CAS Big Data December 6, 2012

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Big Data

I. Definition

II. Growth in Storage, Computing Power and Data III. Effect on the Organization -- Tasks and Roles IV. Big Data’s Impact on Other Industries

V. Data has always been crucial to insurance. How is Big Data different?

VI. Important Elements in the Insurance Environment VII. Insurance Big Data Example – Usage Based

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Definition

• At what point does:

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Definition

“Lots of” become “Big”?

• Typically definition is operational, i.e. working with Big Data requires the coordination of multiple

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Definition

“Lots of” become “Big”?

• Typically definition is operational, i.e. working with Big Data requires the coordination of multiple

computers

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McKinsey View on Big Data

Effectiveness

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Significant Improvement is Possible

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Trends Affecting Big Data

Cost to process and store data is shrinking Process - Moore’s Law

Storage - 2010

9 7 exabytes in disk storage

9 6 exabytes on PCs, notebooks and mobile devices

9 Average company 200 terabytes

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2010 World Data Storage Capabilities

Other

Disk

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Database Size

Name Power of Ten Example

Kilobyte 3 Xmas card list is about 500 kb in Access Megabyte 6 This PowerPoint is 2.9 meg

Gigabyte 9 Clients routinely pass my team 5 Gigabyte files representing a month’s worth of customer data

Terabyte 12 Colleague described their UBI database as 7 terabytes -- Library of Congress is 235

Petabyte 15 Sensor readouts for oil exploration companies

Exabyte 18 5 of these would equal the number of “seconds since the Big Bang”

Avogadrobyte*

*term I invented 24

2 times the number of molecules in 22.41 L of any gas

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Trends Affecting Big Data

Shrinking: Cost to process and store data

• Process - Moore’s Law • Storage - 2010

Growing: Data

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Trends Affecting Big Data

Shrinking: Cost to process and store data

• Process - Moore’s Law • Storage - 2010

Growing: Data

• NewAge.com (Q4 2011) 1.2b social media users over the age of 15 are on for long periods every day

• 800 exabytes in 2009 (60 times storage) • Annual growth rate of 45%

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Data

• Growing array of sensors that connect machine-to-machine create 30% more data each year

9Satellite imagery

9Telematics

9Radio Frequency Identifiers (RFIDs)

9Event Date Recorders (EDRs)

9Electronic Health Records (EHRs)

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Data

9Satellite imagery

9Telematics

9Electronic Health Records (EHRs) • Not all digital – text, video, speech

• Unclear how to reconcile 30% and 45% annual growth estimates for data. Both represent an avalanche.

• As the data set grows organization and flexible tools become more critical.

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Data

9Satellite imagery

9Telematics

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Data

9Satellite imagery

9Telematics

• Not all digital – text, video, speech

• Unclear how to reconcile 30% and 45% annual growth estimates for data. Both represent an avalanche

• As the data set grows organization and flexible tools

become more critical

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Data

9Satellite imagery

9Telematics

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Data

• Prioritizing what to

collect is a key skill

• Decisions to retain

or discard should

be periodically

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Data

• Prioritizing what to

collect is a key skill

• Decisions to retain

or discard should

be periodically

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Mae West

“Too much of a good

thing…”

is wonderful

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Mae West

“Too much of a good thing

is wonderful.”

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Herbert Simon

“A wealth of information

creates a poverty of attention

and a need to allocate that

attention efficiently among

the overabundance of

information sources that

might consume it.”

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Big Data’s Effect on the Organization

Functions and Their Roles • Information Technologists

• Data Management

• Actuaries, Statisticians and Product Developers

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Big Data’s Effect on the Organization

Functions and Their Roles

• Information Technologists

• Data Management

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Big Data’s Effect on the Organization

• Information Technologists • Data Management

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Big Data’s Effect on the Organization

• Information Technologists • Data Management

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Key Tasks

Identify:

• Data to collect

• Technological and analytic capabilities necessary to access/analyze data.

• Management processes to evaluate and use the results.

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Key Tasks

Identify:

• Technological and analytic capabilities necessary to

access/analyze data

• Management processes to evaluate and use the results.

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Key Tasks

Identify:

• Technological and analytic capabilities necessary to access/analyze data

• Management processes to evaluate and use the

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Challenges

• Lead time for the collection of data. Process to identify what needs to be collected including data from 3rd _parties

• Data base organization.

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Challenges

• Lead time for the collection of data. Process to identify what needs to be collected including

data from 3rd _parties

Metadata – easy to access the data for an analysis

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Challenges

• Lead time for the collection of data. Process to identify what needs to be collected including

data from 3rd _parties

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Big Data IT Requirements

• Large database storage

capabilities 9Big Table 9Cassandra 9Cloud Computing • Distributed processing strategies 9Hadoop 9Stream Processing

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Big Data IT Requirements

• Large database storage capabilities

9Big Table

9Cassandra

9Cloud Computing • Distributed processing

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Big Data Technologies

McKinsey Study June 2011

Big Table Business Intelligence Cassandra Cloud Computing Datamart Data Warehouse Distributed System Dynamo

Extract, Transform and Load Google File System

Hadoop HBase MapReduce Mashup Metadata Non-relational Database R Relational Database Semi-structured Data SQL Stream Processing Structured Data Unstructured Data Visualization

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Big Data Analytical Techniques

McKinsey Study June 2011

A/B Testing

Association Rule Learning Classification

Crowdsourcing

Data Fusion and Data Integration Data Mining Ensemble Learning Optimization Pattern Recognition Predictive Modeling Regression Sentiment Analysis Signal Processing Spatial Analysis

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Management

• Set priorities for data and analysis

• Supervise main actors

• Approve changes supported by analyses • Involvement is key

9 Fast turnarounds and willingness to work with data.

9 Recognize that views may change as additional data emerges.

9 Slick is not always a positive attribute.

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Management

• Set priorities for data and analysis

• Supervise main actors

• Approve changes supported by analyses • Involvement is key

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Management

• Set priorities for data and analysis • Supervise main actors

• Approve changes supported by analyses

• Involvement is key

9 Recognize that views may change as additional data emerges.

9 Slick is not always a positive attribute.

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Management

• Set priorities for data and analysis • Supervise main actors

• Approve changes supported by analyses

• Involvement is key

9 Fast turnarounds and willingness to work with data 9 Recognize that views may change as additional data

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Actuaries

• Identify the data

elements to collect and reevaluate these

decisions at regular intervals

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Actuaries

• Identify the data elements to collect and reevaluate these decisions at regular intervals

• Integrate Big Data into rate making and reserving

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Actuaries

• Participate as technical experts on company cross-functional teams for process improvement

• Help decision makers quantify their decisions. For example, the asset value of renewals.

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Actuaries

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Big Data Impact on Other Industries

• Airlines

• Oil Exploration

• Retailers

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Airlines

– mature applications

Southwest and Continental

Goal is to maximize ticket revenue while minimizing overbooking. Lots of 3rd _party tools to choose from:

• Teradata Airline Decisions • PROS and PROS Cargo

Teradata’s features:

9 Leverages your Integrated Passenger Name Record (iPNR) data warehouse with sophisticated analytics on overbooking and passenger no-shows

9 Highlights the impact of customer behavior on demand and show-rate trends hidden in PNR details

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Airlines

Goal is to maximize ticket revenue while minimizing overbooking. Lots of 3rd _party

tools to choose from:

• Teradata Airline Decisions

• PROS and PROS Cargo

9 Provides detailed descriptions of each metric and report layout, including the value and audience for the report

PROS was a pioneer in the field:

9 Aligning your RM (Revenue Management) department with other departments

9 Forecasting fluctuations in passenger demand

9 Managing revenue in markets without fences or price restrictions

9 Differentiating customer value in real-time

9 Accepting group requests without displacing high-value demand

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Airlines

Goal is to maximize ticket revenue while minimizing overbooking. Lots of 3rd _party

tools to choose from:

• Teradata Airline Decisions

• PROS and PROS Cargo

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Oil Exploration

– enormous data

Chevron reported:

9 Industry wide estimates of 8 percent higher production rates and

9 6 percent higher recovery from a "fully optimized" digital oil field.

9 Internal IT traffic of 1.5 terabytes a day

Seismic exploration develops huge datasets:

• Petabyte

• Elastic wave propagation is measured through

• Land based acoustic receivers

• Marine based hydrophones Operational Control Systems:

• Distributed sensors and • High-speed communications

• Explored with data-mining techniques to monitor and fine-tune remote drilling operations.

The aim is to use real-time data to make better decisions and predict glitches

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Oil Exploration

Chevron reported:

9 Internal IT traffic of 1.5 terabytes a day

Operational Control Systems:

• Distributed sensors and • High-speed communications

• Explored with data-mining techniques to monitor and fine-tune remote drilling operations.

The aim is to use real-time data to make better decisions and predict glitches

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Oil Exploration

Chevron reported:

9 Internal IT traffic of 1.5 terabytes a day Seismic exploration develops huge datasets:

• Petabyte

• Elastic wave propagation is measured through • Land based acoustic receivers

• Marine based hydrophones Operational Control Systems:

• Distributed sensors and

• High-speed communications

• Explored with data-mining techniques to monitor and fine-tune remote drilling operations. The aim is to use real-time data to make better decisions and predict glitches

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Retailers

– emphasis on customers

Amazon

Largest online retailer in the US.

Collects huge volumes of customer choice data elements

9 Products researched

9 Products purchased

Use this data to develop their recommendation engine

9 “You might be interested in…” recommendations

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54 We Have Spent Our Work Lives Knee Deep in Data – What’s Really New Here?

• New data sources and dramatically increased volumes

• Allow the use of innovative analytical techniques

• Applicable to a wider array of issues and processes

• Actuarial experience with data and analysis is a key resource to every insurance

company

Each of us knows someone like M. Jourdain in the Middleclass Aristocrat (Bourgeois Gentilhomme) who earns our scorn for his smugness at the discovery that he has “spoken prose all of his life.”

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We Have Spent Our Work Lives Knee Deep in Data – What’s Really New Here?

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company

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We Have Spent Our Work Lives Knee Deep in Data – What’s Really New Here?

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company

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Special Characteristics of Insurance

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Value of Renewal Book

Combined Ratio of 94

20% New Business at 120 CR 80% Renewal at 88 CR

U/W Asset Value (in Written Premium) of Renewals

80% of Premium is Renewal 12 point U/W Margin

2 Year Average Additional Life

– 19.2% of EP

– Each additional month of life adds .83%, e.g. 3 years 28.8%

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Value of Renewal Book

Combined Ratio of 94

20% New Business at 120 CR 80% Renewal at 88 CR

U/W Asset Value (in Written Premium) of Renewals

80% of Premium is Renewal 12 point U/W Margin

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Special Characteristics of Insurance

• Legacy Book is most valuable asset

• Like all relationship businesses

client

retention and cross-selling is key

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Need for Customer Lifetime Value

• Individual client relationships

represent dramatically different values to the company. Value range is more than an order of magnitude from

lowest to highest

• Decompose renewal value and

assign it at the individual customer relationship level

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Need for Customer Lifetime Value

lowest to highest

9PLE or Policy Life Expectancy

9Include the value from all insurance relationships

• Critical element for the objective function on many projects

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Need for Customer Lifetime Value

lowest to highest

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Special Characteristics of Insurance

• Legacy Book is most valuable asset

• Like all relationship businesses client retention and cross-selling is key.

• Regulatory realities

– Limit intervals between changes – More disclosure

– Identical risks get the same rate, no AB experiments

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Usage Based Insurance

Big Data Example in our Industry

Auto Market ($166 B Personal; $24 B Commercial)

– 187 million personal vehicles

– Around 10% of vehicles because of age or other factors don’t support telematics.

– 90% of US vehicle population is 168 million.

– Potential market is 29% of 168 million or 49 million vehicles UBI offered on “opt-in” basis. Market Research and

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Usage Based Insurance

Big Data Example in our Industry

Auto Market ($166 B Personal; $24 B Commercial)

– 187 million personal vehicles

– Around 10% of vehicles because of age or other factors don’t support telematics.

– 90% of US vehicle population is 168 million.

– Potential market is 29% of 168 million or 49 million vehicles

UBI offered on “opt-in” basis. Market Research and Progressive experience

– 40% “interested in UBI” – 75% enroll in Snapshot

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UBI – Tripsense

Progressive Tripsense 2005

Safe Driver Neutral Unsafe Discount Before Mileage

Adjustment 20% 15% 10%

Discount After Mileage Adjustment

0 to 2,500 18.4% 13.4% 8.4%

2,501 to 5,000 15.1% 10.1% 5.1%

5,001 to 7,500 11.8% 6.8% 1.8%

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UBI Data Records

Once Per Second

• trip_id • update_time • latitude • longitude • altitude • gps_speed • obd_speed • engine_speed • event_code • coolant_temp • trip_odometer

Four Times Per Second

• accel_lat • accel_lon • accel_vert

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UBI Market Composition

Program Segments:

1. Reusable Device with Limited Service Capabilities

2. Installed Device Provides Driver Feedback and Value Added Services

3. Specialized Programs For Unique High Service Markets

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Value of UBI

Simplified Illustration

Benefit: 20% improvement in pure premium

Costs

Average Discount: 12.5%

Average Cost for Technology and Communication Costs: 3.5%

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Value of UBI

Simplified Illustration

Costs

Average Cost for Technology and Communication Costs: 3.5%

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Value of UBI

Simplified Illustration

Costs

Average Cost for Technology and

Communication Costs: 3.5%

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Impact of UBI on Conversion

Elasticity example relevant to UBI

– Assume elasticity of 2, before intro of UBI program

– 15% Discount should result in a 30% increase in conversion

– Prior to the introduction of the discount quotes converted at a 20% rate

– The 30% increase in conversion will increase the conversion rate to 26%

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Elasticity Depends on Expected

Competitiveness

Example:

– Applicant considers a quote from Company A that includes UBI

– Rate, before UBI, is 5% less than the applicant’s renewal offer. – Once the UBI data is collected it will likely result in an additional

10% saving.

Key Question

Is the conversion rate improvement like we would expect to observe at 5% or 15%?

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Elasticity Depends on Expected

Competitiveness

Example:

– Applicant considers a quote from Company A that includes UBI

– Rate, before UBI, is 5% less than the applicant’s renewal offer

– Once the UBI data is collected it will likely result in an additional 10% saving

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Elasticity Depends on Expected

Competitiveness

Example:

– Applicant considers a quote from Company A that includes UBI. – Rate, before UBI, is 5% less than the applicant’s renewal offer.

– Once the UBI data is collected it will likely result in an additional 10% saving.

Key Question

Is the conversion rate improvement like we would expect to observe at 5% or 15%?

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Elasticity Depends on Expected

Competitiveness

Example:

– Applicant considers a quote from Company A that includes UBI. – Rate, before UBI, is 5% less than the applicant’s renewal offer. – Once the UBI data is collected it will likely result in an additional

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Big Data

• Numerous insurance applications

• Actuaries

– Increase their impact

– Skills and experience to play a leadership

role in insurance

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Big Data

• Numerous insurance applications

• Actuaries

– Increase their impact

– Skills and experience to play a leadership

role in insurance

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Ed Combs

Ed Combs is currently the principal at G. Edward Combs Consulting, LLC.

Ed has had over 30 years experience in the Personal Lines Insurance Industry. He has consulted for many of the major Property and Casualty insurers and has been an executive at both Progressive and 21st _{Century Insurance.}

His certifications include:

CPCU - Chartered Property and Casualty Underwriter, and ARM - Associate in Risk Management.

He is a former board member of the Insurance Institute for Highway Safety, the Highway Loss Data Institute and the Advocates for Highway and Auto Safety.