Advanced Analytics Lab

SAS Fraud Framework and Social Network Analysis For Insurance

Copyright © 2009, SAS Institute Inc. All rights reserved.

Advanced Analytics Lab

Starting with the SAS Fraud Framework Increasing Fraud - The Business Problem  Fraudsters • • • • •

Far more sophisticated – organized crime, patient, sharing of rules Engaging insiders to understand detection environment High velocity of attacks – disappear after 2-3 transactions Hitting multiple channels and industries at the same time Continuously evolving fraud strategies

 Current Fraud Systems • • • • • •

Systems are silo’d by line of business - no sharing of data Current systems act on transaction or customer Rules and predictive models alone have limitations Rely on 3rd party systems No real proactive steps taken to combat Fraud (or Churn) Evidence insufficient to act upon 2

Copyright © 2009, SAS Institute Inc. All rights reserved.

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SAS Fraud Framework Innovation in detection driven by industry  Robust and flexible framework capabilities • Support for real-time, intra-day, batch execution

• Ability to use existing data infrastructure • Ability to use existing fraud alert output from any LOB / 3rd party • Business intelligence for all levels of users

 Support for business functions • Provide strategic insight into threats, trends, risks • Enterprise view of fraudulent behavior • Rapidly test , simulate, and deploy models/rules without dependence on IT • Ability to provide single view for investigators

 Phased approach to support tactical & strategic initiatives 3 Copyright © 2009, SAS Institute Inc. All rights reserved.

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Customer Successes  Large Auto Insurer (PIP)  100,000 claims supplied from high risk geo in historical data set  Over $70M in total payments on Investigation Worthy alerts identified in the validation phase

 Regional Auto Insurer  Used 27 months of historical data from one PIP/casualty jurisdiction

 Over $11M in actionable dollars-at-risk identified in validation phase

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SAS Fraud Framework Using a Hybrid Approach for Fraud Detection

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Analytic Engine Analytic Approach: Unsupervised Methods  Use when no 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 6 Copyright © 2009, SAS Institute Inc. All rights reserved.

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Analytic Engine Analytic Approach: Supervised Methods  Use when a known target (fraud) is available  Use historical behavioral information of known fraud to identify suspicious behaviors similar to previous fraud patterns

Fraud Scores

Predicted Fraud Scores Incomes

# of previous investigations

 Include parametric and nonparametric predictive models, such as generalized linear model, tree, neural networks, and so on 7 Copyright © 2009, SAS Institute Inc. All rights reserved.

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Analytic Engine Predictive Analytics

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Analytic Engine Analytic Approach: Text Mining

Text Mining (e.g., call center logs or investigator notes) 9 Copyright © 2009, SAS Institute Inc. All rights reserved.

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Analytic Engine SAS Social Network Analysis  Network scoring • Rule and analytic-based

 Analytic measures of association help users know where to look in network • Net-CHAID for local area of interest (node) in the network • Density, Beta-Index (network) • Risk ranking with hypergeometric distribution, degree, closeness, betweenness, eigenvector, clustering coefficients (node)

 Modularity (sub-network) 10 Copyright © 2009, SAS Institute Inc. All rights reserved.

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SAS® Fraud Framework Process Flow Operational Data Sources

Exploratory Data Analysis & Transformation

Alert Generation Process Fraud Data Staging

Customers

Business Rules

Alert Administration

SAS® Social Network Analysis

Analytics

Network Rules

Anomaly Detection

Network Analytics

Predictive Modeling Accounts

Transactions

Intelligent Fraud Repository

Learn and Improve Cycle

Alert Management & Reporting Case Management

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Why Social Network Analysis? Provides the ability to apply Rules, Predictive Models, and Anomaly Detection on linked data  More fraud/actionable cases identified • Including both previously undetected networks and extensions to already identified cases

 Reduction in false positive rates • SNA reduces false positives by up to 10+ times over traditional rulesbased approaches

 Improved analyst / investigation efficiency • Each referral takes 1/2 – 1/3 the time to investigate using SAS’ fraud network visualization on aggregated data

 Significant increase in ROI per analyst / investigator 12 Copyright © 2009, SAS Institute Inc. All rights reserved.

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Demo Screenshots

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Alerts Page

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Provider Details

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Details Page

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Network

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Demo Movie  Social Network Analysis – In Action http://tinyurl.com/SFFDemoMovie

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