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Visual Analysis For Insurance Fraud Detection and Investigation. Valerie A. Zicko CCA, CFE. Agenda. Finding Good Claims Data The Analytical Process Visual Analysis Visual Analysis And Visual Mining Software Examples of Visual Analysis Pros and Cons of the The Software.
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Visual Analysis For Insurance Fraud Detection and Investigation Valerie A. Zicko CCA, CFE
Agenda • Finding Good Claims Data • The Analytical Process • Visual Analysis • Visual Analysis And Visual Mining Software • Examples of Visual Analysis • Pros and Cons of the The Software
Finding Good Claims Data • Finding Related Claims is the Heart of Case Investigation and Fraud Detection • Claims Databases are the Most Effective Tool for Finding Claims
Finding Good Claims Data • Their Effectiveness is Directly Related to the Quality and Completeness of the Database • Multiple identifiers for a person: Name, SSN, DOB, Address • Properly formatted address • SSN preferably a verified SSN • Service providers
Analysis • Is the process of adding meaning to collected information • Requires going beyond the facts
As a Result of the Analytical Process We: • Identify what is known • Develop a hypothesis • Formulate a collection plan for the missing information • Develop leads for further investigation or research
The Hypothesis orTheory includes • Key Players WHO? • Criminal Activities WHAT? • Method of Operation HOW? • Geographical Scope WHERE?
SAMPLE HYPOTHESIS: A group of claimants from Watery Mills, including Merry Hider, I.M. Pompous, and Daffy Von Flake, are staging accidents. Daffy Von Flake is steering the claimants to Dr. Frank N. Stein, who is providing treatment and medical bills to support PIP/BI claims for non-existent injuries. Confidence Level: Somewhat confident
Examine the data in terms of entities (fields) and relationships to discover or verify patterns, relationships and anomalies. Visual cues are used to graphically depict the relationships an entities. Operates at the detail/transaction level Visual Analysis
Proactive Seeks Out Patterns From Unrelated Data General to Specific Reactive Seeks Additional Patterns in Related Data Specific to General Visual Analysis
Large Segments of Analyzed to Discover Patterns Claims for a Time Period Claims for Geographic Area Specific Claim Types Subjects for Further Analysis May Emerge Visual Analysis - Proactive
Specific Entities Are Targeted for a Drill Down Analysis. From a Pattern Analysis From a Suspicious Claim Related Records Are Fed to the Analyzer Visual Analysis - Reactive
Visual Analysis Software • ALTA Analytics - NETMAP™ • Harlequin - WATSON™ • I2 - ANALYST’S NOTEBOOK™ • Winshapes - CASELINK™
ALTA Analytics - NETMAP™ • NETMAP for Claims • Works with your corporate database • NICB, AISG interface • Works on normal Ins. Link types • www.altaanlytics.com • Big Bucks -6 figures
Harlequin - WATSON™ • Database + Chart Generator • Import your data • Pre-defined links • PC based • www.harlequin.com • Suite of programs • $1,000 to $15,000 per User
I2: ANALYST’S NOTEBOOK™ • Chart Generator • Reads your database for charts • User Specified links • Suite of programs • www.I2.co.uk • PC based • $5,000 to $8,000 per User
Winshapes - CASELINK™ • PC Based • Database + Chart Generator • Predefined Links • Designed for SIU • www.winshapes.com • $795 per user
Links types are pre-defined by software Enter data once - generate multiple charts Auto update as new info added Source data linked to chart Can process large amounts of data Data mining is intuitive Visual Analysis Benefits
Can Be Limited to Pre-defined Links Auto-generated Charts Can Look Like Dish of Spaghetti Learning = Time + Effort Program Makes Decisions. You Must Know How to Ask to Get the Desired Results. Non-standard Situations Are Forced Visual Analysis Disadvantages
Summary • Analytical methods help see beyond the obvious facts • Computer tools can make analysis easier and faster • Visual analysis software permits larger amounts of data to be analyzed more quickly • Visual analysis displays information in a graphical manner which aids clarity. • Nothing replaces digging for the facts.