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Cool Analytics for the Insurance Industry
Geetanjali Chakraborty
Advanced Analytics & Predictive Modeling Practice
Deloitte Consulting
IASA Conference November 21, 2014
Agenda
Analytics is all around us…
What is analytics?
How analytics is being used in insurance?
Lifestyle Based Analytics
So many have already done it…
The savings potential
Questions
Copyright © 2014 Deloitte Development LLC. All rights reserved.
Analytics is all around us…
Analytics is all around us…
Copyright © 2014 Deloitte Development LLC. All rights reserved.
Why is analytics such a hot topic?
“…Perhaps the most important cultural trend today: the explosion of data
about every aspect of our world and the rise of applied math gurus
who know how to use it.“
“…the world contains an unimaginably vast amount of
digital information which is getting ever vaster ever
more rapidly.”
The increase in digital footprint, the rise of cheap computing
power and digital storage, and the seamless integration of
networks are allowing the accumulation of huge amounts of data.
Data is information about the past. Analytics can make it about the
present and the future. Knowledge and insights about the future
can drive significant business value
Copyright © 2014 Deloitte Development LLC. All rights reserved.
Analytics in the insurance industry
Insurance is a data rich
industry and has long
mined its data to
improve pricing and
underwriting activities
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Predictive Analytics in Insurance
Pricing
• Tiering, schedule plan
• Class plan optimization and optimal scheduled credits/debits
Traditional
Applications
• Enhanced underwriting decision making
Underwriting
• Risk selection, retention strategies, automated underwriting
• Resource allocation, straight-through processing
Customer
Service
• Queue Prioritization
• Service Offerings
• Resource Allocation
• Targeted Lead Generation
Marketing and
Agency Management
Claims
Emerging
Trends
• Cross-Selling Potential
• Agency/Agent Management, Training, Servicing
• Automated Processing and Triage
• Fraud/Salvage/Subrogation Potential
Management
• Duration Improvement and Litigation management
Copyright © 2014 Deloitte Development LLC. All rights reserved.
What is analytics?
What do you think of when you hear “Analytics”?
Statistics
Numbers
Data and
Computers
Business
Intelligence
Analysis of
data
Analytics is the discovery and
communication of meaningful
patterns in data; relying on the
simultaneous application of
statistics, computer
programming and operations
research to quantify insights.
Analytics imply a wide range of possibilities in its definition, its business
application, and its delivery.
Copyright © 2014 Deloitte Development LLC. All rights reserved.
Basic Ingredients of Analytics
Data
Technologies
Synthetic
Data
Intelligence
Internal
External
Data
Data
Basic ingredients of analytics include Data that contains insights,
intelligence to extract those insights and act on them and Technologies to
implement appropriate actions.
Copyright © 2014 Deloitte Development LLC. All rights reserved.
Example of Data Sources – P&C
Policyholder Info
Customer Data
Coverage Information
Experience Data
Policy Records
Product Coverage
Policyholders
Correspondence
Options
3rd Party Databases
Business Credit
Personal Credit
Insured
CLUE / MVR / ISO CIB
Loss Control Data
Check Cashing
Weather
3rd Party
Billing Data
Sub-Prime Lending
Agency
Data
Credit Bureaus
Information
Real Estate
Billing / Payment Hist
Customer
Marketing
and Sales
Accepted Applications
Rejected Applications
Billing
Data
Geographic / Geocode
Demographic
Data
Psychographic
Claims Data
Policy
Claims Data
Coverage
Information
Information
Losses and Frequency
Bureau Data Sources
Consumer / Lifestyle
Timing / Patterns
Medical and Pharmacy
Jurisdiction
Behavioral
Weather
Claimant information
Injury/Diagnosis
Treatment patterns
Settlement data
Claims Notes
Medical Billing Data
Legal Bill Data
10
Heat / Cold Extremes
Precipitation Extremes
Hail
Wind / Storms
Event Extremes
Agency Information
Retention
Litigation
Marketing / Sales
Recruiting
Profitability
Campaign, Promotion
Adjusted Premium Ratio
Cust Response Scores
New Business Volume
Cust Segmentation
Continuing Education
Copyright © 2014 Deloitte Development LLC. All rights reserved.
Examples of Internal Predictive Variables
Insights can be revealed through both traditional and non-traditional risk
characteristics.
100%
Relative claim severity
80%
Claimant Age
60%
40%
20%
0%
-20%
-40%
-60%
-80%
< 25
25-30
30-35
35-40
40-45
45-50
50-55
55-60
60-65
20 to 25
25 to 30
65+
Relative claim severity
40%
30%
Distance: Claimant Home and Employer
20%
10%
0%
-10%
-20%
-30%
-40%
<1
1 to 3
3 to 5
5 to 7
7 to 10
10 to 15
15 to 20
30+
Copyright © 2014 Deloitte Development LLC. All rights reserved.
Examples of External Predictive Variables
Public domain data on the financial condition of the parties involved in the claim
can provide new insights into loss and expense severities.
Percentage of Sports Ultility Owners
External public database variables
provide new insights. Even
variables based on the claimant’s
address have proven predictive.
Loss and Expense Relativity
50%
40%
30%
20%
10%
0%
-10%
Low
High
-20%
-30%
Percentage of Population with High School or Less Education
Loss and Expense Relativity
30%
20%
The populations with lower
10%
education levels were over 20%
0%
Low
-10%
High
higher in terms of loss and
expense severity.
-20%
-30%
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Representation of a Claims Multivariate Model
Model Inputs
Several hundred internal and external
variables are tested to identify the 50 100 with greatest predictive power
Predictive modeling combines and converts available internal and
external claim characteristics into a score with corresponding
reason messages. In workers’ compensation, output may also be
“normalized” by injury group to better understand high severity
claims relative to those with similar diagnoses.
Claim Segmentation
Curve
Sample Model Equation
w1(Claimant Age) + w2(Dist_H_W)
w5(CoMorbidity) + w6(Report_lag) +….
Outcomes
+w3(Emerg_ Rm) + w4(Occupation) +
Model Outputs
92
John Smith
1 Circle Ave.
Anytown, NY
Reason Messages:
• Multiple co-morbidities
• Claim history
• Employment characteristics
• Distance from work
High
Low
Claim Complexity
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Social Media Data
Every claim investigation typically starts with a visit to social networking
websites such as Facebook and Twitter to assess the validity of the claim.
Copyright © 2014 Deloitte Development LLC. All rights reserved.
Analytics on the Cloud
Cloud computing is used by top
insurance carriers to manager their
claims better and faster.
Copyright © 2014 Deloitte Development LLC. All rights reserved.
Lifestyle Based Analytics
Lifestyle Based Analytics (LBA)
Traditional data is augmented with non-traditional data to create stronger correlations to the target
Traditional
internal
data
sources
Applicant
data
Non-traditional
external
individual or
household level
data sources
Consumer
data
EASI
census
Customized
analysis
Customer data
Comorbidity
data
Historical Claims
Marketing
Marketing and
and Sales
Sales
•Movement beyond traditional “likely to buy”
•Movement beyond traditional “likely to buy”
models
models
•Improvement in morbidity by selling only to
best
risks
•Improvement
in morbidity by selling only to
and data cleansing
Evaluate and
Develop
models
Score individual
Non-traditional data sources unlock
new insights into employee
populations
•More accurately price products in situations
•More you
accurately
products
in situations
where
have noprice
or limited
medical
where
you
have
no
or
limited
medical
experience
experience
Data aggregation
predictive
Household
data
Pricing
Pricing
Predictive Analytics
create variables
Benchmark
data
Business value
profiles
Efficiency
Innovative data sources
best risks
Customer
Customer Retention
Retention
•Identify compounding components of at risk
•Identify compounding components of at risk
customers
customers
•Develop, deploy data driven intervention
strategies
•Develop, deploy data driven intervention
strategies
Medical Management / Wellness
•Improved targeting of health events within a
population; based on predicting propensity of
having a certain clinical condition
•Deeper understanding of the current &
potential risks of the customers
•Understand the behaviors creating the risks
and monitor and develop behavior related
strategies to change customers risks
Lifestyle base analytics can be used to add efficiency across critical business areas
Copyright © 2014 Deloitte Development LLC. All rights reserved.
LBA provides Better Answers To Difficult Questions
Lifestyle Based Analytics (LBA) can be used to better understand the member and prospect populations
Retention:
• Which members of a relatively unknown
population should we target for retention?
Managing Health Risk:
• Which members will likely be afflicted with
a specific disease?
Acquisition:
• Which consumers are most likely to buy?
Efficiency:
• Who are the best candidates to target
with a specific product?
• Which members are most likely to
comply with health engagement
programs?
Future Health Risks:
• What are the future health risks for
members with unknown claims data?
• Which members have a higher
probability of having positive outcomes
from medical management programs?
• Which groups would it make sense to
offer wellness initiatives to?
Health Plans, using a new generation of lifestyle-based analytical models, may be able to predict
the likelihood of significant life events with more accuracy than ever before, and it starts with
something as simple as a name and an associated address
Copyright © 2014 Deloitte Development LLC. All rights reserved.
LBA and Improved Morbidity Risk Evaluation
Lifestyle-based analytics (“LBA”) focuses on identifying increased morbidity and mortality risks for “lifestyle” based diseases.
According to the US Surgeon General, lifestyle based diseases account for over 70% of US of healthcare expenses and
subsequent deaths.
Lorenz Curve for Neoplasm Female Sample
Examples of lifestyle-based diseases include:
diabetes, cardiovascular, cancer, and respiratory.
This chart demonstrates LBA’s ability to identify
future cancer claims in a healthy female
population.
The blue arrow points to LBA’s ability to predict future cancer claims in this
same population. In this case, 20% of LBA’s highest risk members accounted
for over 60% of the future cancer claims.
The red arrow points to traditional underwritings ability to predict cancer claims
in this healthy population. In this case, 20% of the highest risk members
accounted for 30% of the future cancer claims.
The black arrow points to a random distribution. In this case, 20% of the people
will have 20% of the future cancer claims.
Copyright © 2014 Deloitte Development LLC. All rights reserved.
Result for Claims Cost
Average Claims Relativity
80%
Actual Claims Relativity
60%
40%
20%
0%
1
2
3
4
5
6
7
8
9
10
-20%
Members with the
worst algorithm scores
experienced actual
claims 60% higher
than average
-40%
-60%
-80%
Predicted Claims Decile
Observations
 Algorithm was constructed using a 40/30/30 train/test/validate methodology
 Lift above demonstrated on blind validation after final algorithm was chosen
 Age/Gender correction made (neutral)
 Individual variables:
‒ Selected disease state algorithms (both binary and cost-weighted)
‒ Selected 3rd party ailment indicators
‒ Selected individual characteristics
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Data Visualization
Front End Tools
We have the ability to display modeling results in graphic, front-end tools that allow users to select different dimensions for
additional analyses. The exhibit below depicts member risk levels for Cardiovascular Disease for a sample of individuals in
the greater New Jersey area.
MEMBERS
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Front End Tools (continued)
The exhibit below shows the trend of policies and premiums across 10 buckets grouped by high to low loss ratio for Auto
insurance renewal business.
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So many have already done
it…
What Underwriters & Claims Executives are Saying
Science, enabled by technology, now plays an
integral role in our value proposition. Pricing risks
and establishing optimal claim outcomes for our
Insureds are being aided by sophisticated analytics
such as predictive modeling
Our claim scoring models review new claim
notices daily to identify red flags and
suspicious claims for investigation.
… better outcomes, through enhanced
automation from first notice of loss to
claims resolution
…first Workers’ Compensation
Company to apply advanced analytics to
claims
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Who’s Attending Predictive Modeling Seminars?
• Insurance Company of the West (ICW)
• Utica National
• Companion Property & Casualty
• WR Berkley
• ACE Ltd.
• Sentry
• Acuity
• NJM
• XL
• Auto Owners
• Westfield Group
• Main Street America
• Grange Mutual
• RLI
• Louisiana Workers Compensation Corp
• Unitrin
• Fireman’s Fund
• AFICA
• California State Fund
• Plymouth Rock
• Secura
• Beazley Group
• Allstate
• American Family
• State Farm
• Meadowbrook
• QBE
• Nationwide
• Farmers
• Church Mutual
Copyright © 2014 Deloitte Development LLC. All rights reserved.
The savings potential
Benefits Realized
Deloitte successfully designed and implemented Workers’ Compensation claim severity predictive model
into multiple clients’ claims operations to help injured workers return to work sooner.
Claim Routing & Assignment
Fraud Detection
 Right claim, right resource
 Reduce lag time of SIU referrals
 Improve routing to auto-adjudication
 Improve mix of claims referred to SIU
 Increase triage consistency through automation
 Deterrence of “soft-fraud”
Projected Business Impact
4-8% reduction in
loss and expense
5-10% improvement in
SIU managed claims
3-7% improvement in nurse
managed claims
20-25% redeployment
of supervisory resources
Medical Management
 Prompt assignment of nurses on those cases that
need it most
 Integrate behavior issues into nurse assignment
 Cost effective use of field case management
Top Line Growth
 Demonstrated ability to close claims faster and
cheaper leads to competitive market advantage
 Improved client satisfaction strengthens the
relationship and brand
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Benefits Case – Calculation & Allocation Tool
Once broad benefit target areas, amounts and associated metrics are defined, we use our Benefits Calculation Tool to provide a highly
tailored and approach/framework to refine, allocate and aggregate benefits.
Illustrative Benefits Calculations
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Questions?
Questions
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Biography
Geetanjali Chakraborty
Email : gechakraborty@deloitte.com
Tel (US) : +1 617 437 2393
Geet specializes in the development and application of predictive analytics and business
intelligence for the financial services and insurance industries. With a background in
mathematics, Geet has worked with many Fortune 500 companies to leverage data
analytics and technology to contain costs and gain operational efficiencies. She has
lead various analytics teams through the end-to-end process of model design, build, and
implementation, and co-develop Deloitte’s Advanced Analytics solutions for Healthcare
Insurance. She has publications in Claims P&C magazine, Claims 360 degree magazine
and through IIMA Analytics conference
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End of Presentation
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