CAS Innovation Council
Innovation in Predictive Modeling
Aaron Halpert
Serhat Guven
Kevin Bingham
October 2014
Agenda
Introductions
Defining Innovation
Overview of Predictive Analytics
Innovation Case Study
Summary, Questions and Discussion
1
With you today
Aaron Halpert, ACAS MAAA
Principal, AMH Advisory LLC
Co-Chair CAS Innovation Council
Relevant Experience/Specialization
Aaron is an independent consulting actuary after recently retiring from KPMG. He joined the firm in 1984, and was a
Principal in KPMG’s US Property/Casualty Actuarial Services practice. During his tenure with the firm, Aaron Chaired
KPMG’s Global Actuarial Group. He has over 35 years of professional experience in the insurance industry, eight with a
major insurance service organization, and 27 as a consulting actuary.
Aaron has had extensive experience in all segments of property casualty actuarial work including loss reserving,
ratemaking, design of statistical and management reports, financial modeling and risk management.
Education and Credentials
Aaron graduated from the Brooklyn College with a bachelors of science in mathematics. He became a member of the
American Academy of Actuaries and an Associate of the Casualty Actuarial Society in 1983.
Other CAS Volunteer Activities
Aaron Chairs the CAS Leadership Development Committee and serves on the CAS Nominating Committee and the
CAS Branding Task Force.
2
With you today
Kevin Bingham, ACAS MAAA
Principal, Deloitte Consulting LLP
Co-Chair CAS Innovation Council
Relevant Experience/Specialization
Kevin Bingham is a principal in Deloitte Consulting's Advanced Analytics and Modeling practice. He has over twentyone years of industry experience, including sixteen years in consulting. He currently leads Deloitte Consulting's claim
predictive modeling and medical professional liability practices.
Kevin's work involves consulting for the insurance industry, healthcare and life sciences industry, and public sector. The
complex problems appearing during the course of his work experience has provided Kevin with dynamic research
opportunities leading to the development of articles and presentations addressing important issues and trends. His
publications have been the subject of panel discussions and presentations at a number of industry events and client
presentations for employees and the board of directors. To date, he has published almost 70 articles and spoken at
over 100 seminars and training events.
Education and Credentials
Kevin graduated from Clarkson University with a bachelors of mathematics. He became a member of the American
Academy of Actuaries and an Associate of the Casualty Actuarial Society in 1998.
3
With you today
Serhat Guven, FCAS MAAA
Director, Towers Watson
Relevant Experience/Specialization
Serhat Guven is the Americas P&C Sales Practice Leader. He has over 19 years of experience in the insurance
industry. Prior to joining Towers Watson, Serhat was a senior consultant for EMB after spending nine years in a variety
of positions at United Services Automobile Association (USAA), where he was the technical expert on multivariate
pricing, demand modeling, classification and tiering analysis, territorial ratemaking and data management.
Serhat’s primary area of expertise is developing sophisticated predictive modeling solutions for a wide variety of
insurance applications. Particular examples of Serhat’s past predictive modeling experience include:

Price optimization development and implementation projects for large and midsize personal lines insurers
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Multivariate modeling of the insurance risk for small and large personal and commercial lines insurers

Training on predictive modeling techniques and applications
Serhat is very active on industry and professional committees. He currently serves on the Casualty Actuarial Society
(CAS) ratemaking and exam committees.
Serhat has authored several actuarial papers, most recently, “Beyond the Cost Model: Understanding Price Elasticity
and Its Applications” (CAS 2013 Winter Forum). He is a frequent speaker on predictive modeling topics at actuarial
seminars and meetings.
Education and Credentials
Serhat graduated from the University of Texas at Austin with a bachelors of science in mathematics. He became a
member of the American Academy of Actuaries in 2001 and a Fellow of the Casualty Actuarial Society in 2002.
4
Defining Innovation
Innovation Introduction
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The environment: Competition from within the profession and from
other professions.
The CAS recently formed the CAS Innovation Council to focus on
generating and developing innovative ideas, practices, products and
services that support the CAS strategic goals. By providing thought
leadership in innovation, the CAS can better leverage its resources,
influence the business community served by our members and
demonstrate leading practices by integrating innovation throughout the
CAS.
The Innovation Council’s PROFILE SERIES: The Series will showcase
actuarial innovators who have demonstrated how to integrate the
principles of innovation and their actuarial skills and experiences to
creatively address complex business issues.
In today’s inaugural series presentation, Serhat will show you how the
pillars on innovation help fuel the growing actuarial practice of
predictive modeling.
Common Pillars of Innovation
Have a mission that matters
Think big but start small
Strive for continual innovation not instant
perfection
Look for ideas everywhere
Share everything
Spark with imagination fuel with data
Be a platform
Never fail to fail
THINK INNOVATION: “The Eight Pillars of Innovation” – Susan Wojcicki
Overview of Predictive Modeling
What is Predictive Modeling
Private Car Comp Bodily Injury Ultimate Average Cost
9,500
9,000

Predictive modeling uses historic
data to identify patterns which
can be used to predict future
behavior
Extrapolation is in two
dimensions

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Over time
Across risk factors
These predictions are then used
to inform business decisions
Predictive modeling takes many
forms
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8,500
Average Cost

8,000
7,500
7,000
6,500
6,000
Selected
Scenario 1
Scenario 2
Class Group A
Costs/billings
Conversion/renewal rates
Operations
Wider customer behavior
Class
Group B
Impact of Action
9
Predictive Modeling Survey Findings

A majority of carriers view sophisticated pricing as essential or very
important

Personal lines carriers are far more likely than commercial lines to
prepare detailed qualitative or quantitative competitive market
analysis (CMA)

Carrier activity has been focused on pricing, competitive position,
data and implementation
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Carriers of all sizes and across all lines have plans to expand and
enhance their current predictive modeling applications
Industry Journey in Pricing Sophistication
US carriers cover a wide spectrum of sophisticated pricing techniques
2013
2010
2009
Customer
Lifetime
Value
Customer
Behavior
Models
2007
ADVANCED
Competitive
Market
Analysis
2001
BASIC
Cost
Modeling
Price
Optimization
EXPERT
Applications within Insurance
Claims and claims
analytics
Finance
$
Enterprise Risk
Management
Customer
services
Reserving
Modeling
team
Underwriting &
Pricing
Planning
Distribution
Marketing
“Predictive modeling is being used to help integrate all aspects of companies’
operations and identify the true customer value”
Application Varies with Sophistication
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Basic analytics projects
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Advanced development projects
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Update existing rate plan
Modify territory definitions
Change the rate structure
Retention Elasticity models
Claims Analytics and Triaging
Expert development projects
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Conversion Elasticity models
Customer Lifetime Value Models
Price Optimization
Innovation Case Study
Pillar 1: Have a Mission that Matters
• CAS Statement of Principles
 Principle 4: A rate is reasonable and not excessive,
inadequate or unfairly discriminatory if it is an actuarially
sound estimate of the expected value of all future costs…
• Company mission
 ‘The right rate for the risk’
• Public mission
 Identifying and mitigating risk
Pillar 2: Think BIG but start small
• Model complexity
 Adding factors on top of
existing structures
 Rebuilding entire structure
 Finding new factors
 Incorporating external
information
• Mitigating impact of results
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Risk selection and guidance
New business deployment only
Renewal caps
Optimization
Pillar 3: Strive for Continual Innovation NOT instant Perfection
Data assembly can take up to 70% of the project
• Identification of data problems can save significant time
Too much or too little time spent in data assembly
jeopardizes the success of the project
• Early modeling unearthed even more data problems than
before
• Early adopters and innovators recognized the lift in the
model far outweighed the inaccuracies of the data
• Output of the model was then to prioritize what should be
fixed now vs. what could be fixed in future iterations
Pillar 4: Look for Ideas Everywhere
• Our own literature
provides insight
 Principle of locality
 Multivariate Spatial
Analysis of the Territory
Rating Variable
• Perspectives from other
disciplines further
enhance results
 Claims managers
 Underwriting specialists
 etc
Distance
Adjacency
Pillar 5: Share Everything
• Modeling is not done in a vacuum – incorporating other
disciplines in the process significantly improves
performance
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Claims managers
Underwriting specialists
Marketing team
Customers?
• External insight broadens the
playing field
 CAS national and regional
activities
 Other?
Pillar 6: Spark with Imagination Fuel with Data
• Explosion of interest in what correlates with risk.
Predictive models quantify the effect
Towers Watsons’s 2013 Predictive Modeling Survey
Pillar 7: Be a Platform
SOLUTIONS ARE NOT DONE IN ISOLATION
 After a period of time, the analytics model will need to be re-parameterized to
reflect new data and trends
 In addition Generation 1 solutions will need to be revisited for their
effectiveness
 Often done in two parallel work-streams
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Re-parameterize the model
– Comparing the results to the prior models and document any additional lessons learned
– Integrate the new model into existing processes and identify an updated list of analytical
conclusions.
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Conduct reviews with other areas based on the updated models.
– Facilitate the discussion between the staff and the analytics team to enhance the policies
based on Generation 2 objectives.
– Reevaluate the practices for the Generation 2 operational models.
Pillar 8: Never Fail to Fail
Never be unwilling to experiment
• Predictive modeling is formalized experimentation
 Start with 1,000 variables and wind up with ~30 selections
 Look at over 499,500 two way interactions and wind up with ~10
selections
 Consider over 166,167,000 three way interactions that results in
~4 selections
 What can you learn from the variables and interactions that did
NOT work?
• Tools used in experimental analytics
 Statistical tools – all which can fail - (e.g. GLMs; CARTs;
Machine Algorithms)
 Non statistical tools (e.g. consistency over time and data;
business judgment; other)
Summary
Key take-aways
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Innovation comes in all shapes and sizes but has certain
key characteristics

Predictive modeling has revolutionized the insurance
industry over the last 15 years leading to less subsidization
in pricing

The innovative development of predictive modeling also
has the same central tenets
Q&A
Looking forward
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We hope you enjoyed this inaugural presentation of the
Innovation Council’s Profile Series.
Stay tuned for future presentations on how innovation
helps actuaries expand our footprint in other practice areas
such as catastrophe management and ERM.
Bring your ideas to the Innovation Council! Everyone has a
role to play!