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David Helmuth and Suresh Selvarangan from Deloitte Consulting LLP presented on "Big Data - An insurance business imperative" at the Insurance Data Management Association's (IDMA) annual conference on Apr. 8, 2014.
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Big Data An insurance business imperative
David Helmuth and Suresh Selvarangan Deloitte Consulting LLP Tuesday, April 8, 2014
Copyright © 2014 Deloitte Development LLC. All rights reserved. 2
Agenda
What is Big Data? 1
Where can Big Data bring value in Insurance? 2
3 The Journey to Big Data – steps to get there
What is Big Data?
Copyright © 2014 Deloitte Development LLC. All rights reserved. 4
Big Data is more than just growth in data volume. Big Data includes data that is unstructured, generated from non-traditional sources, and/or real-time – in addition to being large in volume.
Clarifying the definition
Type Size Examples
Admin Kilobytes Policy Administration, Claims Administration, Billing
CRM Megabytes Segmentation, Offer Details, Customer Touch Points, Support Contacts, Campaigns
Web Gigabytes Web Logs, Offer History, Dynamic Pricing, Affiliate Networks, Search Marketing, Behavioral Targeting, Dynamic Funnels
Big Data Terabytes Call Notes, Social Network, External Demographics, Business Data Feeds, Imagines, Audio, Video, Speech to Text, SMS
Size of Data
Big Data
Web
CRM
Admin C
ompl
exity
of D
ata
Illustrative
Copyright © 2014 Deloitte Development LLC. All rights reserved. 5
Creating value with the three V’s of big data
Velocity
Volume
Variety
Value
+
+
=
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Identifying the types of big data in insurance
Big Data is highly prevalent within insurance, but remains underutilized.
Type Which V Why is it “Big”
Structured Claims Data
• Volume • On average, 30 years of historical claims data is stored
Claims Notes and Emails
• Variety • Notes and emails are considered unstructured data
Telematics • Volume • Velocity • Variety
• Streaming data is captured frequently (minutes); the sheer volume and velocity of the data poses challenges for traditional relationship systems
Weather Patterns and Seismic Data
• Volume, • Variety
• Data can be provided in relational format or using geo-spatial parameters
• Volume is a long-standing issue with analyzing weather patterns
Social Media • Volume • Velocity • Variety
• A large amount of social media data is generated • Data is transmitted in varying formats, all unstructured • Data is created at a rapid pace
Copyright © 2014 Deloitte Development LLC. All rights reserved. 7
Enterprises face the challenge and opportunity of storing and analyzing Big Data, respectively. Insurers, in particular, may expect to be challenged with: • Handling more than 10 TB of data
• Data with a changing structure or no structure at all
• Very high throughput systems: for example, in globally popular
websites with millions of concurrent users and thousands of queries per second
• Business requirements that differ from the relational database model: for example, swapping ACID (Atomicity, Consistency, Isolation, Durability) for BASE (Basically Available, Soft State, Eventually Consistent)
• Processing of machine learning queries that are inefficient or impossible to express using SQL
Implications for the enterprise
“Shift thinking from the old world where data was scarce to a world where business leaders demonstrate data fluency” - Forrester
“Information governance focus needs to shift away from more concrete, black and white issues centered on ‘truth’, toward more fluid shades of gray centered on ‘trust.’ ” - Gartner
“Enterprises can leverage the data influx to glean new insights – Big Data represents a largely untapped source of customer, product, and market intelligence” – IBM CIO Study
Copyright © 2014 Deloitte Development LLC. All rights reserved. 8
Big Data is supported and moved forward by a number of leading vendors throughout the ecosystem. In many cases, vendors play multiple roles and are continuing to evolve their technologies to meet changing market demands.
Taking a look at the big data ecosystem
Big Data File and Database Management
Big Data Integration
Big Data Analytics
Stream Processing
and Analysis
Appliances
BI/Data Visualization
Big Data Ecosystem
Where can Big Data Bring Value in Insurance?
Copyright © 2014 Deloitte Development LLC. All rights reserved. 10
Making big data and analytics top of mind
Insurance is a tough market. Big Data driven analytics can provide an edge in both day-to-day management decisions and in finding top line growth Industry-wide investment is turning analytics from an emerging issue into a core competency: • 82% of insurance executives
surveyed cite data and analytics as a key strategic priority
• 81% of insurance companies surveyed intend to increase spending on data initiatives in the coming years
• By 2016, it is estimated that 25% of large global companies will have adopted big data analytics for at least one security or fraud use case
Manage the Business • Gain visibility into operational performance • Improve statutory and market conduct reporting • Streamline core processes • Identify fraudulent claims
Doing Nothing is Not an Option
• Competitors and emerging startups are changing the industry, pushing analytics from an advanced capability to a core competency
Grow the Business • Create personalized pricing for customers • Build stronger distribution channels • Proactively cross- and up-sell current customers • Target opportunities in new geographies
Copyright © 2014 Deloitte Development LLC. All rights reserved. 11
Big Data
Telematics
Visualizations
Advanced Analytics
Claims Analytics
Applying big data in insurance
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Streaming telematics data
Stream
• Latitude and longitude captured at predefined intervals during a trip, typically within 1–3 minutes intervals
• Average number of drivers, taking an average number of trips per day — volume grows large very quickly
Event
• Excess speed, acceleration, breaking, turns, and other values derived from sensors
• Volume of events more variable depending driving conditions and driver behavior
Trip Score • Relative score based on various factors captured during a trip
Event Stream
Trip Score
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Identifying the risk
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Integrating telematics data to gain insights
Traditional Data
When combined with policy / demographic factors / claims experience / driving history, you can really start to answer the important questions.
Premium leakage? Are my drivers driving more than the estimate provided during underwriting?
What is the relationship between the driving behavior and driving history?
Do my drivers with lower scores have higher claims?
Traditional data
Stream Event
Insight
Copyright © 2014 Deloitte Development LLC. All rights reserved. 15
Moving from basic to advanced analytics
Technologies around Big Data have emerged to handle exponentially growing volumes, improve velocity to support real-time analytics, and integrate a greater variety of internal and external data.
Big Data and Advanced Analytics Attributes
Reactive
Gigabytes
Weekly/monthly reporting
Predefined, structured data
Strategic
Terabytes
Weekly/monthly modeling
Expanded, still structured
Real-time
Petabytes
Real-time modeling
Dynamic, includes unstructured data
Decisions
Volume
Velocity
Variety
Yesterday Today Tomorrow
Foresight Hindsight Insight
Reporting Predictive modeling Big Data and advanced analytics
Hypothesis Testing
Copyright © 2014 Deloitte Development LLC. All rights reserved. 16
Evolving the actuarial process
More sophisticated customer digital interactions require and enable increasing insight into customer behavior. Organizations that leverage big data and advanced analytics can have accelerated growth through greater insight and understanding of their expanded customer interactions.
New Signals Predictive models to push and alert business of opportunities and insights
Profitable Growth Investments in analytics infrastructure and tools to improve insight into financial and market information
Hidden Insight Social media has given
rise to new ways to connect with customers
and uncover patterns
Computing Capacity Real-time
processing and data mining are now
possible
Volume and Variety
Global data volumes
continue to grow
Copyright © 2014 Deloitte Development LLC. All rights reserved. 17
Empowering actuaries with analytics
A well-constructed and maintained Enterprise Data Warehouse frees up actuaries, analytics modelers , and data scientists to focus on the data itself and their loss/predictive models.
Trying to figure out how to draw and integrate data from a number of different sources takes valuable time away from actuaries and IT.
Product Analysis Design
Consistent Simplified
Basis
Rationalized Model Inputs
Methodology Analysis
Policyholder Data
Lapse/PUP/Surrender Rates
Expenses
Mortality
Bond Rates
Unit Allocation Rates
Commission
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Automate unstructured claims data with analytics
Analytics on unstructured data is a process to automate the interpretation of language to find the useful information hidden in documents and text within the enterprise and from external sources.
Usage
Unstructured analytics enables the following capabilities: Capture early signals of customer
discontent Quickly target product deficiencies Find fraud Route documents to those who can
best leverage them Comply with regulations such as
XBRL coding or redaction of PII
Data Retrieval
The Data retrieval engine searches across all relevant content to provide a summarized output
Text Mining
Text mining tools extract and identify relationships between entities of interest
Other Capabilities
Linguistic and statistical techniques to extract concepts and patterns Transformation of language into data Unlocking of meaning and
relationships
Copyright © 2014 Deloitte Development LLC. All rights reserved. 19
Use analytics to improve loss outcomes
Claim adjuster notes and call center notes, often stored as free-form texts, contain valuable information that can be leveraged for better claim outcomes and improve efficiency within claims organization.
Big Data platforms allow insurers to perform advanced analytics on the unstructured claim adjuster notes and to
provide near real-time updates, which opens up the following
possibilities
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Realize claims efficiencies
First Notice of Loss Call center notes used to
predict severity Social media data along
with notes used to predict potential fraudulent activities
1 Triage/Assign Claim With improved
severity prediction, claims are classified and assigned in timely manner, reducing costs
Improved claim segmentation leads to “best-fit” adjuster being assigned
2
Initial Claim Setup Improved severity
prediction allows more accurate reserves to be allocated
3 Perform Investigation Adjuster can
search for similar claims and replicate best practice
4
Negotiate / Settle Claim Improved
predictions lead to improved loss outcome
5
Performing advanced analytics on unstructured claims data can improve claim loss outcome and related costs by improving the efficiency and effectiveness of the claim adjuster’s claims handling activities and improving reserving practices.
Copyright © 2014 Deloitte Development LLC. All rights reserved. 21
Leverage visualization techniques
Most insurance companies have access to similar data sets; leading players use visualizations to combine these sets in complex ways to extract unique, actionable insights.
Example scenario
• The Chief Risk Officer or Chief Information Officer for a large Property & Casualty Carrier needs to prepare for an impending natural disaster, in this case a hurricane heading up the eastern seaboard
• Information about the path of the storm, insured risk, loss prediction models all need to be evaluated in combination with team location data
Examine the hurricane’s projected path using a real-time, publicly available information from NOAA
Overlay the path with the book of business. Projected losses correlated to in force policies and loss projections from Catastrophic Loss Models
Gain insights about where agents and adjusters are location, if they are likely to be impacted by the event, and what other field service personnel can be brought in for support
1. Examine 2. Overlay 3. Assess
The Journey to Big Data
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Move from basic to advanced information management. Big Data is the next step in the evolution of analytics to answer critical and often highly complex business questions. However, that journey seldom starts with technology and requires a broad approach to realize the desired value.
Expand on your capabilities
Reporting
Data Analysis
Modeling and Predicting
“Fast Data”
“Big Data”
Data Management
Standardize business processes
Focus less on what happened and more on why it happened
Establish initial processes and standards
Leverage information for predictive purposes
Analyze streams of real-time data, identify significant events, and alert other systems
Leverage large volumes of multi-structured data for advanced data mining and predictive purposes
Copyright © 2014 Deloitte Development LLC. All rights reserved. 24
Journey to big data
Develop a Strategic Plan Identify strategic priorities
Identify Opportunities Brainstorm and ask “crunchy” questions
Determine Data Sources Assess the landscape, current capabilities, and priorities
Adopt in Production Prioritize and implement successful, high- value initiatives in production
Identify and Define Use Cases Based on the assessments and business priorities, identify and prioritize big data use cases
Pilot and Prototype Identify tools, technologies, and processes for use cases and implement pilots and prototypes
1 2
3
6
4 5
Copyright © 2014 Deloitte Development LLC. All rights reserved. 25
Step 1: Develop a strategic plan
Every Big Data project starts with a short planning and scoping phase.
Conduct analysis
Evaluate current situation
Mission vision values
Situation assess-
ment
Key issues
Analysis of external sources
Analysis of internal sources
Synthesis
Future
Industry
Scenarios
Future industry
scenarios
Formulate strategy
Create transformation
plan
BI & analytics roadmap
Strategic Big Data
plan
Action-plans
Strategic options
Reward
Strategic direction
Workshops Interviews Brainstorm sessions, Workshops, and analyses
Implementation plan writing
Creativity and ideas
Think outside of the box
Copyright © 2014 Deloitte Development LLC. All rights reserved. 26
Step 2: Identify opportunities
Identifying strategic opportunities starts with asking “crunchy” questions for “sticky” business issues. This process is independent of the underlying data (volume, variety, and velocity) and therefore applicable to both traditional and big data analytics.
Sales • How many of our leads have
converted into sales? • What is the profile of those
leads? • What campaigns are
generating the higher response rate and have the best ROI?
Risk How can we eliminate offers to those adversely effected by underwriting decisions?
Customers • Are our customers frequently changing products? • What are the key customer metrics across LOB’s for acquisition, retention
rates, and customer satisfaction? • Who are the next 1,000 customers we’ll lose — and why? • How do factors such as politics and demographics affect the price our
customers are willing to pay?
Product How can we improve product pricing by analyzing data from different sources?
Copyright © 2014 Deloitte Development LLC. All rights reserved. 27
Asking the right question can go a long way. Big Data introduces new technologies and tools for coping with the volume, velocity, and variety that characterize data sources in current business ecosystem. The opportunities are exciting, but a multitude of difficult questions first need to be answered.
Step 3: Determine data sources
Selection Criteria
Data Structure What structure can be derived from nontraditional data sources to make storage, analysis, and ultimately decision-making easier?
Governance What data governance is appropriate when analysis is distributed, needs change, and data definitions and schemas evolve over time?
How is data quality managed across so many sources of data, many of which come from outside the organization, such as public social networks?
Architecture What levels of availability and reliability are possible in mission-critical applications when data volumes are so large?
What intellectual property, licensing, and data protection considerations apply when Big Data environments are distributed across boundaries?
Infrastructure Is specialized hardware required for a particular need, or can low-cost commodity hardware be leveraged to scale processing?
How can current IT skill sets best be leveraged in evolving the infrastructure to include Big Data?
Copyright © 2014 Deloitte Development LLC. All rights reserved. 28
Step 4: Identify and define use cases
Identify and define use cases to unlock the value of Big Data.
Identify
Identify key information needed and the data sources required.
Access
Access internal and external data sources to provide an integrated view of the organizational data.
Analyze
Analyze the data using statistical tools and techniques to discover patterns and generate insights.
Act
Act on the insight from the analytical models and visualizations to produce business results.
Visualize
Visualize the data to engage non-technical business users and focus attention on the right problems.
Copyright © 2014 Deloitte Development LLC. All rights reserved. 29
Steps 5 and 6: Pilot and adopt
Valuable time and money can be saved by adopting a business user driven prototyping approach that targets value providing initiatives.
Governance and stew
ardship
End User Environment
Collection
Ingestion
Discovery and
cleansing
Integration
Analysis
Delivery
Production
Extract & Load
LOB applications Files Data marts
Marketplace — external data
Data quality
Analysis cubes
Data warehouse
Transform
Analysis Reports Dashboards & scorecards
Analyze
Business user
Hypotheses / questions ? Pilot
Spreadsheets, Specialized Tools, Sandboxes
Value? Yes 1 2
POC
prototype
3 Implement
AND
4
Repeat the POC / prototyping process with more value-providing initiatives
Repeat process Adopt Implement successful, high- value initiatives in production
Big Data environment
Visualize
Analytical environment
Hadoop | MPP | Appliance | In-memory
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