Session 32 - Big Data Big Changes How the World is Changing. Keith Walter

Transcription

1 Session 32 - Big Data Big Changes How the World is Changing Keith Walter

2 The hype Big Data - Volume, Velocity, and Variety Analytics is the Key to Profitable Growth Find Hidden Insight on Customers and Risks New Technology Social Media, Mobile, Cloud... creates confusion and opportunity 2

3 Opportunity? Connected Cars and Homes: Telematics is creating new opportunities in auto insurance and reflects a broader movement toward connected consumers and shifting customer expectations Wearables: ComputerWeekly.com Wearable technology to transform laggards in insurance industry. Karl Flinders Monday 11 May

4 Opportunity? Gamification in Insurance: Customer Engagement and Beyond Gamification could prove to be an effective capability for insurers, as exemplified by the Manulife/John Hancock vitality program Earn Points Increase your Status Get Rewarded Live Healthier * Sources: and Manulife turns to fitness, rewarding customers for healthy habits JACQUELINE NELSON The Globe and Mail Published Sunday, May , 6:56 PM EDT 4

5 Opportunity? Fast Company: 23andMe Expands into Canada October 2014 In an announcement, 23andMe said Canadians will be privy to 108 health-related reports, including genetic risk factors for various health conditions, drug response, trait reports, and inherited conditions. Some 20,000 Canadians have already taken advantage of its services, which cost $199 No genetic testing could mean premium hike HomeLife By Donald Horne 28 July 2014 * Sources: 5

6 Opportunity? Fitch: Cyber Insurance is Key Growth Opportunity for Global (Re)Insurance Increased Number of Attacks: Since 2010 there has been an increase of cyber attacks across the globe. Despite company efforts, these attacks continue. Companies agree that they need to change the way they defend against and recover from cyber attacks. Increased Sophistication of Attacks: Attacks are going beyond the easy smash & grab of credit cards and are increasing in persistence and sophistication such as trade front running, IP theft, M&A and other data. Rising Costs: It used to be that a cyber breach would cost only the company remediation but now the costs are increasing with the average cost $6.75 million*. Increased System and Device Connectivity: Companies IT infrastructure continues to extend beyond the walls of their data center. With Bring Your Own Device, business partnerships, mobile and cloud proliferation, data is increasingly exposed to higher risks. Underdeveloped Cybersecurity Workforce: With the increase in sophistication of attacks and expanded device connectivity, finding and retaining a capable cybersecurity person is difficult, thereby creating wage inflation. * Sources: Ponemon Institute: Second Annual Cost of Cyber Crime Study Benchmark Study of U.S. Companies," Ponemon Institute, August 2011; HP Research: Cybercrime Costs Rise Nearly 40 Percent, Attack Frequency Doubles," HP, October 2012; Threats Impacting the Nation, U.S. Government Accountability Office, April 2012; Fortune 500, 2012; Costs to Reach Improved and Ideal Cybersecurity Levels By Industry, Bloomberg, 2013; "Forecast: Information Security Worldwide, , 3Q12 Update, Gartner, 2012; Federal Information Technology Market, ," Deltek, August 2012; 6

7 Business Impact Tactical Strategic The Advanced Analytics Journey - Realizing value by integrating advanced analytics in business decisions Capability Map Integrated Advanced Analytics Integrated Intelligent Interactions Advanced Analysis Transactional Reporting Query, Ad Hoc Reporting Performance Management Data Information Knowledge Insight Foresight Information and Analytics Use Hindsight Insight Foresight 7

8 Case Study 1 Integrating advanced analytics in managing a commercial P&C insurance book of business Foresight With an advanced analytics predictive model, scored results provide an instant look at the quality of your business scored by your model. Scoring of the block of business can be done on a number of dimensions, bringing a range of internal and external data sources together for deeper insights. 8

9 External Data Sources - Companies who are succeeding in advanced analytics are continuing to explore new data Expanding Range of Data Suppliers Economic Real Estate Equities Commodities Interest Rates Foreign Exchange Inflation Economic/Bus. trends National indices Wage Data Wealth/Net Worth Unemployment Stats Aggregate CRA Data Representative data categories Financial Credit Score Gross/Total Debt Service Ratio Credit Ratings Health Diabetes Cancer Cardiovascular Disability Injury Depression/Mental Demographic Age Gender Ethnicity Income Immigration Data Medical & Drugs Disability Data Nursing Home Data Hospital Visit Statistics Prescription Drug Usage Other suppliers Geographic Crime Statistics Climate Data Geographic Mapping Population Concentration Behaviors/Lifestyle Physical Activity Level Hobbies Lifestyle Clusters Social Values Purchase Property Spend by Category Firmographics Industry Location Structure Growth and Investment Competition 9

10 Predictive Model Development New techniques are being applied by insurance companies A variety of Predictive Model Development techniques are available and which present different tradeoffs. In addition, multiple models can be used in complementary and additive ways. Common Techniques Generalized Linear Models Classification and Regression Trees Machine Learning 10

11 Predictive Models and External Data Sources in Real-Time Decision Making Companies driving greatest value are applying advanced analytics in Real-Time Decision Making User-input Enrichment Sales and Service Enhancement UW and Pricing Sophistication Improve the Customer s Experience by reducing the number of user-inputs Enhanced ability to service and sell to customers through real-time anticipation of customer need and to increase lifetime value Optimize underwriting and pricing by deriving insights from internal and external data and real-time decision support models 11

12 Case Study 2 Real-time analytics in banking Feedzai Partners 12

13 Case Study 2 Real-time analytics in banking 13

14 Case Study 2 Real-time analytics in banking 14

15 Case Study 2 Real-time analytics in banking 15

16 Case Study 2 Extended to insurance Rules Builder Modelling Manager Decision Engine Whitebox Score LEARN (A) PREDICT (B) ACT (C) EXPLAIN (D) Build from existing conversion or new business rules Develop learning models that adapt to changing buyer behaviours and learn from preferences Reduce intuitive decisions and develop standardized approaches that can be applied with increased granularity Communicate outcomes and develop new opportunities 16

17 Business Impact Tactical Strategic The Advanced Analytics Journey Real-time decisioning for innovation and business value Capability Map Insight Driven Organizations Real-Time Integrated Intelligent Interactions Advanced Analysis Transactional Reporting Query, Ad Hoc Reporting Performance Management Data Information Knowledge and Insight Foresight Information and Analytics Use Hindsight Insight Foresight 17