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AI & Machine Learning - Webinar Deck

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Listen to an experienced, global panel of insurance professionals present, discuss and answer your questions on the theme of “AI & Machine Learning”.
Brought to you by The Digital Insurer and sponsored by KPMG.

Published in: Economy & Finance
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AI & Machine Learning - Webinar Deck

  1. 1. 1 Why Telematics? Webinar: Insurance Aggregators In Asia 29th September 2015 Webinar: AI & Machine Learning
  2. 2. Our Panelists
  3. 3. Discussion agenda 1 Presentations: Gary Richardson: How will insurers derive value from machine learning Adrien Cohen: AI & motor claims assessment Juergen Rahmel: Technical considerations for AI Alberto Chierici: Chatbots & customer service David Robson: Enterprise view of AI 2 Questions and Answers 3 Snap Poll – share your view
  4. 4. Questions & Answers How to participate: If you have a question please type into the messaging area and send to all participants Session format: The moderator will use a combination of his own questions and those from the audience
  5. 5. Gary Richardson @KPMG: How will insurers derive value from AI?
  6. 6. Two sides to great AI Gary Richardson @garydata
  7. 7. 7 Document Classification: KPMG Public © 2016 KPMG LLP, a UK limited liability partnership and a member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. The Human Side Building the team Global Alignment Educating Leadership Investing at Scale Governing Obsessive Data Collection Track benefits
  8. 8. 8 Document Classification: KPMG Public © 2016 KPMG LLP, a UK limited liability partnership and a member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. The Machine Side Model Portability Streaming Data Visualisation Schema on read Scaling Automation Open API’s
  9. 9. Adrien Cohen @Tractable: AI & motor claims assessment
  10. 10. 10 TRACTABLE MISSION : LEARN EXPERT VISUAL TASKS WITH ARTIFICIAL INTELLIGENCE  Past 3 years have seen fundamental breakthroughs in computer vision via deep learning  Deep learning systems now surpass human accuracy in certain recognition tasks  Tech giants (Google, Facebook…) are applying it to generic visual recognition tasks for consumer applications Image classification error rate Our mission is to identify and build commercially disruptive applications of computer vision Our focus is insurance claims
  11. 11. 11 TEAM OF 20 BACKED BY SILICON VALLEY VC, UNIQUELY POSITIONNED IN MOTOR WITH MITCHELL DATASET  Raised one of the largest EU seed rounds of 2015 from West Coast Investor  Prof. Z. Ghahramani, head of ML @ Cambridge both an investor and advisor BACKERSTEAM  Partnership with Mitchell in the US, leading insurance claims player  Transfer dataset of 350M images + estimates: enables training AI to superhuman performance  Tractable uniquely positioned with deep learning tech & data PARTNERSHIP IN THE US  Founding team of 3 with previous $bn exit  R&D team of 10 with 30+ years combined research and 1000+ citations
  12. 12. 12 PRODUCT VISION : HOW TRACTABLE AI WILL CHANGE P&C INSURANCE Automated Bodyshop Adjustment Customer Self Service  Generate preliminary repair estimate at FNOL from photos  Applicable to auto and home  Settle low severity claims in minutes  Flag unnecessary repair procedures from photos  Collaborative workflow with the bodyshop  Contain leakage on high volume low value claims Total Loss Triage  Triage between repairable and total loss at FNOL from photos  Avoid unnecessary towing operations and storage fees  Manage policyholder expectations early on in process  Automate analysis of drone footage  Elastic response to claim spike during catastrophic hail events  Maintain efficient cycle times Roof inspection Hail inspection  Count dents & measure depth from photos  Elastic response to claim spike during catastrophic hail events  Maintain efficient cycle times 1 2 3 4 5 DescriptionProduct
  13. 13. Dr. Juergen Rahmel @AI Advisor: Technical considerations for AI
  14. 14. 14Dr. Juergen Rahmel Understanding Artificial Intelligence – a Tool Box Source: Cognitive Architectures: Research Issues and Challenges by Pat Langley, John E. Laird and Seth Rogers Decision Making Planning Reasoning Prediction Data Intake Processing Interaction
  15. 15. 15Dr. Juergen Rahmel Understanding Artificial Intelligence – a Tool Box Source: Cognitive Architectures: Research Issues and Challenges by Pat Langley, John E. Laird and Seth Rogers Perception Decision Making Planning Reasoning Prediction Execution CommunicationRecognition Data Intake Processing Interaction
  16. 16. 16Dr. Juergen Rahmel Understanding Artificial Intelligence – a Tool Box Source: Cognitive Architectures: Research Issues and Challenges by Pat Langley, John E. Laird and Seth Rogers Perception Reflection and Learning Decision Making Planning Reasoning Prediction Execution CommunicationRecognition Data Intake Processing Interaction
  17. 17. 17Dr. Juergen Rahmel Understanding Artificial Intelligence Integration Example: A simple ‘Chat Bot’ solution – a talkative FAQ Reasoning ExecutionRecognition
  18. 18. 18Dr. Juergen Rahmel Understanding Artificial Intelligence Integration Example: A simple ‘Chat Bot’ solution – a talkative FAQ recognize simple keywords search internal rule base reply best possible predefined answer Reasoning ExecutionRecognition Customer: “I had a car accident, what to do now?” Chat Bot: “Call Police to record the case. Later, please submit case number via your Insurers Website… ” Data / Rule Base: …”opening hours”  Mon-Fri ….. ...”special offers”  Offer a/b/c... ...”accident”  next steps ... ...”claim”  claim process ....
  19. 19. 19Dr. Juergen Rahmel Understanding Artificial Intelligence Integration Example: A simple ‘Chat Bot’ solution – a talkative FAQ recognize simple keywords search internal rule base reply best possible predefined answer Corporate Network Reasoning ExecutionRecognition Customer: “I had a car accident, what to do now?” Chat Bot: “Call Police to record the case. Later, please submit case number via your Insurers Website… ” Data / Rule Base: …”opening hours”  Mon-Fri ….. ...”special offers”  Offer a/b/c... ...”accident”  next steps ... ...”claim”  claim process ....
  20. 20. 20Dr. Juergen Rahmel Understanding Artificial Intelligence Integration Example: A complex ‘Chat Bot’ solution – a conversational Advisor Corporate Network Reasoning Recognition recognize intention clarify intention Communication Customer Data Product Data Customer: “I am thinking about increasing my family protection” Customer: “I want to buy an education insurance”
  21. 21. 21Dr. Juergen Rahmel Understanding Artificial Intelligence Integration Example: A complex ‘Chat Bot’ solution – a conversational Advisor Corporate Network Reasoning Recognition recognize intention clarify intention Communication Communication identify offering customize offering Planning Prediction Reasoning Decision Making Customer Data Product Data Customer: “I am thinking about increasing my family protection” Customer: “I want to buy an education insurance” Chat Bot: “We propose the following options in your situation…” Chat Bot: “…and the particular product parameters are ...”
  22. 22. 22Dr. Juergen Rahmel Understanding Artificial Intelligence Integration Example: A complex ‘Chat Bot’ solution – a conversational Advisor Corporate Network Reasoning Recognition recognize intention clarify intention Communication Communication identify offering customize offering Planning Prediction Reasoning Decision Making Customer Data Product Data Customer: “I am thinking about increasing my family protection” Customer: “I want to buy an education insurance” Chat Bot: “We propose the following options in your situation…” Chat Bot: “…and the particular product parameters are ...”
  23. 23. 23Dr. Juergen Rahmel Understanding Artificial Intelligence Integration Example: A complex ‘Chat Bot’ solution – a conversational Advisor Corporate Network Reasoning Recognition recognize intention clarify intention Communication Communication identify offering customize offering Planning Prediction Reasoning Decision Making Customer Data Product Data Customer: “I am thinking about increasing my family protection” Customer: “I want to buy an education insurance” Chat Bot: “We propose the following options in your situation…” Chat Bot: “…and the particular product parameters are ...”
  24. 24. Alberto Chierici @Spixii: Chatbots and customer service
  25. 25. Making insurance more simple, accessible and personal than ever before
  26. 26. ? How do you engage with digital consumers without asking them to download an app?
  27. 27. Engaging Frictionless Efficient Personal Approachable Goals Customer
  28. 28. ? The preferred automated experience for insurers Chatbot Behavioural economics Non-invasive software Advanced analytic tool Customer Experiences designer
  29. 29. David Robson @IBM Watson: Enterprise view of AI
  30. 30. Artificial Intelligence in the Insurance Enterprise David Robson - IBM Watson Group A one minute introduction to Watson: https://www.youtube.com/watch?v=6SNs9kvRWSA Modern AIs can …. Read Natural Language • News, policies, fact sheets, web sites etc • Listen and speak Understand • understand what it has read or heard and retain this knowledge at huge scale Apply Knowledge • In conversation with people • Making decisions (medicine, underwriting etc) Learn with Experience • Train with experts and during operation • Improves with experience and feedback Machine Learning Deep Learning Natural Language Processing
  31. 31. Common use cases for AI in Insurance Client Engagement Underwriting Claims management Client Insight Image recognitionDiscovery
  32. 32. Visual Recognition Analyzes the visual appearance of images or video frames to understand what is happening Language Translator Translate text from one language to another Personality Insights Understand and engage users on their own term based on their personalities and values Conversation Hold natural language conversations with both your external and internal customers Speech to Text Provides highly accurate, low latency speech recognition capabilities Text to Speech Synthesizes natural-sounding speech from text Message Resonance Communicate with people with a style and words that suits them Discovery Add a cognitive to applications to identify patterns, trends and actionable insights Relationship Extraction Intelligently finds relationships between sentences components (nouns, verbs, subjects, objects) Tradeoff Analytics Helps make better choices under conflicting goals with smart visualizations & recommendations Document Conversion Converts a single HTML, PDF, or Mic. Word™ document into a normalized HTML, plain text A cognitive platform Tone Analyser Leverage cognitive analysis to identify a variety of tones at sentence or document level Alchemy Data News Provides access to an AI enriched, curated dataset of news and blog content DATA Face Detection/Recognition Returns the position, age, gender, and, in the case of celebrities, the identities of the people in the photo Alchemy API Enable businesses to build apps that understand the content and context of text online
  33. 33. 2 36 Questions & Answers How to participate: If you have a question please type into the messaging area and send to the presenters Session format: The moderator will use a combination of his own questions and those from the audience
  34. 34. Snap Poll3 37 Q. Which of the following use cases for AI / Machine Learning do you find most compelling 1. Educating consumers about insurance 2. Selling insurance 3. AI as an engagement tool to retain and service customers 4. Managing the claims process and identifying fraud 5. Risk Management & Prevention Advisory services 6. Other How to participate: Just respond to the question when it appears on your screen
  35. 35. Announcements Preregistration Open London 20TH Sept / Singapore 2nd Nov Following the success of our first Asia conference last year, we will be holding our second Asia annual conference in Singapore on 2nd November 2017 Our first annual European conference will be held in London on 20th September 2017 Pre-registration for both events is available NOW: http://asia2017.the-digital-insurer.com/ http://europe2017.the-digital-insurer.com/ Apply For An Award Applications are now open for Europe and Asia awards Award categories include the Start-up Insurtech Award and Insurance Innovation Award Award finalists will present their innovations and solutions at the conferences. The winners will be determined via a live vote on the conference app from all the attendees Nominate yourself today via the event website Entries close on the 5th May for Europe and 26th May for Asia
  36. 36. Post webinar activities Recording will be emailed to registered participants Next Webinar will be on 17th May 2017 – Digital Transformation Strategies Register on our website: https://www.the-digital-insurer.com/event/digital- insurer-webinar-incumbents-fight-back-digital-transformation-strategies/ Please give us your feedback If you would like to follow up with any of the panelists - Simon Phipps: simon.phipps@kpmg.com - Andrew Dart: andrew.dart@the-digital-insurer.com - Gary Richardson: gary.richardson@kpmg.co.uk - Adrien Cohen: adrien@tractable.io - Juergen Rahmel: jr@ietc.hk - Alberto Chierici: alberto.chierici@spixii.ai - David Robson: david_robson@uk.ibm.com

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