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IASA 86TH ANNUAL EDUCATIONAL CONFERENCE & BUSINESS SHOW

Big Data Analytics: Answering the Unanswered Questions

Session 302

Introductions

John Runte, PrincipalBaker Tilly Virchow Krause, LLP414 777 [email protected]

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• Analytics Practice Leader• Designs and Implements Analytic Solutions –

Many in the Insurance Industry • Focuses on Big Data Solutions

Agenda

1) What is Big Data?

- Characteristics

- Disruptive Technologies

- Where Does Big Data Fit?

- Big Data At Work

2) Insurance Industry Perspectives

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“The secret of change is to focus all of your energy, not on fighting the old, but on building the new.”

— Socrates

What is Big Data?Big Data Is No Data Left Behind

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TransactionsYour Data:Decisions based on your data

Big Data:Decisions based on all data relevant to you

Machine-Generated Data

Social Data

Documents

ERP, CRM, DW/DMAny Data,

Any Source

80%

What is Big Data?Characteristics of Big Data

VelocityVolumeVariety

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What is Big Data?Big Data Challenges

Why big data presents a technical challenge to existing BI architectures?• Unfettered growth

•Terabytes, Petabytes, Zetabytes• From many sources

•Analog sensors, GIS, Twitter, Social Media• Relevant versus non-relevant

What is Big Data?Answering the next unanswered question

Looking for answers when you don’t know the question

1) You Don’t Know The Problem In Advance2) Each Problem is Slightly Different3) Each Problem Has Different Parts;

Each With Own Problems4) Velocity of Change & Disruption Is Significant5) Data From All Sources

» Structured Sources» Unstructured Enterprise Content» External 3rd Party Data

Disruptive TechnologyApplication Software Over Time

• Technology solutions get commoditized

• Innovations get implemented at competitors as they learn of new developments

• Commodity technology becomes table stakes and offers relatively low strategic value or competitive advantage

• Traditionally many firms focused on buying low-risk industry standard solutions

• Few chose to be innovators because they had the luxury to be fast followers

Tactical Strategic

Diff

eren

tiate

dC

omm

oditi

zed

ERP

ERP

ERP

Disruptive TechnologyThe “Pre-meditated” Analytics Visionary

• Sees the accelerated speed of software obsolescence

• Possesses an accelerated innovation capability

• Constantly creates newer analytics to maintain distance over competitors

• Has made the shift to a iterative analytics world

• Understands that being a fast follower doesn’t work

Tactical Strategic

Diff

eren

tiate

dC

omm

oditi

zed

BI

BI

BI

BI

Disruptive TechnologyYou have a choice…

Analytics Solution ValueTactical

Strategic

Spee

d of

Inno

vatio

nPioneers

Laggards

BIBIBI

BIBIBI

BIBIBI

BIBIBIBecome

DisruptedBecome

DisruptedBecome

Disrupted

Become a Disrupter

Search & Information Discovery

Advanced Analytics

All Data, Unanswered Questions

Prescriptive Actions, Machine Learning

Next Generation Databases

Historic Source of Truth

Data Warehouse / Data Marts

OLAP Cubes

Data Warehouse

Data Marts

Reporting, Query and Analysis Tools

Business Intelligence Tools

ERP, CRM & Other Transactional Apps

Databases

Sensors

Unstructured Data File Systems

Content Mgt Systems

RSS Feeds & Social Media

Where Does It Fit?

Automating an “Information Discovery” capability can help solve difficult problems relative to risk and appropriate pricing for risk by making sense of complicated data landscapes. Using information

discovery as the foundation for your analytics will enable you to weed out the noise and return accurate risk models with more accurate pricing. To accomplish this evolution, your analytics platform

should include the following:

Data DiscoveryEfficiently expose and

retrieve data whether it is structured or

unstructured, internal or external data buried in the

deep web or buried in a disparate company

databases.

Data UnderstandingRetrieving the data isn’t

enough – it’s the ability to understand and normalize the data that allows you

to make heads from tails.

Filtering and Attribute

IdentificationData is processed

through both context and risk-specific filtering to enable insight into the most pervasive data

themes.

Attribute Selection

What is important when? Risk discovery is not a

science built on paranoia – despite what traditional search engines strive for when building long lists of search results; not all

data is relevant.

Foundational Capabilities

Big Data at Work…..A Critical Foundation – Illustrative Example

1 2 3 4 5

Initiate DeliverMeasure

Risk Models, Ratings

Engines & Beyond…..

Attributes be darned, a clear picture in the right context is what will allow your organization to think ahead of their competitors

or at least more accurately price risk.

Information DiscoveryInformation Discovery + Other Advanced Analytics

Risk & Pricing:Target Quantifiable Improvements in Combined Ratios

INDUSTRIALIZECommunicate

Contact Management

Call Center Transcripts Agent Notes

Satisfaction Surveys

Policy Documents

PolicyGeneration

Service Performance

Relationship Strategy

Loss Evaluation & Adjudication

Compliance Management

Service Feedback

Social Network Data

Adjustor Notes & Reports

Prior Loss Details

Loss DescriptionReserve

Management

Big Data at Work…..A Critical Foundation – Illustrative Data Sources

Unanswered Questions and Problem Solving

What did I do?1) Searched for insights2) Got some additional

information – unstructured information

3) Looked at financial and transactional data

4) A combination of search, analysis, financial and unstructured data

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3

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AN ILLUSTRATIVE INSURANCE INDUSTRY OVERVIEW – P&C

The P&C industry is currently in a state of flux…

• Shifts in growth from developed countries to emerging markets

• Increase in the development and consumption of data and technology

• Escalation of both risk and regulation

• Changes in customer behavior

• Changes in competitive landscape

• Big Data not a best practice, as most carriers are stymied by the challenges of making a business case

1717 | © 2012 Oracle Corporation | Confidential – Oracle Internal

…moving to a “New Normal” with global competition.

Shifts in growth:• Aging Population• New Infrastructure Investments• Rising Middle Class• Low-Income Rural Communities• Attractive Growth Markets and Segments

Changes in customer behavior:

• Loyalty on the Decline• Clear Channel Preference• Heterogeneity in Emerging Markets

• Multi-Dimensional View of Customer a Necessity

…moving to a “New Normal” with global competition.

Changing Face of the Competition:

• Return of the Broker• Rise of the Aggregator• New Players• From Payer to Provider

Consumerization of IT:• Going Mobile• Digital Marketing• Analytics

The New Normal:• Slow GDP Growth• Increased Regulation• Commoditization• Stronger Roles of Intermediaries

New normal

Includes border-line profitability, primarily via investments, and the beginning of an increasingly unfavorable interest rate environment.

ERP CRM Enterprise Data Warehouse

Structured Operational Data

Information Discovery Platform Best platform for Unstructured Analytics

ServerHybrid Search/Analytical Database

Flexible Data Model

Social MediaContent Systems,Files, Email

3rd Party

Unstructured Data

Big Data

Analytical ApplicationsPre-built BI Solution for Understanding Operations

Operational Data ModelDashboards & MetricsPre-built Reports

EDW

Traditional BI Technology Stack

Clinging to a model-driven culture vs. being “Data-Driven” Competitors

Increasingly considering “Big Data” for competitive advantage that will yield growth and profitability

Personal Lines• Use of Telemetrics / Telematics, especially for discounting• Emergence of Usage-based pricing • M2M / Real-Time Analytics• Using new insights into customer preferences and behaviors to

substantially improve cross-selling and retention initiatives• Analyzing 100% of the calls from the call center via audio analysis• Customer-centric versus siloed by product• Real-time credit and fraud scoring


Commercial Lines• Lagging personal lines in innovation• Use of Telemetrics / Telematics, especially for discounting• Emergence of Usage-based pricing • M2M / Real-Time Analytics• Using new insights into customer preferences and behaviors to

substantially improve cross-selling and retention initiatives• Analyzing 100% of the calls from the call center via audio analysis• Video for commercial property surveillance• Customer-centric versus siloed by product• Real-time fraud and credit scoring

“Today there are relatively few insurers actively using big data at this point.”

— Martina Conlon, Novarica, April 2013

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Providing value across the entire insurance business process value chain

Examples

Customer Acquisition• How and where to prospect for good business. Effective customer acquisition efforts can help companies predict which prospects are likely to respond to a specific marketing campaign.

Target Marketing• Where to find the right customers, how to identify good customers. Optimal target marketing efforts are broader than customer acquisition efforts and focus on defining which prospects are likely to yield profitable business.

Broker Management • To help your company understand how your agency or broker force is performing, and how much of their “good business” is being submitted to your company

Examples

Cost Reduction • To reduce costs, for example, by making decisions on when to use outside data and when not to.

Retention Management • To understand which of your company’s clients are most likely to leave and, in addition, which are likely to be profitable and unprofitable

Claims Process Management • To understand which claims are likely to be fraudulent, which are likely to develop into large claims, and other factors

The emergence of Usage Based Insurance (UBI)

"Insurers have been experimenting with telematics for 15 years. Now, telematics is rapidly gaining momentum, and every auto insurer should be thinking about their plans for telematics and usage-based insurance."

- Mark Breading, co-author of UBI study and SMA Partner


Highlights of the study include:• Seventy percent of North American property and casualty insurers are

engaged in some stage of usage-based insurance, whether they are operating active programs, conducting pilots, or building strategies.

• Almost 20 auto insurers are now running UBI programs in US and Canadian markets.

• Eight of the top 10 US companies now have UBI programs or pilots underway.

• The most frequently used variables in UBI programs are mileage, time of day, speed, and hard braking.

• The key challenges insurers face in building successful UBI programs are managing technology costs, understanding loss experience, creating consumer demand, and overcoming patent issues.


Co-author Richard Welch of REW Consulting sees the industry nearing a tipping point: "Telematics is going to have a profound effect on the auto insurance business," says Welch. "Even if consumer adoption is in the low end of the consensus range, usage-based insurance will grow rapidly, and as it grows, the traditional market will shrink, making it very difficult for companies not playing in the usage-based segment to maintain market share."

Notice of Loss

Validation and Investigation Litigation Repudiation

Reserve Repair Salvage Subrogation

RefineReserve

Settle

FraudManagement

InvokeReinsurance

CRM

UpdatePolicy

DataWarehouse

The typical P&C claim process

Providing Root Cause Analysis

Sales and Marketing• Why do some geographical regions buy more of your product than others?• How competitive is your pricing?• How do agents and consumers regard your brand vis-à-vis your competitors?

Loss Risk / Claims Analysis• What additional factors are associated with claim losses?• What additional factors are associated with fraudulent claims?

Agent and Broker Management• What additional attributes are associated with high-performing agents?• What do agents and brokers think about your brand?

By including root-cause analysis to support “level appropriate” measures across the organization to enable better decision making and measures taken

Net SalesResults by Line of BusinessResults by ProductResults by Agency / Agent

Net SalesResults by Strategic Business UnitResults by ProductResults by Product/SBUResults by Agency / Agent

Operating Units

Strategic Business Units

Lines ofBusiness

CFO

Support Consistent Evaluation of Business

Performance

Management Decisions Linked to Value Drivers

Client Financial Performance Measures

Net SalesResults by ProductResults by Agency / AgentResults by Customer/ProductResults by Operating Unit

Net SalesResults by ProductResults by Agency / AgentResults by Customer/ProductResults by Operating Unit

Complementing traditional BI reporting

Insurance Based Use Cases

Opportunity PrioritySales and Marketing – Customer Acquisition and Retention 2

Fraud and Abuse 5

Agent / Broker Management 4

Underwriting / Loss Risk Analysis 3

Marketing 6

Product R&D 8

Claims Management and Resolution 7

Call Center 9

Complementary to Traditional BI 1

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Presenting numerous potential opportunities to better regulate risk; identify, attract and retain profitable customers and agents; offer new products and innovative pricing; etc.

Opportunity How Big Data can help

Accessing and analyzing consumer data for sales and marketing, especially customer acquisition and retention. This opportunity is aimed at propelling growth by accessing, aggregating and analyzing all types of consumer behavioral data, as captured on the web in blogs, connections, associations, travel, profiles and click activity. This will accelerate an insurer’s customer acquisition process so they can increase their overall sales and improve customer retention and profitability.

Using its unique technical design, big data technology can accommodate the volumes, velocity and complexity of consumer prospect and customer data, including text, and integrate data from various sources to provide new insights in sales and marketing for customer acquisition and loyalty. Additionally, it can enable further advertising insight by leveraging blogs, call center transcripts, customer surveys, customer transaction data and notes.Moreover, it can be used to provide insight and high visibility into

the online channel, a key driver of growth and competitive positioning for any insurance company. Solutions that allow product visibility and insight into all parts of the channel facilitates product development, introduction and distribution.

For a $3 Billion company, a 1% increase in sales effectiveness using Big Data Technology would result in ~$30 million in new sales, or ~ $2.5 million to the bottom line with a 8.5% profit margin.

Sample impact

Discovering New Insights In Sales and Marketing for Customer Acquisition and Retention

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Opportunity How Big Data can help

This opportunity is aimed at accessing, aggregating and analyzing all types of underwriting data to assist underwriters find relevant, accurate data points by scouring through millions of sources, both on and off-line. Algorithms would be used to parse the data to find what’s relevant, classify the risks based on their impact, understand and assess their likelihood so each applicant’s data could be subjected to them through an underwriting model. Then the highest-ranked information would then be presented to the underwriter could make informed decisions more efficiently.

Using its unique technical design, big data technology can accommodate the volumes, velocity and complexity of risk-related data, including risk correlation factors, telemetrics, prospect behavior, GIS, audio, video, etc., and integrate the data from various sources to provide new insights for underwriting. It can scour blogs, reviews, news, legal filings, and relevant information from a variety of other sources –including the insurer’s own data, and then deliver thesummarized results in a clean, categorized profile that can be saved for future reference or used to dig deeper into each data point.

For a typical $3 Billion insurer with a 70% loss ratio, a 1% decrease in losses using Big Data Technology would result in material impacts to the bottom line.

Sample impact

Discovering New Insights for Underwriting and Loss / Risk Analysis

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Disclosure

Pursuant to the rules of professional conduct set forth in Circular 230, as promulgated by the United States Department of the Treasury, nothing contained in this communication was intended or written to be used by any taxpayer for the purpose of avoiding penalties that may be imposed on the taxpayer by the Internal Revenue Service, and it cannot be used by any taxpayer for such purpose. No one, without our express prior written permission, may use or refer to any tax advice in this communication in promoting, marketing, or recommending a partnership or other entity, investment plan, or arrangement to any other party.

Baker Tilly refers to Baker Tilly Virchow Krause, LLP, an independently owned and managed member of Baker Tilly International. The information provided here is of a general nature and is not intended to address specific circumstances of any individual or entity. In specific circumstances, the services of a professional should be sought. © 2014 Baker Tilly Virchow Krause, LLP

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Questions?

John Runte, PrincipalBaker Tilly Virchow Krause, LLP414 777 [email protected]

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IASA 86TH ANNUAL EDUCATIONAL CONFERENCE & BUSINESS SHOW

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