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2017 Predictive Analytics Symposium
Session 24, General Insurance Applications of PA
Moderator: Stuart Klugman, FSA, CERA, Ph.D.
Presenter:
Peter Wu, ASA, FCAS, MAA
SOA Antitrust Compliance Guidelines SOA Presentation Disclaimer
https://www.soa.org/legal/antitrust-disclaimer/https://www.soa.org/legal/presentation-disclaimer/
General Insurance Applications of
Predictive Analytics: Past,
Current, and Future
Deloitte Consulting LLP
2017 SOA Predictive Analytics SymposiumChicago, September 2017Peter Wu, FCAS, ASA, MAAA, CSPAManaging Director
- 2 - Copyright 2017 Deloitte Development LLC. All rights reserved.
Theme
Data analytics in the U.S. Property and Casualty insurance industry is HOT!
Why?
Because P&C insurance is a zero sum game, and data and analytics will create adverse selection for the companies who are not doing it!
- 3 - Copyright 2017 Deloitte Development LLC. All rights reserved.
A Success Story - Credit Score Revolution
80%
85%
90%
95%
100%
105%
110%
115%
-5%
0%
5%
10%
15%
20%
25%
30%
35%
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Com
bine
d R
atio
Gro
wth
Rat
e
Year
Guess Which Company is It?
Industry Growth RateProgressive Growth RateIndustry Combined RatioProgressive Combined Ratio
- 4 - Copyright 2017 Deloitte Development LLC. All rights reserved.
The Evolution of P&C Insurance Data AnalyticsAdvanced analytics along with leveraging large amount of internal and external data have become mainstream over the last 20 years in several industries within financial services. Property and Casualty insurance has been one of the leading industries in the integration of advanced data analytics into core operations.
Credit Scoring An early bellwether of the disruptive power of data in insurance.
Predictive modeling transformation of the P&C industry and actuarial profession.
Analytics-powered key operations such as underwriting, claim triage, and marketing
From predictive modeling to broad based analytics and big data
Increasingly and granular applications on every aspect of insurance operations and customer service.
A core strategic capability Actuaries and data scientists
1990 2000s Today and Future
- 5 - Copyright 2017 Deloitte Development LLC. All rights reserved.
Credit Score Revolution
Introduced in late 1980 and early 1990s First important factor identified over the past 2
decades Strongly correlated with P&C insurance loss Composite multivariate score vs. raw credit
information Viewed at first as a secret weapon Quiet, confidential, controversial, black box, etc
1997 NAIC/Tillinghast Study of 9 Companies' DataLoss Ratio Relativity of the Best and Worst 20% of Credit Score
Co1 Co2 Co3 Co4 Co5 Co6 Co7 Co8 Co9 AvgBest 20% -38% -29% -19% -15% -14% -34% -22% -22% -36% -25%Worst 20% 48% 20% 32% 30% 46% 59% 20% 22% 95% 41%
Early believers and users have gained significant competitive advantage!
Credit Score for Auto Insurance Application
Sheet1
Exhit 1 Tillinghast - NAIC Credit Score Study [4]
Company 1Company 2Company 3
Scores & Loss Ratio Relativity SummaryScores & Loss Ratio Relativity SummaryScores & Loss Ratio Relativity Summary
ScoreMidpointEarnedLoss RatioScoreMidpointEarnedLoss RatioScoreMidpointEarnedLoss Ratio
IntervalPremiumRelativityIntervalPremiumRelativityIntervalPremiumRelativity
813 or more850.010.2%0.657-0.380840 or more854.010.0%0.607-0.290826 or more845.010.0%0.723-0.187
768-812790.09.9%0.584823-839831.010.0%0.813803-826814.510.0%0.903
732-767749.511.0%0.692806-822814.010.0%0.626782-803792.510.0%0.895
701-731716.010.9%0.683789-805797.010.0%1.342759-782770.510.0%0.795
675-700687.510.4%1.184771-788779.510.0%1.059737-759748.010.0%1.073
651-674662.59.8%0.793748-770759.010.0%1.019710-737723.510.0%0.941
626-650638.09.9%1.332721-747734.010.0%1.322680-710695.010.0%0.912
601-625613.010.0%1.280686-720703.010.0%0.810640-680660.010.0%1.115
560-600580.09.4%1.2140.483635-685660.010.0%0.9860.202583-640611.510.0%1.2210.321
559 or less525.08.6%1.752635 or less592.09.9%1.417583 or less535.010.0%1.421
Company 4Company 5Company 6
Scores & Loss Ratio Relativity SummaryScores & Loss Ratio Relativity SummaryScores & Loss Ratio Relativity Summary
ScoreMidpointEarnedLoss RatioScoreMidpointEarnedLoss RatioScoreMidpointEarnedLoss Ratio
IntervalPremiumRelativityIntervalPremiumRelativityIntervalPremiumRelativity
832 or more859.010.0%0.672-0.151845 or more857.010.0%0.800-0.141810 and up837.519.7%0.656-0.344
803-832817.510.0%1.027830-845837.510.0%0.919765-809777.020.1%0.795
767-803785.010.0%0.823814-830822.010.0%0.740715-764739.520.8%0.911
739-767753.010.0%1.036798-814806.010.0%0.733645-714679.520.2%1.066
720-739729.510.0%0.775779-798788.510.0%0.855Below 645600.019.2%1.5930.593
691-720705.510.0%1.000757-779768.010.0%0.889
668-691679.510.0%1.041730-757743.510.0%0.993
637-668652.510.0%1.023695-730712.510.0%1.143
602-637619.510.0%1.2510.301643-695669.010.0%1.3000.464
602 or less571.010.0%1.350643 or less600.010.0%1.628
Company 7Company 8Company 9
Scores & Loss Ratio Relativity SummaryScores & Loss Ratio Relativity SummaryScores & Loss Ratio Relativity Summary
ScoreMidpointEarnedLoss RatioScoreMidpointEarnedLoss RatioScoreMidpointEarnedLoss Ratio
IntervalPremiumRelativityIntervalPremiumRelativityIntervalPremiumRelativity
750 and up795.021.3%0.783-0.217755 or more775.08.9%0.767-0.218780 and up815.016.8%0.637-0.363
685-749717.025.8%0.900732-754743.09.3%0.798745-779762.013.7%0.715
630-684657.019.6%1.083714-731722.59.6%0.859710-744727.013.9%0.734
560-629594.519.3%1.150698-713705.59.9%0.969670-709689.515.0%0.807
Below 560520.013.9%1.2000.200682-697689.510.3%0.922635-669652.012.1%0.909
666-681673.59.7%0.978590-634612.011.2%1.241
647-665656.010.5%1.070530-589559.59.8%1.3570.945
625-646635.510.2%1.107Below 530495.07.5%2.533
592-624608.010.7%1.1220.223
591 or less562.010.8%1.324
Sheet2
1997 NAIC/Tillinghast Study of 9 Companies' Data
Loss Ratio Relativity of the Best and Worst 20% of Credit Score
Co1Co2Co3Co4Co5Co6Co7Co8Co9Avg
Best 20%-38%-29%-19%-15%-14%-34%-22%-22%-36%-25%
Worst 20%48%20%32%30%46%59%20%22%95%41%
Sheet3
- 6 - Copyright 2017 Deloitte Development LLC. All rights reserved.
Sample Equation: .4591 - 0.053 * (Account Years) + 0.037 * (Number of Late Payments) + 0.026 * (Law Suits) - 0.075 * (Credit Limits) + 0.025 * (Number of Collections) - 0.038 * .
500 900
Credit Score RevolutionTr
ansf
orm
to F
inal
Sc
ale
Pred
icted
Loss
Rati
o
135%125%
110%115%
100%90%80%
70%
140%
55%50%
57%61%
64%
80%
74%
86%
120%
90%
OverallLoss Ratioof 68%
Better than Average Accounts
Average Accounts
Below Average Accounts
Decile
1 8 9 105432 6 7
Pred
icted
Loss
Rati
o
135%125%
110%115%
100%90%80%
70%
140%
55%50%
57%61%
64%
80%
74%
86%
120%
90%
OverallLoss Ratioof 68%
Better than Average Accounts
Average Accounts
Below Average Accounts
Decile
1 8 9 105432 6 7
Payment pattern information, account history, bankruptcies/liens, collections, inquiries, bad debt/defaults
Formula scoring or rule-based scoring Industry scores vs. company proprietary scores
Credit Score- A composite score that usually contains 10 to 40 credit characteristics
- 7 - Copyright 2017 Deloitte Development LLC. All rights reserved.
Credit Score Revolution
Large scale multivariate scoring using external data sources, a classic example of advanced data analytics applications
A significant behavior economic characteristic translated into a powerful Auto insurance class plan factor
Brilliant marketing approach for credit score:Benefits/ROI are measurable and lift curve can be translated into bottom-line benefitBlind test and independent validation can be done to verify the benefit
Pred
icted
Los
s Rat
io
135%125%
110%115%
100%90%80%
70%
140%
55%50%
57%61%
64%
80%
74%
86%
120%
90%
OverallLoss Ratioof 68%
Better than Average Accounts
Average Accounts
Below Average Accounts
Decile
1 8 9 105432 6 7
Pred
icted
Los
s Rat
io
135%125%
110%115%
100%90%80%
70%
140%
55%50%
57%61%
64%
80%
74%
86%
120%
90%
OverallLoss Ratioof 68%
Better than Average Accounts
Average Accounts
Below Average Accounts
Decile
1 8 9 105432 6 7
Why is credit score so successful?
- 8 - Copyright 2017 Deloitte Development LLC. All rights reserved.
The Evolution of P&C Data AnalyticsAdvanced analytics along with leveraging large amount of internal and external data have become mainstream over the last 20 years in several industries within financial services. Property and Casualty insurance has been one of the leading industries in the integration of advanced data analytics into core operations.
Credit Scoring An early bellwether of the disruptive power of data in insurance.
Predictive modeling transformation of the P&C industry and actuarial profession.
Analytics-powered key operations such as underwriting, claim triage, and marketing
From predictive modeling to broad based analytics and big data
Increasingly and granular applications on every aspect of insurance operations and customer service.
A core strategic capability Actuaries and data scientists
1990 2000s Today and Future
- 9 - Copyright 2017 Deloitte Development LLC. All rights reserved.
3rd Party Data
Marketing and Sales
Claims Data
Weather
CustomerData
PolicyInformation
Coverage Information
AgencyInformation
BillingData
Claims DataLossesFrequencyTiming / PattersLoss Control DataFraud / Lawsuit
Agency InformationRetentionRecruitingProfitabilityAdjusted Premium RatioNew Business VolumeContinuing Education
3rd Party Data
Business CreditPersonal CreditCrime StatisticsTraffic Patterns / StatsCLUE / MVRCredit BureausReal EstateGeographic/Geo-codingDemographicPsychographicBureau Data SourcesConsumer / LifestyleEnhanced Census
Campaign, PromotionCust Response ScoresCust Segmentation
Data Analytics Transformed P&C Insurance Industry Insurance industry contains large amount of data and is ideal for DM and PM
applications. Information age provides a wealth of external and 3rd party data sources
Billing / Payment HistAccepted ApplicationsRejected Applications
Billing Data
Marketing Data
Coverage and Policy Data
Policy InformationApplication InformationProduct CoverageInsureds Information
- 10 - Copyright 2017 Deloitte Development LLC. All rights reserved.
Data Analytics Transformed P&C Insurance Industry
Adoption of a wide range of new and powerful modeling and data exploration techniques- GLM, Neural Networks, Decision Trees, Clustering Analysis, MARS, etc.
1
X1
X3
X2
Z1
Z2
Y
1a11
a12
a21
a31
a321
b1
b2
a22
a01
a02
b0
FREQ1_F_RPT 0.500
TerminalNode 2
Class = 1Class Cases %
0 2508 57.41 1859 42.6
N = 4367
LIAB_ONLY 0.500
TerminalNode 3
Class = 0Class Cases %
0 7591 96.51 279 3.5
N = 7870
NUM_VEH 4.500
Node 4NUM_VEH
N = 20844
Node 1NUM_VEHN = 57203
0 20 40 60 80 100
-2-1
01
2
x
yy = 0.29 + 0.02*x
0 20 40 60 80 100
-2-1
01
2
x
y
y = 0.29 + 0.02*x - 0.086*max(0,x-35
0 20 40 60 80 100
-2-1
01
2
x
y
y = 0.29 + 0.02*x - 0.086*max(0,x-35) + 0.084*max(0,x-65)
- 11 - Copyright 2017 Deloitte Development LLC. All rights reserved.
DATA SOURCES
`
Business Rules Engine
External Data
Internal Data
Applicants Data
Agency Data
MODELING PROCESS
SCORE FOR EACHPOLICY
DRIVE BUSINESSDECISIONS
Data Aggregation+
Data Cleaning
Evaluate and Create Variables
Model Development
You learn why
Scoring Engine
`
Data Analytics Transformed P&C Insurance Industry
..etc
- 12 - Copyright 2017 Deloitte Development LLC. All rights reserved.
85% of our new products are automated with predictive modeling, which enhances underwriting consistency and makes it easy for our commercial agents to do business with Safeco
SafecoFully launched Customized Pricing, our predictive pricing model that automatically provides the most appropriate price for a new small business submission
Achieved overall written premium growth of 4%.
Hartford
Commercial lines NPW grew 3% for Q2, driven by $83M in new commercial business, up 13%compared to Q2 2006.
The dramatic improvement is a direct result of our multidisciplinary WC improvement strategy and predictive modeling
Selective
The quality of our property, WC, auto, and specialty mix is continuing to improve as we use predictive analytics
Agents are giving us preferred shelf space vs. weaker competitors
Hanover
Increased ease of use through faster decisions, streamlined processing, and expanded account rounding
Reduced system quote to issue time through dynamic questions, improved agent interface, and automated UW.
Travelers
Data Analytics Transformed P&C Insurance Industry
Strong performance in our core Property & Casualty Operations via portfolio optimization, data-driven underwriting, cross-sell, claim excellence, and catastrophe exposure management
CNA
P&C Insurers Public Statements on the Benefits of Predictive Modeling
- 13 - Copyright 2017 Deloitte Development LLC. All rights reserved.
The Evolution of P&C Insurance Data AnalyticsAdvanced analytics along with leveraging large amount of internal and external data have become mainstream over the last 20 years in several industries within financial services. Property and Casualty insurance has been one of the leading industries in the integration of advanced data analytics into core operations.
Credit Scoring An early bellwether of the disruptive power of data in insurance.
Predictive modeling transformation of the P&C industry and actuarial profession.
Analytics-powered key operations such as underwriting, claim triage, and marketing
From predictive modeling to broad based analytics and big data
Increasingly and granular applications on every aspect of insurance operations and customer service.
A core strategic capability Actuaries and data scientists
1990 2000s Today and Future
- 14 - Copyright 2017 Deloitte Development LLC. All rights reserved.Copyright 2015 Deloitte Development LLC. Proprietary and Confidential. All rights reserved.
Today and Future: Many Disruption ForcesThe P&C industry continues to experience disruption due to various driving forces:
Technology
Big Data and Analytics
Sharing Economics
Exponential Disruption
AI and Robotic Automation
Process
Digital and Mobile Reality
- 15 - Copyright 2017 Deloitte Development LLC. All rights reserved.Copyright 2015 Deloitte Development LLC. Proprietary and Confidential. All rights reserved.
Perception: Analytics evolving LINEARLY. Reality: Occurring EXPONENTIALLY
Indu
stry
Impa
ct O
ppor
tuni
ties
N
ew In
nova
tions
1995 2010 2025
ActuarialModels
InsightEconomy
2020+
Digital Enterprise
Internet of Things
Analytics asa Disruptor
2014-2018+
Machine Learning / AI
Crowd-sourcingAnalyticsApplied
2013-2016
Big Data
AnalyticsAware
2009-2013
CloudComputing
Data ScientistsAnalyticsas R&D silo
1995 - 2009SocialMedia
Smart Phones
Today and Future: ..and Accelerating at an Exponential Rate
- 16 - Copyright 2017 Deloitte Development LLC. All rights reserved.
Today and Future Technology Reshaping Every Industry
Mobile App
Drivers DNA
Night Driving
Speeding
Fatigue
Traffic Condition
Telematics
- 17 - Copyright 2017 Deloitte Development LLC. All rights reserved.
Today and Future: More Access to Data for Insurance Companies
http://www.data.gov/
http://www.data.gov/
- 18 - Copyright 2017 Deloitte Development LLC. All rights reserved.
Today and Future Big Data, More Data, More Modeling
- 19 - Copyright 2017 Deloitte Development LLC. All rights reserved.
Today and Future: A Core Strategic Capability
Customer
Workforce Finance
Supply Chain
Sector-Specific
Pricing and UW
segmentation analytics
Safety and Loss
Control analytics
Performance metrics
identification, design and
benchmarking
Customer analytics
Property and
Casualty Analytics
Claim Modeling
FraudDetection
Salvage Rate Modeling
Loss Control Modeling
Warranty Modeling
Underwriting Modeling
Pricing Modeling Account
Modeling Premium Audit
and Leakage Modeling
Demand Modeling Cross Sale and
Upsale Modeling Marketing Mix
Modeling Customer
Segmentation and Retention Modeling
Lifestyle Base Analytics
Customer Lifetime Value Analytics
Workforce Analytics
Workers Safety analytics
Business Information Analytics
Competitive Analysis
Increasingly Data Analytics Applications on Every Aspect of Insurance Operations and Customer Service
- 20 - Copyright 2017 Deloitte Development LLC. All rights reserved.
Lessons Learned from the P&C Analytics Journey
What Analytics IS NOT
A Black Box approach by quants only
Replacement for people
A shining complex math equation
A single variable magic bullet
Actuarial and/or systems projects
One of the many projects
Model complexity drives results
Short live hype
POC
What Analytics IS
An approach with transparency and communications
Tools and capability for underwriters and actuaries
A wide range of internal and external data is gold
Relationship among many variables is power
Business and strategic initiatives
Senior management support and all hands on deck
Implementation drives results
Stay for a long time and will get even bigger and better
Mature and proven to be impactful
- 21 - Copyright 2017 Deloitte Development LLC. All rights reserved.
For Life Insurance Industry: Growing Applications for Data Analytics
Customer
Workforce Finance
Supply Chain
Sector-Specific
Pricing and UW
segmentation analytics
Safety and Loss
Control analytics
Performance metrics
identification, design and
benchmarking
Customer analytics
Life Insurance Analytics
Fraud Detection: Application fraud
for non-smoker discounts
Fabricated death claims discount
Fake applications for agent rewards
Application Triage Analytics: Using external data
to identify preferred customers for fast track underwriting
Significant time and cost saving and more accurately placing policyholders risk status
Group Life Mortality Assessment: Large scale modeling
on group life insurance mortality experience
Goal is to develop more accurate pricing tool on exposure rating in addition to experience rating
Identify additional pricing factors beyond traditional factorsInforce
Management Analytics: Retention models
to identify insureds with potential high risk of lapse
Identify cross sale, up-sale opportunity to increase business and retention of existing policyholders.
Growth in Life Insurance Applications for Data Analytics
- 22 - Copyright 2017 Deloitte Development LLC. All rights reserved.
Learning and innovation go hand in hand. The arrogance of success is to think that what you did yesterday will be sufficient for tomorrow-William Pollard
Fruits for Thoughts
- 23 - Copyright 2017 Deloitte Development LLC. All rights reserved.
Q&A
Cover pageWu