AI in Life Insurance Case Studies

1. 1
AI IN LIFE INSURANCE
CASE STUDIES
Vishwa Kolla
Head, Advanced Analytics
John Hancock Insurance

2. 2
TOPICS
Background Landscape Case Studies

3. 3
BIG DATA or ANALYTICS or DATA SCIENCE or AI or AA – WHAT IS IT?
CAMPAIGN NUDGE OPS - INTEG
APPS APPLICATIONS BI
STRATEGY INSIGHTS RECOMMEND
Data Math
Code
AA = COMBINE DATA AND MATH USING CODE TO DRIVE BUSINESS VALUE

4. 4
2001 – 2013 CAGR Revenue
(Firm | Industry)
Source: 2001 – 2013 Revenue figures from Capital IQ
3%
3%
3%
1%
5%
7%
7%
8%
10%
12%
INTEGRATING AI EFFECTIVELY UNLOCKS VALUE

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AI IS VERY OLD
Source: https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
Most
Operate
here

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DATA IS UBIQUTOUS

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COMPUTATIONAL POWER IS INCREASING EXPONENTIALLY
Number of
Calculations
Per Second
Per $1000
Source: The zettabyte era: Trends and analysis, Cisco, updated June 7, 2017’ United Nations; MMC Ventures; Nvidia;
McKinsey Global Institute analysis

8. 8
ALGORITHMS ARE GETTING BETTER
Source: The zettabyte era: Trends and analysis, Cisco, updated June 7, 2017’ United Nations; MMC Ventures; Nvidia;
McKinsey Global Institute analysis

9. 9
AI ELEMENTS
AI
ELEMENTS
MACHINE AND
DEEP LEARNING
IOT, RPA,
AUTONOMOUS VECHILES
CHAT BOTS
COMPUTER VISIONNATURAL LANGUAGE

10. 10
MACHINE LEARNING IS WHERE IT ALL STARTS
Machine
Learning
Supervised
Learning
Classification
SVM
Discriminant
Analysis
Naïve Bayes
Nearest
Neighbor
Regression
Linear, GLM
Trees
(RF, GBM)
Ensemble
Neural
Networks
Un-
supervised
Learning
Clustering
K-Means /
K-Medioids
Hierarchical
Neural
Networks
Machine Learning (ML) gives
computers (machine)
ability to learn (learning)
without being explicitly programmed (learning)
Arthur Samuel, 1959

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DEEP LEARNING IS THE NEXT FRONTIER
© Andrew Ng
When you hear the term deep learning, just think of a
large deep neural net.
Deep refers to the number of layers typically and so this
kind of the popular term that’s been adopted in the
press.
I think of them as deep neural networks generally.
- Jeff Dean
Source: https://www.youtube.com/watch?v=QSaZGT4-6EY
MULTI-LAYER
PERCEPTRONS
CONVOLUTIONAL
NEURAL NETWORKS
RECURRENT
NEURAL NETWORKS

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AI SYSTEMS MAKE IT REAL
IOT, RPA, AUTONOMOUSML, DL
VISION & LANGUAGE CHAT BOTS
TARGETING UNDERWRITING CLAIM HANDLING
CYBER SECURITYFRAUD
CALL CENTER APP FORM
EDA / LOOKUPS CLAIMS
ADJUDICATION
APS MEETINGS
CONNECTED
HOME
CONNECTED
HEALTH
OCR
CONNECTED
CARS
MORTALITY
RISK
MORBIDITY
RISK
ANNOTATION
PAY AS
YOU GO
NOT EXHAUSTIVE

13. 13
TOPICS
Background Landscape Case Studies

14. Source: Suncorp Group, The Changing Face of the Insurance Customer, 2013
Can I try
before I
buy?
What are my
friends doing?
I want a seamless
experience across
devices
I want options How does this
help me?
I want to be
valued
I prefer
Value to
Brand
I want to buy in 2
days
SATISFY THE SEGMENT OF 1

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USE CASES IN LIFE INSURANCE
PROSPECTING NURTUREACQUISITION
MARKET
SEGMENTS
CUSTOMER
SEGMENTS
LIKELY TO [*]
MEDIA
MIX
CHANNEL
SURVEY
ANALYTICS
CROSS / UP-
SELL
OCR
MISREP
LIKELIHOOD
MORTALITY
APS
SUMMARY
FLUIDLESS
SMOKER
LIKELIHOOD
MORBIDITY
CHURN
NEXT BEST
OFFER
CLAIM
LIKELI-
HOOD
JOURNEY
CLAIM
SEVERITY
NEXT BEST
ACTION
FRAUD
>>
TEXT
ANALYTICS
OPTIMIZE
NEXT LIKELY
ACTION
WELLNESS
IOT
ANALYTICS
NPS
ANOMALY
>>

16. 16
TOPICS
Background Landscape Case Studies

17. TODAY’S PERCEPTIONS ABOUT LIFE INSURANCE

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“Life insurance ranks at the top of the list of things consumers
know they probably should buy, but get no personal enjoyment
from whatsoever. There's just no happy way to look at life
insurance. In the best-case scenario, life insurance is just another
bill to pay. And in the worst case, your family collects the
benefits, but unfortunately you're dead.”
Source: USA Today, Knowing when you need life insurance, September 19, 2013

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ACQUISITION ON DIGITAL CHANNEL IS CRITICAL

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LOOK ALIKE MODELS SEEMED LIKE A GOOD START
>
Source: Krux.com

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THEY WERE NOT ENOUGH

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Capitalism is under siege. Diminished trust in business is
causing political leaders to set policies that sap
economic growth … Business is caught in a vicious
circle … The purpose of the corporation must be
redefined around …
CREATING SHARED VALUE
Michael E. Porter and Mark Kramer, Jan-Feb 2011

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Link

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AI HELPS MAKE UW PROCESS MORE EFFICIENT
Decision
Acquired
Scheduled
UnderwriterApplicant Part I
Typically
3-6 weeks
Process
Bottle
neck
Traditional Process
End to End Turn Around Time = Hours to 3 - 6 weeks
Consent
Proxy for Cholesterol,
Glucose, Nicotine
Likely to misrepresent
Adjustment for Height
and Weight
Other issues found
after roll out
Encourages healthy
people to apply

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MAN – MACHINE COMBO SPEEDS UP SUSPICIOUS PATTERN DETECTION
not pay NOT PAYING
not payable NOT PAYING
not payble NOT PAYING
not paycheck NOT PAYING
not payment NOT PAYING
not payout NOT PAYING
not payroll NOT PAYING
payment not NOT PAYING
absence pay NOT PAYING
absent pay NOT PAYING
withhold payment NOT PAYING
pay unpaid NOT PAYING
unpaid pay NOT PAYING
Check for keyword
‘Not Paying’
Use deep learning
to find exact
words / patterns
Identify useful patterns
Identify claims associated with
the identified patterns
Business checks
flagged cases
Reports
cases to
BUIBusiness uses
expert knowledge
to provide directional
guidance
Machine
only
Man
+
Machine
Use deep learning
to find similar
words/patterns
Reports cases
To BUI
Use all
unstructured text
as input to DL
Select
A few
datasets

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ROBUST DATA ECOSYSTEMS ARE A FORTUTIOUS OUTCOME

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D E V E L O P
AI-enhanced roadmap
C O L L A B O R A T E
to create shared value
S H A P E
your value delivery
SUMMARY