cpcu society big data & analytics webinar april 7, 2015
TRANSCRIPT
Presented by the CPCU Society
Webinar: April 7, 2015 Big Data Analytics
Presenter—
Pat Saporito, CPCU
Senior Director, Global Center of Excellence for Analytics
SAP Labs
Webinar Speakers
During this webinar, we intend to comply in all respects with the federal, state, and international antitrust laws. These laws forbid agreements among competitors in the marketplace which restrict a company’s freedom to make independent decisions in matters affecting competition.
Participants will not discuss, nor field questions about, any matters relating to individual company rates, underwriting, coverages, or marketing. We will not discuss:
• Present or future prices of products or services
• Present or future sales terms and conditions
• Treatment of any customer
• Current or future business strategies or marketing plans, or
• Refusing to deal with any customer, competitor, or supplier
Antitrust Statement
Big Data and the Internet of Things are causing disruptions in all industries.
Instead of being disrupted by it, insurers can apply data and analytics for
innovation.
This session will review trends, opportunities, and challenges of using big
data in underwriting, claims, and risk management and how CPCUs can
improve their analytic proficiency for personal and professional
development.
At the conclusion of this webinar, the active participant will understand:
• Big data and Internet of Things data sources that affect insurance
• Insurance analytic use cases in sales/marketing, claims, and underwriting
• Analytic skills needed by CPCUs and how to develop them
• How to apply analytics in your job and to advance your career
Session Description
Agenda
• Big Data, Internet of Things, and disruption
• Potential business value and challenges
• Emerging roles and stakeholders
• Culture and change management
• Insurance professional’s role
Big data is a term that describes large volumes of high
velocity, complex, and variable data that requires advanced
techniques and technologies to enable the capture, storage,
management, and analysis of the information.
TechAmerica Foundation: Commission on Big Data
What Is Big Data?
Big Data provides opportunities, but you need the ability to:
Visualize Predict Analyze Report Capture Engage
Potential to provide transformational business value
Big Data Matters—The Five Vs
Drive better profit margins
New strategies and
business models Operational efficiencies
Value
Velocity
Volume Variety
Mobile
CRM data
Planning
Opportunities Transactions
Customer
Sales order
Things
Instant messages
Demand
Inventory
Veracity
Internet of Things:
Connecting Devices, Data, People, and Processes Definition: the network of physical objects that contain embedded technology to
communicate and sense or interact with their internal states or the external
environment (Source: Gartner Group)
Connected retail
Connected logistics
Responsive supply chain Connected building/cities
Connected car Integrated Data Platform
Big Data and the Internet of Things:
Great Promise, Rarely Delivered
Data (Big and Small)—Its Uses
• 1:1 Marketing
– Amazon.com
• Gamification/game design
– Angry Birds, CastleVille
• Group/collective buying
– Groupon, Living Social
• Social networking
– Facebook, LinkedIn
• Inbound marketing
• Retargeting, remarketing
– Travelocity
• Location-based marketing
– Google Places
• Video
– YouTube, Hulu
• User-generated content
– Facebook, LinkedIn, Twitter
• Mobile technologies
– Smartphones, iPads, tablets
Data Enhancement:
Select Third-Party Data Categories and Sources
Categories Sources: • Acxiom • AM Best • AMA • American Housing Survey • American Tort Reform Foundation • Bureau of Labor Statistics • Cap Index • Carfax • Census Point • Choicepoint • Corporate Research Board • Directory of US Hospitals • Dun & Bradstreet • EASI Analytics • Equifax • ESRI • Experian • Insurance Institute for Highway Safety • Internal Revenue Service • State licensing data (Attys, CPAs, MDs,
etc.) • Martindale/Hubble Attorney Listing • MRI Purchasing Propensities • NFIRS—National Fire Reporting • NHTSA • OSHA • US Census • US Geological Surveys • Warranties
Analytics Evolution
Organizations need to mature their analytics to attain business value
Raw data
Cleaned data
Standard reports
Ad hoc reports &
OLAP
Agile visualization
Predictive modeling
Optimization
What happened?
Why did it happen?
What will happen?
What is the best that
could happen? Use
r En
gage
men
t
Maturity of Analytics Capabilities
Self-service BI
Generic predictive analysis
Co
llect
ive
Insi
ght
Turning New Signals Into Business Value
:-)
Brand sentiment
360O Customer view
Product recommendation
Propensity to churn
Real-time supply & demand forecast (quotes & capital)
Predictive maintenance
Fraud detection
Network optimization
Insider threats
Real-time risk mitigation
Asset tracking/ behavioral analysis
Personalized care
Impact of Internet of Things on Insurance
Smart healthcare Wellness & disease management
Smart equipment Preventive maintenance
Smart trucks Fleet management
Smart houses & buildings Home & property insurance
Smart vending Design your own insurance
Connected cars Usage-based insurance
Detect and analyze
data trends by
aggregating sensor
data
Benefit from more
real-time risk data,
enabling tailored
products, sales,
underwriting, and
pricing
Increase quality of life
through intelligent
vehicles, buildings,
healthcare
Big Data Challenges
Staffing
and
skills
Data quality/governance
Cost
Uncertainty
about value
of big data
Tools &
technologies
Connecting people to
information, applying
analytics
Optimizing Value With Integrated Analytics
Integrate and apply across all business processes
Business rules, data, and
KPIs should be leveraged
across business
Example: Business rules
used in 1st party claim
fraud detection can also
be applied up front during
underwriting process
Analytic use will skyrocket: 2020 vs. 2014
Do you have the Analytic Skills needed
today – and in the future?
Nucleus Research, Gartner, Fortune Magazine
10%
75%
Use analytics today
Need analytics by 2020
Managing and consuming all data is getting harder
Not utilizing all the information
out there
Bottom Line: not leveraging the power of “collective insight”
Missing new insights
IT is not agile enough, and the business wants to get involved
=
Analytic Value Chain:
Many Different Types of Users & Slills
New Titles and Roles
Data diva
Data savant
Data superhero
Chief analytics officer
Analytics cave man
Not everyone is a data scientist, but most people
need analytics in their jobs.
Data Scientist: Underwriter 3.0?
“…has a ‘chief science officer,’ a position he added in 2012 to the property-casualty unit as part of his effort to focus on science-driven decisions about strategy. The science team numbers about 130, many of them PhDs.” “…in recent analysis by AIG, in conjunction with Johns Hopkins University, of about 23 million of its workers' compensation claims.” Source: AIG’s New CEO Looks to Data to Chart Insurer’s Course, WSJ, Aug. 30, 2014
http://on.wsj.com/1A2jCCL
Where are you today? Where do you want to be?
Pricing & Underwriting
Traditional class rated
Portfolio analysis Household analysis, tier-rating plans
Risk-based pricing, ad hoc or on-demand rate reviews
Data Poor quality, siloed, inaccessible data
Data assembled across product lines/historical
Consistent enterprise view; knowledge/data mining
Atomic-detail data wisdom/predictive
Product Development
One product fits all
Unbundled coverages
Cafeteria/menu approach
Customer & profitability driven
Marketing
Product value Customer
segment value
Customer lifetime value
Dynamic value management
Accounting & Finance
Unit-focused claims mgmt.
Integrated, but reactive claims mgmt.
Driver-based historical claims mgmt.
Driver-based predictive claims mgmt.
Metrics Siloed, functional, lagging metrics
SBU-strategic objective linked, historical drivers
Strategic & cross-SBU objective linked, predictive drivers
Integrated predictive models & metrics
Claims
Traditional planning & budgeting
Driver-based planning & budgeting
Integrated planning Predictive planning
Less Advanced More Advanced
Insurance Analytics Evolution
Information Culture—Connecting People to Data
Use information as a strategic asset in decisions Build and tell fact-based stories Maximize business performance with effective use of information (apply the analytics)
“
”
The stone age was marked by man's clever use of crude tools; the information age, to date, has been marked by man's crude use of clever tools. Anonymous
Shifting to an Enterprise Analytics Mindset
Be ready for continuous disruption.
Create an information-driven culture.
Intelligence and analytics are universal,
big data isn't.
We all emit data—lots of it!
Data needs to be front and
center, no matter how big or small.
Analytics isn’t just for power users—it’s for everyone.
Invest in your future by improving your analytic IQ.
• Volunteer for analytics projects
• Expand your peer network, especially with:
– Chief analytics officer— let them know data & analytics you value, you need
– BI Competency Center – extended roles for business analysts, data stewards
• Enlarge your stakeholders
• Role: business analyst, data steward, data scientists, data managers, developers
• Function: actuarial, marketing, underwriting, claims, loss control, legal, IT
• Expand your skills
• Learn about big data and internet of things
• Try new tools—especially new visualization and text-mining tools
• Lead by example
• Use infographics in producing/presenting analytics
Next Steps
Practical Guidance
Free download of Chapter 1 (Overview)
Thank You!
Pat Saporito, CPCU Sr. Director, BI Global COE for Analytics [email protected] (201) 681-9671 Twitter: @Pat.Saporito LinkedIn: www.linkedin/in/patriciasaporito
Blogs/Tweets: Analytics from SAP http://blogs.sap.com/analytics/ @sapforinsurance
Analytics Bibliography—Books
Analytics at Work: Smarter Decisions, Better Results. Thomas H. Davenport, Jeanne G. Harris, Robert Morison. Harvard Business School Publishing. 2010.
Applied Insurance Analytics: A Framework for Driving More Value from Data Assets, Technologies and Tools. Patricia Saporito. Pearson FT Press, 2014.
Big Data: A Revolution That Will Transform How We Live, Work and Think. Viktor Mayer-Schönberger and Kenneth Cukier. Houghton Mifflin Harcourt, 2013.
Big Data@Work. Dispelling the Myths, Uncovering the Opportunities. Tom Davenport. Harvard Business School Publishing, 2014.
Business Intelligence in Plain Language: A Practical Guide to Data Mining and Business Analytics. Jeremy Kolb. Applied Data Labs, Inc. 2012.
Business Intelligence Competency Centers: A Team Approach to Maximizing Competitive Advantage. Gloria J. Miller, Stephanie V. Gerlach and Dagmar Brautigam. John A. Wiley & Sons. 2006
Mining the Talk: Unlocking the Business Value in Unstructured Information. Scott Spangler and Jeffrey Kreulen. IBM Press/Pearson, plc. 2008.
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die. Eric Siegel. John Wiley & Sons. 2013.
The Visual Display of Quantitative Information. Edward Tufte. 2001. (A classic reference work; the original “bible” of visualization. Also see: Envisioning Information and Visual Explanations, by Tufte.
Analytics Bibliography—Trade and Professional
Associations (non – insurance)
International Institute for Analytics (IIA). www.iianalytics.com
An independent research firm cofounded by Jack Phillips and Research Director Thomas H. Davenport. Works with organizations to build strong and competitive analytics programs.
INFORMS (Institute for Operations Research & Management Sciences) www.informs.org
Professional organization for cross-industry operations research and management professionals. Sponsors the CAP (Certified Analytic Professional) professional designation.
TDWI (The Data Warehouse Institute) www.tdwi.org
A leading educational and research organization for BI and Data Warehousing. TDWI produces an annual BI Benchmark Report.
IDMA (Insurance Data Manamenet Assn) www.idma.org
ACORD www.acord.org
Analytics Bibliography—Trade and Professional
Associations (Insurance)
ACORD www.acord.org
IASA (Insurance Accounting & Systems Assn.) www.iasa.org
IDMA (Insurance Data Management Assn) www.idma.org
Analytics Bibliography—Articles, Studies, and
White Papers
“Benchmarking Analytic Talent.” Talent Analytics Corp. December 2012. A research study on analytics professionals.
“Big Data: The Next Frontier for Innovation, Competition, and Productivity.” May 2011. McKinsey Research Institute. One of the key studies on big data.
“Business Intelligence and Performance Management; Key Initiative Overview.” Gartner Group. 2013. (Research Brief)
“Data and Analytics in Insurance: P&C Insurer Strategic Priorities and Operational Plans for 2014 and Beyond.” Mark Breading and Denise Garth. June 2014. Strategy Meets Action.
“The Data-Driven Organization.” Marcia W. Blenko, Michael C. Mankins, Paul Rogers. Harvard Business Review. June 2010.
“Disruptive Technologies: Advances that Will Transform Life, Business, and the Global Economy.” May 2013. McKinsey Research Institute. Insights into Machine to Machine (M2M), Internet of Things (IoT), and other technologies.
“The Way Forward. Insurance in an Age of Customer Intimacy and Internet of Things.” Economist Intelligence Unit; sponsored by SAP. June 2014. Global survey of P&C and Life insurance executives on the future of insurance. Key findings include important role of data and analytics.
Emerging Exposure: Cyber Risk
Education Program
Marty Frappolli, CPCU, FIDM, AIC
(484) 831-9009
Managing Cyber Risk
http://www.theinstitutes.org/program/cyber
-risk-management