The digital connected world is having an impact on the technology environments that insurers must create to thrive in the new era of computing. The nature of customer interactions, business processes from product, risk and claims management are continuously changing. During this session we will review recent research and insights from insurance companies in the life, general and reinsurance markets and discuss the implications for insurers as the industry considers implications from core systems, predictive and preventive analytics and improvements to customer experiences. Millions of dollars are being spent annually by the insurance industry in InsurTech investments from risk listening, customer interactions (chatbots, SMS messaging, smart interactive conversations), to methods of evaluating claims (digital capture at notice of incident, dashcams, connected homes/vehicles). These are all new types of data which the industry hasn't previously had to manage and govern. Additionally, at the heart of this is how to create new business opportunities from data. We will also have an interactive conversation on discussing and exploring insurance implications of the new computing environment from AI, Big Data and IoT (Edge computing).

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Digital Shift in Insurance: How
is the Industry Responding with
the Influx of Big Data, Analytics
and AI?
Monique Hesseling| mhesseling@cloudera.com
Managing Director Insurance

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AGENDA
• Digital Era and Impact to Insurance
• Why Big Data, Analytics & AI in Insurance
• Example Use Cases
• Monitoring AI Trends in Insurance
• Q&A

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Digital Claims Handling estimated at ~20% increase in
customer satisfaction and 25-30% reduction in claims loss
adjustment expense. Source: McKinsey
Ecosystems will account for 30% of global revenues by
2025, Insurance is moving beyond Digital Transformation
with new revenue models from data!!
Source: McKinsey
Digital Transformation impact to the Insurance Industry
Digital Claim Handling ~20% increase in customer
satisfaction and 25-30% reduction in claims loss
adjustment expense.
Ecosystems will account for 30% of global revenues by
2025, Insurance is moving beyond Digital Transformation
with new revenue models from data!!
DIGITAL TRANSFORMATION -- IMPACT TO THE INSURANCE INDUSTRY
Source: McKinsey
Source: McKinsey

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By 2030, the proportion of autonomous
vehicles on the road could exceed 25
percent, having grown from 10 percent
just four years earlier (1)
Experts estimate there will be up to one
trillion connected devices by 2025 (1)
The entities underwriting risk, and the
liability in an automated, smart world, may
change significantly
Smart city projects and technologies
introduce new risks like cyber-risk and
increase different liability exposures
Smart technologies can dramatically
reduce risks for vehicles /property and
improve people’s health and well-being
CONNECTED WORLD RESHAPES THE NATURE OF RISKS
Smart cities change the risks of both individual and business customers
Autonomous vehicles:
1,400 TB data /year—4,000 GB data
/Day 167 GB/hour! (2)
(!) World Economic Forum
(2) Networld World & Intel

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Data opens up
opportunities across
business units
Source: McKinsey, BCG and Bain industry reports
REDEFINING “AGE OF INSURANCE RISK & THE CUSTOMER”
The connected digital world
Customer centric/ need -based offerings
• Robo-advisors with machine learning
• Digital interactions vs. agent-based
• Wearable technology
• Gamification
• Lifestyle based coverage
Digital as preferred interaction channel
Health scores & vitality programs
promote healthy living
• Social listening
• Lifestyle-based needs
• Risk-based needs
Analytics to predict and manage
customer lifecycle and projections
Analytics encompassing
extraneous factors
Changes in regulatory
compliance and data privacy
Frequent updates to population
& environmental data
Change in claim frequency,
severity, fraud anomaly analytics
Advanced analytics to expedite,
improve accuracy

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INSURANCE INDUSTRY AI ADOPTION -- SHIFT IN AI METHODS
Classical Methods Deep Learning
Expert Systems
Programmed Chatbots
Rules Engines
Process Mining
Text Mining Robo Advisors
Robotic Process Automation
Artificial Neural Networks
Image & Vision
Natural Language Processing
Conversational AI
Speech Recognition

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INSURANCE INDUSTRY DATA SOURCES
Streaming capabilities enable safety alerts and real-time loss control in the digital world
Catastrophic
Event Data Customer
Onboarding
Data
Seismic
Data Biometrics
Data
Driver
Behavior
Data
Cyber Threat
Metadata
Drones & Aerial
Imagery
Claims Docs,
Notes &
Diaries
Weather &
Environment
News
Feeds
Video, Photos,
Audio
Photos
Telematics
Connected
Devices/IoT/
Sensors
Chat Bots
Call
Transcripts
RISK & UNDERWRITING
ANALYSIS
NEEDS-BASED
INSURANCE
CLAIMS ANALYTICS
NEW PRODUCT
DEVELOPMENT
CYBER RISK
ANALYTICS
CRM

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HELPING INSURERS RE-THINK DATA & ANALYTICS
Illustration
BatchReal-time
Datavelocity
Dimensional/Structured Behavioral/Unstructured
Data variety
Semi-structured
Climate / air quality / weather events
Drone image feeds
Social media / Sentiment
Sensors/ Wearables (IoT)
Geo-location
Deposition recording
Notes and diary
Medical bills
Transcriptions
Photos
Investigation
TPA invoices
FNOL intake
Claims triage
Vendor invoices
Forms and
letters
Claim payments
Policy verification
Applications / Submissions
3rd party risk models
Prior loss runs
Customer Profiles
1
3
1
1
1
1
3
1
1
1
Historical Weather-events1
Population data1
Call center Chat bots3
Currently using for analytics
Currently available, unable to use
for analytics
Plan to use in next 12 months
Plan to use in next 24 months
1
3

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Example Use Cases

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AREAS OF IMPACT FOR ADVANCED ANALYTICS & AI
• Underwriting cycle
management
• Profitability analysis
• Reinsurance optimization
• M&A post-transaction
analysis
• Target marketing
• New business acquisition
• Retention management
• Agency management
• Photo Recognition
• Digital Loss Assessment
• Automated Intelligent Case
Management
• Intelligent Loss Control
• Claims Frequency/Severity
/Reserve Projection &
Estimation
• Claims Leakage (Claims
Audit)
• Claims Fraud
• Risk Selection/avoidance
• Objectivity/consistency
• Risk scoring
• Risk monitoring
• Loss control
• Premium fraud
• Accuracy & adequacy
• Competitiveness
• Adverse selection
• Customer churn
• Efficiency
PRICING
CLAIMS
CORPORATE
SALES &
MARKETING
UNDERWRITING

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WHERE ARE THE BUSINESS OPPORTUNITIES?
EXAMPLE: Risk & Underwriting
• Risk Modeling
• Customer/Claim Segmentation
• Outstanding Claim Estimation
• Similarity Risk Analysis
• Predictive Modeling of Premium Leakage
• Optimize Physical Survey/Inspections
(leverage external data sources)
• Customer Risk Profile Mapping
• Usage-based Insurance / Telematics
• Application funnel analysis
• Quote to Bind Scoring Model
• Likelihood to Bind analytics
• Risk Similarity/Risk Profile
• Related Entity Analysis
• Multi-line “profitable” class code analysis
• Customer Segmentation
• Retention Modeling
Improve Underwriting
& Risk Selection
Reduce risk
Premium Leakage
Early Identification
of Potentially
Costly Claims
Premium Fraud
Detection
Identify Risk
Profile of
Customers
Increase
Customer
Satisfaction
Provide
Customized
Premiums
Reduce Underwriting Expenses
Increase Premium/Revenue

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Opportunity
Earlier and enhanced focus
on business you want to win
while optimizing expense
Underwriting Expense
(mix)
% Quoted & Issued % Quoted & Lost % Not Quoted
% Initial Declined
% Assessed &
Declined
ENHANCED BUSINESS OUTCOMES
Risk Assessment/Underwriting
• Determine the worthiness
of a submission
• Identify prior submissions
• Visualize prior
submissions
• Make pricing
recommendations
• Identify positive and
negative trends
• Learn and adapt
Optimize expense by shifting the mix of where expenses occur

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OPPORTUNITIES IN UNDERWRITING
Advanced Analytics, ML and AI
• How do we better
leverage the
unstructured data
we have?
• Is there value in
new data sources?
• How should real-
time data be
leveraged?
• How can we better
solve for missing
information?
New models / Real-time data
Risk similarity/
risk profiling
Economic data
Geospatial data
Related entity
analysis
Sensor data
Audio, Video
Fraud/social
network analysis
Real-time risk
appetite
Financial position
& “blemishes”
Newsfeeds
Data.Gov
New Application Renewal
Referrals
Quote to Bind
Fast Track
Process, manage to quote on or before SLA
Accelerated Path
Medium likelihood to win
Normal Path – Low likelihood to close
Quality Risks

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CLAIMS (LOSS) ANALYTICS SOLUTION TREE
Loss Payouts
• Customer/claim segmentation
• Outstanding claim estimation
• Operational risk modeling
• Loss prevention management (sensor-based services)
• Environmental/economic/geospatial analytics
• New predictive variables from text mining
• Claim scoring modeling (severity, key word search)
• Machine learning
• Unstructured data analysis
Large losses / claims severity
Claims frequency volatility
Reduce leakage / fraud
Improve reserving accuracy
Optimize subrogation / salvage
• Fraud – anomaly flagging / link analysis / social media
• New predictive variables from text mining
• Claim scoring / machine learning
• Claim audit analysis / machine learning
• $ Overwrites (compared to best practices)
• Customer/claim segmentation
• Outstanding claim estimation
• Operational risk modeling
• Text analytics
• Audit discovery
• Machine learning
Increase customer satisfaction
• Processing time of claims
• Valid & pay segmentation

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AI ENABLES TRULY INNOVATIVE CUSTOMER CLAIMS JOURNEY
Analysis of IoT sensor
data to identify high-
risk situation (e.g.
safest route or parking
prediction)
Claim event prediction
based upon IoT sensor
data
Biometric customer
authentication
Chatbot-based FNOL and
point of contact
Processing time
prediction
Automated damage-
value estimation
based on picture
recognition or sensor
data in combination
with invoice data
Claims assessor
route optimization
Real-time invoice
analysis for
correction potential
based on past
invoices and spare-
parts catalogs
Subrogation and
salvage opportunity
identification
Real-time prediction of
claims characteristics
Real-time segmentation
of claims by complexity
Supported claims-
handling for optimal
case management
AI AI AI

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2 - Satellite
• Situation pre-event
3 - GPS Location
• Drone path (green)
• Water Level Monitors (black)
• Water Level Crowd Reporting
(orange)
4 - Water Levels
• Showing surge
pattern
POST HURRICANE HARVEY CLAIM RESERVING EVENT ANALYSIS
6 - Aerial Pictures
• Detecting similarity of
events in unstructured
data analysis
5 - AI Clustering
• Drones, Helicopters,
Planes (source from
state & local gov’t)
1 - Satellite
• Situation post-event
1
2
3
4
5
6
Top 25 Global Comm’l & Personal Lines
Carrier partnering with
Data layers fused with AI and advanced visualization

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Hurricanes, exposure visualization and humanitarian
services-open source data

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ARCHITECTURE FOR REAL-TIME CONNECTED INSURERS
REAL-TIME & DIGITAL DATA SOURCES
ENTERPRISE
TRANSACTION
DATA
POLICY, BILLING, CLAIMS,
AGENCY, COMMISSION,
MEDICAL, BILL REVIEW ENTERPRISE DATA LAKE
STORE2
PROCESS3
7
REAL-TIME
ACTION
ACT
1
I
N
G
E
S
T
INGEST1 MONITOR6
LEARN4
• Machine Learning
• Train Scoring
Models
ANALYZE4
• Data Discovery
• Business
Intelligence
5
DEPLOY
Connected
Customers
Connected
Ecosystems
Connected
Channels
VIDEO, AUDIO
NEWS FEEDS
SOCIAL
MEDIA

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Monitoring AI Trends in
Insurance

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SOURCES TO FOLLOW INSURANCE DIGITAL INNOVATION

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INDUSTRY
OPPORTUNITY
High-performance advanced analytics,
or
a combination of structured,
unstructured data and real-time
information, is changing
the ways of the
insurance industry
after decades of conservatism.
Data opens up
opportunities across
business units and
ecosystems

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CALL TO ACTION:
AI SHOULD BE AN
ENABLER OF YOUR
BUSINESS STRATEGY
Data opens up
opportunities across
business units and
ecosystems
DATA
CAPABILITIES
ORGANIZATION
& TALENT
MODELS &
TOOLS
CHANGE
MANAGEMENT
CULTURE

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Q&A

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LET’S KEEP THE CONVERSATION GOING!
mhesseling@cloudera.com
@monhess
+1 856.470.8905
https://www.linkedin.com/in/monique-hesseling-6123002/