The Insurance Fraud Race Using Information and Analytics to Stay Ahead of Criminals. Copyright 2010, SAS Institute Inc. All rights reserved.

Transcription

1 The Insurance Fraud Race Using Information and Analytics to Stay Ahead of Criminals 1 Copyright 2010, SAS Institute Inc. All rights reserved.

2 Presenters Deborah Smallwood Founder, Strategy Meets Action (SMA) James Ruotolo Principal for Insurance Fraud Solutions Global Fraud and Financial Crimes Practice SAS 2 Copyright 2010, SAS Institute Inc. All rights reserved.

3 Insurance Fraud Race Webinar An SMA Perspective April 27, 2011 Deb Smallwood Founder Strategy Meets Action

4 An SMA Perspective: The Insurance Fraud Race Today s Discussion Points Current State of Fraud Approaches & Solutions Business Benefits SMA Call to Action 4

5 Top 10 Imperatives for Insurers Make GOVERNANCE Work Rethink Business & Technology LEGACY Chart a Path for MARKET GROWTH Fast Path NEW PRODUCT DEVELOPMENT Embrace ENTERPRISE RISK MANAGEMENT Capitalize on Intelligence Risk to manage Analysis CLAIMS Unleash ANALYTICS On the DATA Link CUSTOMER Profitability COMMUNICATION Analysis Holistically Drive Dynamic DISTRIBUTION Channels Apply Smarts to Claims UNDERWRITING Analysis Source: SMA Make GOVERNANCE Work 5

6 Current State of Insurance Fraud An Old Problem in a New Age Current Efforts to Combat Fraud Growing Sophistication of Criminals 6

7 Insurer Fraud Fighting Approaches Any Combination SIUs Effective referrals More skilled resources Information Sharing Reporting Aggregation Analytics and Advanced Tools Lobbying Stiffer penalties Improved legislation More law enforcement resources 7

8 Required Business Capabilities Prevention Detection Case Management Management 8

9 Current Fraud Management Environment Information Sources Policy Claims Vendors/Other 3rd Parties HR External Databases Social Media Info Other Unstructured Data Detection and Investigation Tools Spreadsheets Manual Analysis Physical Damage Fraud Systems Specialized Fraud Systems Custom-Built Predictive Models Core Claim Systems Manual Case Management Automated Case Management Management 9

10 Advanced Fraud Management Environment Policy Claims Vendors/Other 3rd Parties HR External Databases Social Media Info Other Unstructured Data Common Data Repository Business Rules Anomaly Detection Integrated Tools Predictive Models Social Network Analysis Automated Fraud Case Management 10

11 Business Benefits from Advanced Fraud Techniques Insurer s consistently report on business benefits beyond loss costs and expense reductions Improve Adjuster/Investigator Efficiency Accelerated and Enhanced Investigation Current/Future Saving from Thwarting Organized Rings 11

12 SMA Call to Action

13 Strategy Meets Action Call to Action Find the Champion Source: SMA Determine Your Fraud Solution, Create the Strategy & Plan Get buy-in Prepare the Data, Buy Technology, & Develop Release Plan Deliver Quick Win, Continue to Expand & Roll Out Gain Momentum 13

14 An SMA Perspective: Featuring as an example: SAS Fraud Framework for Insurance 14

15 James D. Ruotolo Principal for Insurance Fraud Solutions The Insurance Fraud Race Using Information & Analytics to Stay Ahead of Criminals Copyright 2010 SAS Institute Inc. All rights reserved.

16 The Shifting Landscape of Insurance Fraud Insurance fraud is on the rise & today s schemes are: Increasingly sophisticated More agile Higher velocity Cross industry Yesterday s methods are insufficient to address today s fraud risk! 16 Copyright 2010, SAS Institute Inc. All rights reserved.

17 Suspicious Claim Identification Methods Push Pull Reliance on rules / red flags Inconsistent First-come, first-served vs. Advanced detection methods Consistent Optimal prioritization 17 Copyright 2010, SAS Institute Inc. All rights reserved.

18 SAS Fraud Framework for Insurance Business Analytics Framework SFFI Core Components Insurance Lines of Business Prevention Auto Home Workers Comp. General Liability Life & Health Detection Detection & Alert Generation Real-time Decisioning Network Analysis Alert Management Case Management Business Intelligence Data Quality & Integration Analytics 18 Copyright 2010, SAS Institute Inc. All rights reserved.

19 SAS Fraud Analytics Using a Hybrid Approach for Fraud Detection Enterprise Data Suitable for known patterns Suitable for unknown patterns Suitable for complex patterns Suitable for associative link patterns Policy Claims Rules Anomaly Detection Predictive Models Social Network Analysis Providers Applications Rules to filter fraudulent claims and behaviors Examples: Detect individual and aggregated abnormal patterns vs. peer groups Examples: Predictive assessment against known fraud cases Examples: Knowledge discovery through associative link analysis Examples: Referrals NICB Alerts Payments Claims History Claim within certain period from policy inception Delay in reporting claim No witness Ratio of BI to APD exceeds norm % accidents in off peak hours exceeds norm # claims / year exceeds norm for policy or network Like staged / induced accident indicators as known fraud Soft tissue injury patterns across claims Like network and claim growth rate (velocity) Claim associated to known fraud Linked policies & claims with like suspicious behaviors Identity manipulation Hybrid Approach Proactively applies combination of all 4 approaches at the claim, entity, and network levels 19 Copyright 2010, SAS Institute Inc. All rights reserved.

20 SAS Fraud Framework for Insurance Process Flow Operational Data Sources Exploratory Data Analysis & Transformation Fraud Data Staging Business Rules Alert Generation Process Alert Administration Social Network Analysis Claims Analytics Network Rules Policy Anomaly Detection Network Analytics Predictive Modeling Payments 3 rd Party Data Intelligent Fraud Repository Learn & Improve Cycle Alert Management & Reporting Enterprise Case Management 20 Copyright 2010, SAS Institute Inc. All rights reserved.

21 SAS Fraud Framework for Insurance End-to-End Solution Data Detection Reporting Administration Structured & Unstructured Data Sources Batch or real time processing Data Cleansing Data Integration Variable Extraction & Sentiment Analysis with Text Mining Business Rules Anomaly Detection Advanced Predictive Models Watch Lists Social Network Analysis Network-level analytics Hybrid Technology Advanced Ranking Technology Easy to use web based interface Advanced Query of integrated data Full business intelligence reporting capability Claim system integration Self administered Saas or Installed Custom alert queues Alert suppression & routing rules Workflow analysis Direct integration with SAS Enterprise Case Management For more information on SAS fraud solutions, please visit 21 Copyright 2010, SAS Institute Inc. All rights reserved.

22 Copyright 2010 SAS Institute Inc. All rights reserved.