volume, velocity and variety: how do you …...mexican market potential growth “the mexican...

1 Copyright © 2011 SAS Institute Inc. All rights reserved.

VOLUME, VELOCITY AND VARIETY: How do you insure yourself against the Big Data tsunami?

Thayer Adkins Information Management AMIS Mayo 2012

2

Company Confidential - For Internal Use Only

Copyright © 2012, SAS Institute Inc. All rights reserved.

Private Global Software Company Worldwide revenues $2.725 billon USD in 2011

12,000+ employees in 400 offices in 51 countries

Stability and Reliability Consistent profitable growth for 36 years

Focused on information management and business analytics

25% revenue spent on R&D

Insurance is in our analytic DNA! 150+ insurance customers in the United States

All ten of the top ten carriers

Dedicated analytic solutions and practice areas for Insurance and Financial Services

WHO WE ARE

3



0%

15%

70%

15%

11%

44%

33%

11%

0%

10%

60%

30%

We do not think that there is a New Normal

There is a New Normal, but we've made no response

Thinking and acting in somewhat different ways

Thinking and acting in significantly different ways

Large Midsize Small

To what extent are each of the following business options incorporated in your company's strategies because your company has adapted a New Normal outlook?

Source: Celent 2011 US Insurance CIO Survey

Insurance CIO Priority: “Building out stronger analytics and self-service models for business units to challenge the New Normal.”

OUR PERSPECTIVE

There’s a “New Normal” for insurers

4



OUR PERSPECTIVE

Mexican Market Potential

Growth

“The Mexican insurance market is second only to Brazil in terms of assets and premium in Latin America, totaling US$34,260 million in assets and US$19,743 million in premium for 2010. “ “The enormous potential for growth, as stated by current low insurance penetration of close to 2 percent or around 30 percent of the OECD countries’ average, remains to be exploited “ - March 2012 IMF Country Report No. 12/67

5



OUR PERSPECTIVE

Big Data is RELATIVE not ABSOLUTE

Big Data

When volume, velocity and variety of data exceeds an organization’s storage or compute capacity for accurate and timely decision-making

6



OUR PERSPECTIVE

Big Data is about COMMUNICATING

Big Data

The proliferation of user-generated content that can be leveraged to make better business decisions in real-time.

7



OUR PERSPECTIVE

Big Data is about COMMUNICATING

17% of the world’s population used a social networking site in 2011.

Twitter logs 290 million Tweets per day - Feb 2012

Facebook counts 850 million active users per month, 500 million per day - March 2012

80% of companies use social media for recruitment.

8



Social Media Trends

“Companies that can lead the charge and thrive in the Facebook world view instead of the Google world view are going to have a lot of success in the next few years.”

- Jonah Peretti– co-founder of Huffington

Post and founder/CEO of Buzzfeed.com

9



OUR PERSPECTIVE

The conversation is getting noisier

10



VOLUME

VARIETY

VELOCITY

RELEVANCE

TODAY THE FUTURE

DA

TA

SIZ

E

THRIVING IN THE BIG DATA ERA

11



12



13



14



Telco – Reduce Churn (prevent defection) Understand customer interactions Identify their Social Networks Identify their Key Influencers Understand the potential impact of their defection on other customers

Cu

sto

me

r Li

nk

An

alys

is

Wh

o a

re t

he

Infl

uen

cers

?

Benefits

Better identify customer segments (who are the leaders, friends of leaders and friends of friends)

Provide more effective one-to-one marketing efforts (incorporate info into existing models and campaign)

Ability to score and quantify the influence of an individual in a community

Results

Ability to execute a pre-emptive marketing campaign to reduce churn

“By identifying the influencers, we can make very special offers that will have a high 'take' rate and lead to a viral effect among their followers.” – Director of Customer Intelligence


Finding treasures in unstructured data

like social media or survey tools

that could uncover insights

about consumer sentiment

Mine transaction databases

for data of spending patterns

that indicate a stolen card..

Leveraging historical data

to drive better insight into

decision-making

for the future

Analyze massive

amounts of data in

order to accurately

identify areas likely to

produce the most

profitable results

FORECASTING

DATA MINING

TEXT ANALYTICS

OPTIMIZATION

STATISTICS

ADVANCED ANALYTICS

INFORMATION

MANAGEMENT


16



Frau

d D

ete

ctio

n

Net

wo

rk P

rovi

der

Fra

ud

SIU Business Problem: Personal Auto Insurer Reduce false positives when identifying claims for investigation,

Better prioritize SIU workload

Improve the efficiency of their investigative resources.

Approach

Using claim, policy, and SIU data, SAS developed multiple scoring models and network detection algorithms to identify suspicious losses. The models incorporate text mining to leverage useful variables in unstructured notes.

Results

A hybrid solution that incorporated rules, predictive models, text mining, anomaly detection and social network analysis.

Identified single high risk claims as well as collusive network behavior.

Reduced false positives and improved impact rate by 30%

Identified high risk claims early in the claim lifecycle, improving ability to impact the claims pre-payment.

Determined that additional data sources could improve results further.

17



Usage Based Insurance

GPS devices installed in vehicles are used to price insurance based on time, distance and location

OEM embedded telematics OBD data loggers Smartphone apps

Tele

mat

ics

Wh

at’

s yo

ur

dri

vin

g s

core

?

5,000 GPS enabled autos 2 trips per day 156 journey points 1.56 million rows of data per day

Results

Personalized premium

Lower claims frequency

Improved claims processing

Commercial Lines Fleets

Identify and understand areas in need of improvement within their fleet

Determine the telematics information that is available to shape their safety programs

Provide guidance on effective driver feedback and coaching

18



Pri

cin

g an

d U

nd

erw

riti

ng

“Yo

ur

dig

ita

l DN

A is

go

ld t

o in

sure

rs.”

Life Insurance Underwriting

Use of personal data to assess insurance eligibility and predict longevity

Target who is likely to qualify for coverage

Used as an indicator to determine the need for additional medical tests

Price Elasticity and Optimization

Customer response modeling

Understand the effect of different premium changes on different market segments

Customer attributes and attitudes

Producer attributes and attitudes

Internal influences Environmental influences Customer status changes

and triggering events

19



Mar

keti

ng

An

alyt

ics

“Min

ing

Dia

mo

nd

s”

Mining Diamonds with AXA Equitable AXA mines its customer data to identify target opportunities for financial

advisors

Development of customer satisfaction index

Approach

Using internal and external customer behavior and attribute data, AXA creates predictive propensity models to determine the next best offer for customer segments.

Results

Average number of sales increased by 40% for participating financial advisors

"One of the biggest challenges in the field is having thousands of clients and achieving a level of data segmentation that allows us to understand our customers better. That allows us to stand back and analyze the decisions consumers are making and the trends that exist.”

- Executive VP Case study available at: http://www.sas.com/success/axaequitable.html

http://www.sas.com/success/axaequitable.html


Copyright © 2011, SAS Institute Inc. All r ights reserved.

The most profound technological innovations are driven by the need to communicate

Web Content And

Social Media

Transactional Enterprise

Data Warehouse

Transactional Event Stream

BIG DATA BARRIER TO COMMUNICATION

Why are they doing it?

What are my customers doing?

21



“Voice of

Customer”

“Customer

Focus”

Company Customer

OUR PERSPECTIVE

Information is emotional and interactive.

CEM Customer Knows

Company

Deliver better customer/producer experience

Company Knows

Customer

CRM

Understand the customer/producer relationship

22



E-mail

Direct Mail

Chat Room / IM

Agent

Call Center

IVR Social Networks

Web (Public)

Mobile

• Alerts • Marketing Offers • Policies &

Statement • Inquiry Responses

• Alerts / Prompts • Marketing Offers • Inquiry Responses

• Research Products • Receive a Quote • Find an Agent • Request Information • Capture survey data

• Research Company • Share Experiences • Recommend (Y/N) • Gain Knowledge

• Reset PIN/Password • Policy Status • Claim Status • Make Payment

• Request a Quote • Buy a Policy • Billing • Claims – Inquiry,

Dispute, Submit • Make a Payment • Log a Complaint • Request Information • Manage Account

• Receive a Quote • Buy a Policy • Renew a Policy • Maintain Policy • Make Payment • Submit a Claim • Request Info • Respond to Offer

• Real-Time Inquiry • Call Center Access • Agent Access

Customer

• Alerts • Profile Access

• Location based services • Apps

Microsites

Sensor Data

• Receive a Quote • Maintain Customer

Profile • Buy, Renew,

Maintain Policy • Make Payment • Submit a Claim,

See Status • View Policies & Statements • Request Information • Marketing Offers

• Telematics

Vo

lum

e, V

elo

city

an

d V

arie

ty

© Copyright 2007, Customer Data Integration: Reaching a Single Version of the Truth (John Wiley & Sons)


INFORMATION MANAGEMENT:

BUSINESS & TECHNOLOGY IMPERATIVE

24



Insurance Business Drivers of Information Management

• Customer Satisfaction (and retaining the desirable customers)

• Data-driven Decision Capabilities

• Revenue Enhancement

Business and Revenue Optimization

• Claims Processing Cost Reductions

• Productivity Enhancement

• Data Asset Optimization

Cost Control

• Data Governance (both internal and external)

• Risk Mitigation

• Regulatory Uncertainty (new global regulations for insurers)

Governance, Risk and Compliance

25



Source: The Current State of Business Analytics: Where Do We Go From Here?

Prepared by Bloomberg Businessweek Research Services, 2011

EXTERNAL

VIEWPOINT CHALLENGES IN ANALYTICS ADOPTION

26



$19.0

$16.5

$15.5

$8.0

$7.7

$6.8

$0.9

$8.0

$2.2

$1.6

$3.0

$0.7

$3.3

$10.9

$1.1

$1.3

$0.7

$0.8

$0.7

Policy Admin

Various Core Systems

Data Mastery

Portal/Distribution

BPM/Rules/ECM/CRM/Fin

Claims

Infrastructure & Outsource

($mil)

Large Midsize Small

What is the total multi-year cost of your three most significant IT initiatives?

Source: Celent 2011 US Insurance CIO Survey

“We’ve overused the data warehouse pattern – it doesn’t work for analytics.” - SAS Insurance Customer

OUR PERSPECTIVE

Data mastery continues to challenge the industry.

Large Midsize Small

27



Report on Data Management

18% of companies are committed to collecting and analyzing data via a well – defined data management plan. 82% are not.

50% with big data management strategy say they outperformed their competition. Only 33% without a strategy were able to outperform the competition

39% of organizations surveyed now used data to drive strategy

Among those with a formal data management strategy, 25% said that the use of data over the past 5 years had completely changed the way they do business.

“Organizations with formal data management strategies get more value from data assets and outperform competitors” – A new School of Management – Economist Intelligence Unit and SAS

28



OUR PERSPECTIVE

Data awareness must evolve as well D

ata

Man

age

me

nt

Mat

uri

ty

High

Low

Standard Reporting

Enterprise Data Warehouse

CRM

Data Quality

Data Stewardship

Data Integration

Org. Data Governance

Governance Process and Policy

Low High Ability to Execute

29



Reconciling Policy Holders Across Channels

IVR

Billing

Claims

Marketing

Products

Purchases

Accounts

Product Research

Advocacy Opportunities

Bill

Claim

Payment

Premium

Household

Customer

Quote

Policy

Address

Phone Number

Interactions

Membership Renewal

Claim

Payment

Premium

Customer interactions

Quote

Address

Complaints

Web

Customers

Households

Address

Phone Number

Campaigns

Products

SOR

Policy System A

Partner Systems

Policy

Premium

Customer

Address

Phone Number

Assets

Bill

Claim

Payment

Premium

Household

Customer

Quote

Policy

Address

Phone Number

Interactions

Statement

Renewal Due

Payment

Address Claim

Policy Holder

Agent

Agent

Call Center

A policy holder has duplicated and unsynchronized data in different product systems.

Customer service personnel interact with members and require complete and up-to-date information about all interactions a customer had regardless of channel.

The marketing system has member demographic and household data. It derives campaign and value data from analytical and marketing processes.

The Call Center captures member interactions, complaints, personal notes, and policy, billing, and claims inquiries.

The IVR captures member interactions and inquiries.

The member self-service application on the Web captures member interactions, inquiries, communication preferences, and may create new relationships .

The Claims system captures a member’s claim history, involved parties (who may also be customers), claim status and payment and subrogation information.

Source: Customer Data Integration: Reaching a Single Version of the Truth (Wiley)

30



INSURER PERSPECTIVE

Case Study: Data management complaint session

31



Data Governance in Context

Executive Sponsorship

Data Governance

Data Management

Data Requirement

Data Architecture

Data Administration

Metadata Management

Data Quality

Security & Access Rights

Protect, Enhance and

Fund the Asset

Provide Oversight,

Manage Risks, Assess

Compliance on Asset

Intimate with business

processes; explain

business policies, rules

and guidelines, apply

data integrity processes

Develop and support the

asset per governing

policies & standards

Data Stewardship

32



Top-Down at An Insurer

Initial Vision Assemble

Core Team Create Guiding

Principles

Apply Process Workshop 1: Scenario Planning

Workshop 2: Ownership Workshop 3: Small Controlled Project

Identify Decision-

Making Bodies

Assign Decision Rights

Determine Governance Stakeholders

33



Modelo Maduro del gobierno de datos

Aplicación de Ventas

Marketing de bases

de datos

Proyectos dirigido por

TI

Datos duplicados e

inconsistentes

Inhabilidad de

adaptarse a cambios

del negocio

Almacén de datos

ERP

CRM

Linea de influencia de

los proyectos de TI en

el negocio

Poca colaboración

multifuncional

Alto costo para

mantener múltiples

aplicaciones

TI y colaboración de

grupos de negocios

Vista empresarial de

ciertos dominios

Los datos son un

activo corporativo

Los requisitos de

negocio impulsan

proyectos de TI

Procesos repetibles y

automatizados

Relaciones

personalizadas con los

clientes y operaciones

optimizadas

MDM de Cliente

MDM de Producto

MDM

Automatización de

procesos de

negocios

MDM de Empleado

MDM de Ubicación

34



Data Quality Mandates for Solvency II

(e.g. Articles 48, 82, 104 & 124)

Completeness

Accuracy

Measures Functions (e.g. Articles 82, 104, 121, 124 )

Assess

Verify

Appropriateness

35



Solvency

II Staging

Area

calculation

engine Capital

Adequacy

Reporting Risk

Reporting

Solvency II

Modelling

Data

Sources

Regulatory

Reporting

Data

Govern

ance

Gate

wa

y

Data

Pre

para

tion

Data

Assessm

ent

correction

Data Risk

Assessment:

-Accuracy

-Completenes

s

-Appropriaten

ess

Data Quality Reporting

Meeting the Solvency II Data Requirement

36



Customer Success Governance/Regulatory Compliance Customer name: Barclays Capital

Business issue: • Demonstrate a robust/transparent process for regulatory reports to meet SoIvency II Capital Adequacy

Directive • Gain better understanding of 50,000 ‘buy-to-let’ mortgage customers, properties and their associations. • Data migration mandate

SAS offering:

Development of Business Rules leveraging DataFlux Data Management Platform

Results:

• ROI in 6 months • DQ monitoring cost savings = £500K/year ($795K U.S.) • Automated data profiling = £50k pound ($79.6K U.S.) • Cut migration timelines by 30 days

37



Customer Success Data Quality Driven by Philosophical Change Customer name: Fortune 500 Insurance Company

Business issue: Handles millions of policies annually Growth through mergers and acquisitions; data residing in divergent and incompatible systems. This same data was crucial to their decision-making, business owners had little control over it. Poor data quality

» Sending multiple solicitations to the same clients » Approaching clients about policies for coverage they already had » Missing opportunities to offer clients and prospects services they genuinely needed

Solution:

Data Quality and Data Management Platform

» Data quality solution with the ability to give business users control over the data

Results:

The company realized time savings of as much as 93% from the implementation

Revenue increase and cost savings substantial

38



OUR PERSPECTIVE Final Thoughts….

Insurance analytics are moving beyond pricing

Your customers are talking – are you listening?

Big data is coming at you

Data governance is more important now than ever!

The Data governance needed to support this paradigm is evolving



E-mail: [email protected]

volume, velocity and variety: how do you …...mexican market potential growth “the mexican...

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