Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

SAS FOR INSURANCE

MORE INFORMATION

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

SAS & INSURANCE

• 1200+ insurance companies worldwide use SAS within

these areas:

• Actuarial

• Underwriting

• Claims

• Marketing

• Corporate Information

• Reporting

• Financial

• IT

• Risk

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

SAS & INSURANCE INSURANCE SOLUTIONS

SAS Risk Management for

Insurance

SAS Fraud Framework for

Insurance

SAS Insurance Analytics

Architecture

SAS Customer Analytics for

Insurance

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

ANALYTICAL

INSURER QUESTIONS INSURANCE EXECUTIVES ARE ASKING

Who are my profitable customers & agents?

What claims can I recover?

Where are my expenses increasing?

How can I increase market share?

What are my customers saying about us?

Who is committing fraud?

Are our products competitively priced?

…..

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

ANALYTICAL

INSURER

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

ACTUARIAL

ANALYTICS CHALLENGES

Rising underwriting expenses

Increased competition

Data integrity

Frequent rate revisions

Catastrophe forecasting

Long-tail liabilities

New risk classification

Telematics data

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

ACTUARIAL

ANALYTICS HOW TO OPTIMIZE PRODUCT PROFITABILITY

Multi-variant pricing using advanced analytical tools GLM, Neural Networks, Loss Triangles

Straight through processing for underwriting

Real-time pricing

Data integrity

Renewal impact analysis

Catastrophe evaluation

Reinsurance analysis

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

CUSTOMER STORY ONE BEACON (US)

Customer Quote

The models that we use and build with SAS give us a competitive advantage.

Todd Lehman, Vice President, Corporate Research

Business Problem

• Price insurance to improve bottom line

• Choose polices to underwrite

• Select claims for investigation vs. fast resolution

Results

• Loss ratio up by 2 to 4 points

• Operational projects see 10 times ROI

• Successful move into hard to price speciality lines

Solution

• SAS Enterprise Miner

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

CUSTOMER STORY FCCI (US)

Customer Quote

SAS has speed, sophistication and power

Ned Wilson, Vice President Treasury & Planning

Business Problem

• Reduce Churn

• Compete in deregulated market

Results

• 1.5 percentage-point improvement in combined ratio

from choosing whom to insure and from pricing products

appropriately

Solution

• SAS Enterprise Miner

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

CLAIMS ANALYTICS CHALLENGES

Increasing Fraud

Inaccurate loss reserving

Rising settlement costs

Spiralling litigation costs

Catastrophe resource planning

Ineffective salvage & subrogation processes

Limited Resources

Unstructured data

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

CLAIMS ANALYTICS PREDICTIVE ANALYTICS ACROSS THE CLAIMS LIFECYCLE

Litigation

Management

Medical

Management

Negotiation /

Disposition Evaluation Investigation Assignment

Set-Up &

Coverage Notification

Pre

dic

tive

Cla

ims O

pp

ort

un

itie

s.

Cla

im

Seg

men

tati

on

&

Assig

nm

en

t

Inju

ry /

Tre

atm

en

t M

an

ag

em

en

t

Customer Attrition Propensity

Subrogation / Recovery Identification / Propensity to Recover

Fraud Propensity

Process Adherence / Compliance

Attorney Representation / Litigation Propensity

Workforce Productivity / Performance

Lo

ss R

eserv

ing

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

CUSTOMER STORY CNA (US)

Customer Quote

We have an excellent partnership with SAS. They took the time to meet with us and truly understand the nuances of CNA so that we could build effective predictive models for each line of our business

Tim Wolfe, SIU Director

Business Problem

• Detect and prevent fraud in four separate commercial

lines of business

• Optimally direct its investigation resources on cases with

higher likelihood of fraud

Results

• $1.6m in fraud recovery / prevention within the first 6

months of implementation

• Detection and investigation of 15 potentially fraudulent

provider networks – four times what CNA anticipated

Solution

• SAS Fraud Framework for Insurance

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

CUSTOMER STORY TIER 1 INSURER (UK)

Business Problem

• Well established recoveries process

• Challenge was to see if analytics could improve recovery

rate

Results

• Increased recovery rate by 4% to 6%

• Significant impact on Combined Ratio

• Analytics is now an integral part of the claims processes

Solution

• SAS Enterprise Miner & SAS text Miner

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

CUSTOMER

ANALYTICS CHALLENGES

No single view of customer

Increasing acquisition costs

Lack of cross-channel integration

Decreasing retention rates

Ineffective segmentation and profiling

Insufficient customer insight

Ineffective agency performance measurement

Poor conversion rates

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

CUSTOMER

ANALYTICS HOW TO OPTIMIZE CUSTOMER INSIGHT

Improve customer profitability Profile, segment & predict customer behavior

Increase customer engagement

Enhance marketing performance

Multi-channel integration Recognize right channel for the right customer

Distribution insight Highlight leading / lagging sales productivity KPIs

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

CUSTOMER STORY MAX NEW YORK LIFE (INDIA)

Customer Quote

In the first quarter after implementing SAS, sales to existing customers jumped to more than 20 percent

Nagaiyan Karthikeyan, Head of Business Intelligence and Analytics

Business Problem

• Accurate data warehouse

• Increase customer retention

• Improve cross-sell sales

Results

• Increase cross-sell sales opportunities by nearly 300%

• 40 percent improvement in premium revenue

• Reduced sales expenses through shortened sales cycle

Solution

• SAS Campaign Management & SAS Enterprise Miner

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

CUSTOMER STORY TOPDANMARK (DENMARK)

Customer Quote

With SAS as a strategic partner, we ensure that we have the best technology and knowledge available. The vision of the data mining project is to find the relevant customers far more elegantly, and ensure that they stay with us

Bjørn Verwohlt, Marketing Director

Business Problem

• Automate marketing campaigns to drive strong lead

management instead of spending large sums of money on

mass communication

• Prevent lapses in personal lines

Results

• Generate more campaigns with improved results from

the same amount of resources

• Annually target the ‘best’ 5,000 customers with highest

risk of lapsing

Solution

• SAS Marketing Automation

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

RISK ANALYTICS CHALLENGES

New regulatory compliance

Data availability and poor quality

Unknown operational losses

Incomplete view of risk

Unreliable and inaccurate reporting

Limited or non-sophisticated risk tools

Lack of data transparency & auditability

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

RISK ANALYTICS BEYOND RISK COMPLIANCE WITH SAS

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

CUSTOMER STORY CHARTIS (US)

Customer Quote

We are now much more confident in making reinsurance decisions. Today we have a daily, real-time view of our risk

John Savage, Vice President, Strategic Risk Analysis

Business Problem

• Estimate risk of future losses

• Help underwriters access and price insurance risk

• Estimate bad debt reserve funds for premium receivables

Results

• $14m in new, low-risk business, representing 100%

segment growth

• Avoided potential loss of $75m from certain executive

liability accounts

• Reduced requirement for bad-debt reserve funds

Solution

• SAS Analytics

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

CUSTOMER STORY HDI ASSICURAZIONI (ITALY)

Customer Quote

We have met the double objective of improving data quality and streamlining information processes

Francesco Massari, Head of Organization and Information Systems

Business Problem

• Meet Solvency II requirements while improving data

quality and decision-making speed

Results

• Improve data quality

• Timely information reaches business users, actuarial

scientists and senior management

Solution

• SAS Risk Management for Insurance

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

SAS FOR

INSURANCE VALUE PROPOSITION

More granular pricing = 2 to 4 % improvement in Combined Ratio

Avoid poor risks = 1 to 3% improvement

in Loss Ratio

Reinsurance Analysis = 0.2 to

0.5% improvement in U/W Expenses

Fraud rates reduction by 2 to 5%

Recoveries increase by 3 to 6%

Marketing campaigns ROI

increase by 10 to 15%

3 to 5 times increase in

response rates

Lapse rates reduced by 20 to 25%

Capital allocation decrease by 1%

Copyr i g ht © 2012, SAS Ins t i tu t e Inc . A l l r ights reser ve d .

MORE

INFORMATION

• Contact information:

Stuart Rose, SAS Global Insurance Marketing Director

e-mail: Stuart.rose@sas.com

Blog: Analytic Insurer

Twitter: @stuartdrose

• White Papers:

Analytical P&C Insurer

Analytical Life Insurer

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