CAS Innovation CouncilInnovation in Predictive Modeling

Aaron HalpertSerhat GuvenKevin Bingham

October 2014

Agenda

Introductions

Defining Innovation

Overview of Predictive Analytics

Innovation Case Study

Summary, Questions and Discussion

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With you today

Aaron Halpert, ACAS MAAAPrincipal, 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.

3

With you today

Kevin Bingham, ACAS MAAAPrincipal, Deloitte Consulting LLP

Co-Chair CAS Innovation Council

Relevant Experience/SpecializationKevin Bingham is a principal in Deloitte Consulting's Advanced Analytics and Modeling practice. He has over twenty-one 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 CredentialsKevin 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.

4

With you today

Serhat Guven, FCAS MAAADirector, 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

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.

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Defining Innovation

Innovation Introduction

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

THINK INNOVATION: “The Eight Pillars of Innovation” – Susan Wojcicki

Overview of Predictive Modeling

What is Predictive Modeling

Impact of Action

Class Group A

Class Group B

Predictive modeling uses historic data to identify patterns which can be used to predict future behavior

Extrapolation is in two dimensions Over time Across risk factors

These predictions are then used to inform business decisions

Predictive modeling takes many forms Costs/billings Conversion/renewal rates Operations Wider customer behavior

10

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

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

2001

2007

2009

2010

2013

Cost Modeling

Competitive Market Analysis

Customer Behavior Models

Customer Lifetime Value

Price Optimization

BASIC

ADVANCED

EXPERT

Applications within Insurance

“Predictive modeling is being used to help integrate all aspects of companies’ operations and identify the true customer value”

“Predictive modeling is being used to help integrate all aspects of companies’ operations and identify the true customer value”

$

Finance

Reserving

Underwriting &Pricing

Customer services

Claims and claims analytics

Distribution

Marketing

Planning

Enterprise Risk Management

Modeling

team

Application Varies with Sophistication

Basic analytics projects Update existing rate plan Modify territory definitions

Advanced development projects Change the rate structure Retention Elasticity models Claims Analytics and Triaging

Expert development projects 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 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

• Modeling is not done in a vacuum – incorporating other disciplines in the process significantly improves performance Claims managers Underwriting specialists Marketing team Customers?

Pillar 5: Share Everything

• 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 ISOLATIONAfter a period of time, the analytics model will need to be re-parameterized to reflect new data and trendsIn addition Generation 1 solutions will need to be revisited for their effectivenessOften done in two parallel work-streams

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.

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

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

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!