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SAS FRAUD FRAMEWORK FOR INSURANCE

MORE INFORMATION

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GLOBAL INSURANCE CLAIMS FRAUD

• US Insurance Information Institute estimate $30 billion losses annually; about 10% incurred losses and loss

adjustment expenses

• FBI estimate costs $40+ billion per annum; costing between $400 and $700 in extra premiums

• Insurance Council of Australia estimates that between 10 and 15% of insurance claims across of lines exhibit

elements of fraud

• Swedish Association estimate that 5 to 10% of claims include fraud

• ALFA estimate that fraud 15% of claims paid, or 4-8% of premiums collected equating to €2.5bn per annum

• ABI estimates that undetected fraud = £2.1bn adding about £50 to average premium

• South Africa Insurance Crime Bureau estimate that 30% of short term insurance claims include fraud

• Swiss Insurance Association estimate that 10% of claims paid are fraudulent

• German Insurance Association estimates that fraud costs circa €4bn per annum

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THE SHIFTING LANDSCAPE OF INSURANCE FRAUD

Insurance fraud is on the rise & today’s schemes are:

• Increasingly sophisticated

• More agile

• Higher velocity

• Cross industry

• Influenced by regulatory & political climate

Yesterday’s methods are insufficient

to address today’s fraud risk!

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BUSINESS ANALYTICS AND FRAUD DETECTION

Allows insurers to identify ‘suspicious cases’

Works underneath the insurers existing processes

Does not replace expertise of claims team members but ensures cases are not missed

Allows insurers to detect fraud by multi-dimensions

Case-by-case

Repeat

Organised rings

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FRAMEWORK-BASED

APPROACH END-TO-END SOLUTION

Data

• Structured & Unstructured Data Sources

• Batch or real time processing

• Data Cleansing

• Data Integration

• Variable Extraction & Sentiment Analysis with Text Mining

Detection

• Business Rules

• Anomaly Detection

• Advanced Predictive Models

• Watch Lists

• Social Network Analysis

• Network-level analytics

• Hybrid Technology

Reporting

• Advanced Ranking Technology

• Easy to use web based interface

• Advanced Query of integrated data

• Full business intelligence reporting capability

• Claim system integration

Administration

• Self administered

• Custom alert queues

• Alert suppression & routing rules

• Workflow analysis

• Direct integration with Case Management

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LEVERAGING SAS HYBRID APPROACH TO SCORE TRANSACTIONS,

ENTITIES, AND NETWORKS ACROSS MULTIPLE ORGANIZATIONS

Analytic

Decisioning

Engine

Automated

Business Rules

Anomaly

Detection

Predictive

Modeling

Text

Mining Database

Searches

Social

Network

Analysis

FRAUD ANALYTICS USING A HYBRID APPROACH FOR FRAUD DETECTION

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AUTOMATED

BUSINESS RULES KNOWN PATTERNS

• Automates manual processes

• Operationalize traditional “red flags” or

suspicious loss indicators

• Effective regardless of adjuster

training or experience level

• Administered by business

• Catch suspicious claims that would

“fall through the cracks”

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

DATABASE

SEARCHING KNOWN FRAUD

• Match against data already held on file

• Known customer

• Watch or Hot-list

• Match at household level

• ‘Supplier’ watch list

• Doctors, treatment centres, garages,

agents, lawyers etc.

• Country insurance industry co-

operatives

• Other external databases

• Data protection issues?

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

ANOMALY

DETECTION UNKNOWN PATTERNS

• Use when no known target exists

• Examine current behavior to identify

outliers and abnormal transactions that

are somewhat different from ordinary

transactions

• Include univariate and multivariate

outlier detection techniques, such as

peer group comparison, clustering,

trend analysis, and so on

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

PREDICTIVE

MODELING COMPLEX PATTERNS

• Base: uses confirmed fraud cases

• Use historical behavioral information of

known fraud to identify suspicious

behaviors similar to previous fraud

patterns

• Result – fraud risk score

• Include multiple modeling techniques,

such as regression analysis,

generalized linear models, decision

tree, neural networks etc.

Fraud Scores

Predicted

Fraud Scores

Claims # of previous

investigations

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SOCIAL NETWORK

ANALYSIS ASSOCIATIVE LINK PATTERNS

• Detect unexplained relationships

• Data Linking Analysis

• Nodes = individuals, policies, claims,

addresses, telephone numbers, repairers

(garages), medical providers, lawyers,

employees, bank accounts etc.

• Links

• Scoring: Rule and analytic-based

• Modeling techniques

• Sequence analysis

• Path analysis

• Fuzzy matching

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

TEXT MINING UNSTRUCTURED PATTERNS

• Up to 80% of insurer data is

unstructured text

• Adjuster notes

• Call centre logs etc.

• Configurable parsing, tagging, and

extracting of free text for use in fraud

analytics

• Combine quantitative and qualitative

data with text analysis to improve

predictions

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CASE MANAGEMENT

• Single portal for holistic view of fraud –

can see both current and historical

cases

• Enables Investigation Unit to:

• Manage investigation workflows

• Attach documents and digital files

• Record exposures and losses

• Utilize dashboards and management

reporting

• Track operational performance

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

FINANCIAL CRIMES

MONITOR

• Logically manage your rules, models

and alerts for investigators

• Maintain simple or complex routing

and suppression rules

• Manage analytical table, project,

scenario and scenario group

relationships

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

MAKING LIFE

EASIER

Final

Analysis &

Summary

Decision

to

Proceed?

Analysis

of

Findings

Combine &

Synthesize

Information

Rank &

Prioritize

Results

Query

Various

Systems

Establish

Search

Parameters

Final

Analysis &

Summary

Decision

to

Proceed?

Analysis

of

Findings

Analytical Value-Add

Combine &

Synthesize

Information

Rank &

Prioritize

Results

Query

Various

Systems

Establish

Search

Parameters

Framework-Based Predictive Analytics

“What used to take me most of a day, now takes 10 minutes.”

“It completely streamlines where we need to go.” -SIU Analyst

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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

• $2.1m in fraud recovery / prevention within the first 9

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 .

WHY SAS?

More suspicious cases identified

• Including both previously undetected fraudulent networks and extensions to already identified

fraud

Reduction in false positive rates

• Significant improvement in ‘quality’ of suspicious cases past for investigation

Improved investigation efficiency

• Each referral taking 1/2 – 1/3 the time to investigate using SAS’ link analysis visualization

“We discovered that 5% of its claims pay-outs were fraudulent, and these can now be

corrected and prevented in the future." Assistant General Manager, Market Leader, Southern Europe

“What used to take me most of a day, now takes 10 minutes.’’ SIU Manager, Major Tier 1 USA Insurer

“84% of the claims flagged as possibly fraudulent, turned out to be fraud. A 69 % uplift in

suspicious claim detection compared with the old system.." SIU Manager, Major Tier 1 USA Insurer

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:

Combatting Insurance Claims Fraud

Insurance Fraud Race

• Research:

State of Insurance Fraud Technology

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