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TRANSCRIPT
Predictive Analytics in Insurance –
Getting it right when your customers
need you most
Rob McCullagh
Tony Boobier
Dr Claire Jordan
16 November 2016
Today’s Speakers
2
Tony Boobier
Published Author –
Analytics for Insurance
linkedin.com/in/tonyboobier
Dr Claire Jordan
Senior Analytics Consultant
linkedin.com/in/clairejordan
Rob Mc Cullagh
Strategic Account Director
linkedin.com/in/robmccullagh
Today’s Topics
3
The Analytical Insurer
Using Predictive Analytics to drive customer experience and increase profitability
Predictive Analytics in Insurance – injecting intelligence into the
claims process
4
• Independent consultant and industry
commentator
• Previously World Wide Executive at IBM
• 30 years insurance industry experience
• Author of ‘Analytics for Insurance’
About me:
5
The “analytical insurer” is an insurance company using analytics throughout its organization to
improve business performance.
Distribution
Economy
Incl Brexit
Competitive
Environment
Demand for Growth
Risk Management
Digital and More ‘Savvy’ Customers
Market Changes require Greater Insights
6
Mobile Social
Cloud
Analytics
The Mega Technology Trends in Insurance
7
FinTech
The ‘Connected’
Car /Home/Person
Innovating to
Zero Urbanization
Geo-socialisation
The New Mega Trends in Insurance The New Mega Trends in Insurance
8
The Era of Big Data
A Complex Network of Over
1 Trillion Devices
Generating 2.5 Billion
Gigabytes of Data Daily
Structured
Un-Structured %
%
The Era of Big Data
9
Resources
Strategy
Tactics
Best Practices
Supply Chain
Asset Management
Operational Management
Marketing
Sales
Financial Management
Risk & Compliance
Claims
Distribution Advantage
Differentiation
Profitable Growth
Analytics Create Actionable Insight Analytics Create Actionable Insight
10
Bu
sin
ess
Va
lue
11
Descriptive
Predictive
Prescriptive
Cognitive
Reporting
Last quarter’s results
Analysis
Product profitability
Discovery
# security
breaches this
month vs. last
Alerts
Unusual activity
Forecasting
Trending analysis
Simulation
Impact of rising
rates
Modeling
Predicting elasticity
of insurance rates
Optimization
Highest
returning
portfolio based
on risk appetite
Stochastic
Optimization
Managed
exposure
to Cat Risks
Understands
natural
language,
hypothesizes,
adapts &
learns
The Fourth Age of Analytics
By 2020…
‘50% of all businesses will be making
decisions using artificial intelligence and
prediction’ Gartner
12
13
An Enterprise View on Analytics is Essential
Risk Appetite and
Solvency Optimisation
Underwriting
And Pricing
Marketing
and Distribution
Servicing including
Claims Management
Asset and
Supply Chain Management
Converged Insights are Critical
Business data, weather data, location data, contextual data,
via analytics
will transform Enterprise-wide decision making
14
What will Your Future World Look Like ?
Redesigned business models
New skills, roles and professions
Different organisational Structures
New styles of leadership
15
16
Technology Talent
Management
Implementation Leadership
Four Key Building Blocks of Change
About me:
“passionate about data and the application of mathematical and statistical techniques to solve real business problems”
17
-2
0
2
4
6
8
8 8.4 8.8 9.2 9.6
Revenue Growth
%
Journey Satisfaction 2011
Revenue Growth % - Insurance Industry 2011 v 2010
Company
Name
K
N
C M
E B
A
Journey Satisfaction impacts Revenue Growth
Average of satisfaction with each company’s three key journeys
Source 2011 McKinsey Multi-Industry Survey company financial statements
18
Success Stories
• Improve key to key KPI
performance
• Reduce claims cost
• Increase customer attrition
• Increase customer
satisfaction
BES (Banco Espirito Santo)
reduced attrition by
15-20%
FBTO increases marketing
ROI by 29%
ING Reduced Call Centre
Costs by 20-40%
Infinity Insurance increased
subrogation recoveries by $12m
19
Analysis & Monitoring
Predictive Analytics
Prescriptive Analytics
Reporting
What happened?
Why did it happen?
What will happen?
What should I do
next?
What is Predictive Analytics? The right solution for the right business problem
20 ©Copyright Presidion Ltd. 2016
20
The Predictive Insurance Company
21
Behavioural/Descriptive
Information
•Claim History
•Call Centre Interactions
•Online Activity
•Product Information
•Demographics
Attitudinal Information
•Complaints
•Surveys
•Call Centre Notes
•Emails
Claims Processing
Supplier Analysis
Complaints
Customer Retention
X X √
Up-Sell/Cross Sell
Risk
Customer LTV
External data
•Weather data
•Social Media data
•Regulatory Information
Your customers
22
The Predictive Insurance Company
23
Behavioural/Descriptive
Information
•Claim History
•Call Centre Interactions
•Online Activity
•Product Information
•Demographics
Attitudinal Information
•Complaints
•Surveys
•Call Centre Notes
•Emails
Claims Processing
Supplier Analysis
Complaints
Customer Retention
X X √
Up-Sell/Cross Sell
Risk
Customer LTV
External data
•Weather data
•Social Media data
Improving the Claims Handling Process
First
notification
of loss
Request
additional
information
Fast Track
Escalation
Suspect
Service :
Fast Track
Go through regular
processing and assignment
Refer to SIU
PA
24
Predictive Analytics and the Claims Process
25
Example – Motor Claims Process
Meet Michael Jones:
26
Michael’s Experience
• Michael calls Insurance Company
• Sarah asks some questions
Autocorrect Garage
• Michael’s car is fixed
• Michael is a happy customer
27
Sarah’s Experience
• Sarah takes a call from Michael
• Sarah is prompted by the system to ask certain questions
Autocorrect Garage
• Sarah tells Michael where to bring his car to be fixed
• Sarah is confident she has dealt with Michael efficiently and correctly
28
78%
• Michael makes contact with his insurance company – he damaged the front bumper of his car when he veered off the road
• Sarah is the claims handler dealing with his call
Predictive Analytics at work
29
78%
• Michael makes contact with his insurance company – he damaged the front bumper of his car when he veered off the road
• Sarah is the claims handler dealing with his call
83%
• Prompted by the system Sarah asks Michael were there any witnesses to the accident.
• Michael says yes, the car behind him stopped to see if all was okay and gave Michael his name and number
Predictive Analytics at work
30
78%
• Michael makes contact with his insurance company – he damaged the front bumper of his car when he veered off the road
• Sarah is the claims handler dealing with his call
83%
• Prompted by the system Sarah asks Michael were there any witnesses to the accident.
• Michael says yes, the car behind him stopped to see if all was okay and gave Michael his name and number
91%
• Again, prompted by the system Sarah asks Michael was anyone injured in the accident.
• Michael replies no, there were no injuries.
Predictive Analytics at work
31
78%
• Michael makes contact with his insurance company – he damaged the front bumper of his car when he veered off the road
• Sarah is the claims handler dealing with his call
83%
• Prompted by the system Sarah asks Michael were there any witnesses to the accident.
• Michael says yes, the car behind him stopped to see if all was okay and gave Michael his name and number
91%
• Again, prompted by the system Sarah asks Michael was anyone injured in the accident.
• Michael replies no, there were no injuries
91%
• Sarah is directed by the system to fast track Michael’s claim and information relating to most appropriate garage is relayed to Michael
Predictive Analytics at work
32
Michael’s Options
Approved
Repairer
Average
Lead Time
Average
Repair
Time
Average
Satisfaction
Score
Distance
NPS
Autocorrect
Garage 3 days 4.5 days 89% 3 miles High
Repair
Centre 5 days 4 days 74% 2.5 miles Mid
BRC 7 days 5 days 71% 1.5 miles Low 33
Real Time Front End System
34
Without Predictive Analytics With Predictive Analytics
Call Received
Agent Inputs
Claims
Claim
Classification
Documents
Sent
Claims
Handler
Clarification
Call
Repair
Approval
Garage
Waiting List Resolution
Call Received
Agent Inputs
Claims
Claim
Classification
Repair
Approval Resolution
Request further
information
Time to resolution: 2 Weeks
Number of contacts: 2
Predicted claim duration
Recommended fast track
Supplier Analysis
Time to resolution: 3 Days
Number of contacts: 1
Benefits of Predictive Analytics
35
Unstructured Data? – Text Analytics
What do your customers
really think?
36
Journey-led transformations deliver impact across
multiple dimensions
37
Questions and Answers
38
Thank you for watching
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