timeline Portfolio of fully underwritten applications & UW decisions Customer data Link data using name, DOB and address Portfolio of fully underwritten applications & UW decisions Customer data 100.0% 90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% Postponed / Declined High Rating Low Rating Standard 20.0% 10.0% 0.0% Segment Source: Munich Re

42 Level of digitalization Digital Front-end: Wearables Timeline Munich Re can no guarantee that information is accurate as of today or that it will continue to be accurate in the future. Source: Munich Re

43 Level of digitalization Other Digital Innovation Initiatives Munich Re can no guarantee that information is accurate as of today or that it will continue to be accurate in the future. Timeline Artificial General Intelligence (Cognitive Learning) Telematics Source: Munich Re

44 Level of digitalization Brand New Digital Timeline Munich Re can no guarantee that information is accurate as of today or that it will continue to be accurate in the future. ThinkSeg Venture Capital Youse Caixa Seguradora & CNP Source: Munich Re

45 Insurance tech startups in Latin America Munich Re can no guarantee that information is accurate as of today or that it will continue to be accurate in the future. Source: Munich Re; Mundiventures; Mundilab

46 What is next: Target Picture 2020

47 The (good) old days Source: Munich Re

48 Target Picture 2020 Source: Munich Re

49 Digital Innovation Talks Asociación de Aseguradores de Chile, AG Abril de 2016

50 Münchener Rückversicherungs-Gesellschaft ( Munich Reinsurance Company ) is a reinsurance company organized under the laws of Germany. In some countries, including in the United States, Munich Reinsurance Company holds the status of an unauthorized reinsurer. Policies are underwritten by Munich Reinsurance Company or its affiliated insurance and reinsurance subsidiaries. Certain coverages are not available in all jurisdictions. This presentation contains forward-looking statements that are based on current assumptions and forecasts of the management of Munich Re. Known and unknown risks, uncertainties and other factors could lead to material differences between the forward-looking statements given here and the actual development, in particular the results, financial situation and performance of our Company. The Company assumes no liability to update these forward-looking statements or to conform them to future events or developments.