Experian/SAS Architecture for FCCauthentication results Consumer authentication summary and...
Transcript of Experian/SAS Architecture for FCCauthentication results Consumer authentication summary and...
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Experian/SAS Architecture for FCC
June 28,2012
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Agenda
Introductions
Notional Process Flow for Eligibility Determination and Verification of Identity
► The Problem
► Conceptual Process Flow
Program Integrity Components
► Experian
● ID Proofing
● Comprehensive Datasets
► SAS
● SAS Fraud Framework
● SAS Real-Time Analytics
● SAS Case Management
Analytic Detection Engine Examples
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The Problem:
Currently are three types of calls into IP Relay Service Centers that are ultimately reimbursed by FCC:
Tara: The SSA has certified her bilateral hearing capabilities are under 40 decibels and she’d like to order some things from a local store.
Taylor: His hearing is just fine, and he’s completely hacked Tara’s email accounts obtaining ID and financial information and is placing an order for several Dell computers in her name.
Steven: Like Taylor, his hearing is just fine. Steven, however, doesn’t hack the information himself. He simply purchases ID numbers of program enrollees or obtains false documentation that qualifies him for entry into the IP Relay Service program so he can order products with stolen credit cards
FCC needs a comprehensive strategy to detect and properly classify every IP Relay call into one of these buckets to ensure that carriers spend their efforts connecting calls of legitimate program enrollees
Legitimate Enrollees
Sophisticated Criminals
Unsophisticated Criminals
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Conceptual Process Flow
But FCC can’t monitor the text of the calls…
► Not a problem for SAS/Experian Solution
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Program Integrity Components:
Program Integrity Components
► Experian
● ID Proofing
● Comprehensive Datasets
► SAS
● SAS Fraud Framework
● SAS Real-Time Analytics
● SAS Case Management
Analytic Detection Engine Examples
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Experian: Risk-based approach to identity proofingElements and value proposition
Element Description Value
SummaryDetailed and summary-levelconsumer authentication results
Consumer authentication summary and detailed-level outcomes that portray the level of verification achieved across identity elements such as name, address, Social Security number, data of birth and phone
Delivers a breadth of information to allow positive reconciliation of high-risk fraud and/or compliance conditions
Specific results can be used in manual or automated decisioning policies as well as scoring models
StrategyFlexibly-defined decisioning strategies and process
Data and operationally-driven policies, including KBA, that can be applied to the gathering, authentication and level of acceptance or denial of consumer identity information
Employ consistent policies for detecting high-risk conditions, reconciling those conditions that can be, and ultimately determine, the response to authentication results whether it is acceptance or denial of access
Adjust as operational policies warrant
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Experian:Key Precise ID output elements
Checkpoint results – consumer demographic summary and detail
► Match level codes
► Additional addresses, consumers, phone, DOB, SSN info
Fraud Shield indicators
Shared application conditions
National Fraud Database
Fraud classification
IP address verification and detail
Credit card verification
Scores and score factors
Out of wallet questions
Decisioning
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SAS Fraud Framework for IP Relay Services
Proactively applies combination of all 4 approaches at individual and network levels
Hybrid Approach
Using a Hybrid Analytic Approach for Filtering IP Relay Phone Calls
Suitable for known patterns
Suitable for unknown patterns
Suitable for complex patterns
Suitable for associative link patterns
Applicants
Case Results
Experian Real Time
Experian Master
Enterprise Data
If/Then/Else instructions for the system to filter calls:
Rules
Detect individual and aggregated abnormal patterns vs. peer groups
Anomaly Detection
Predictive assessment against known fraud cases
Predictive Models
Social Network Analysis
Knowledge discovery through associative link analysis
• If applicant designates certain phone numbers and IP addresses for use under their ID, deny service to all others.
• Abnormal and irregular call volume from individual enrollees
• Known individuals involved with collaborative rings identified as well as known associates; risk re-prioritized based upon associations.
• Previously known, compromised ID theft patterns overlaid on call attributes to determine similarity
• Typical call patterns for enrollee does not match actual
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SAS:Analytic Detection Engine Examples:
Bank of America (BoA):
► SAS hosts an environment that performs near-real time scoring on over 8 billion daily BoA credit and debit card transactions.
Internal Revenue Service (IRS):
► SAS is contracted to design and build a near-real time advanced analytic environment for compliance detection across all personal income tax returns.
Center for Medicare Services (CMS):
► SAS was contracted to detect fraudulent claims in the Home Health program. Using 32 previously successful prosecutions, we identified 4 distinct fraud patterns. We then overlaid those patterns on new claims data to provide 54 recommendations for investigation.
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SAS:The Hybrid Approach at IRS
Enterprise Data Management
► Public record datasets – EITCs are flowing out the door because prisoners file taxes from friend/family addresses. SAS will link third party data as part of Enterprise Data strategy
Rules
► Tax laws – Examples abound; if an individual earns over 70k per year, then they are not eligible to deduct student loan interest
Anomaly Detection
► Under-reporting of wages – Comparisons by job category, geographic area, market earnings, etc. allow us to detect outliers in the tax return data
Predictive Modeling
► Known TIN theft schemes overlaid on new tax returns to determine statistical similarity and prevention of identify theft before returns are paid.
Social Network Analysis
► Ghost Return Preparers – The Tax Preparer doing taxes for cash on the side and doesn’t sign individual returns with their information. IRS needs to detect when this occurs
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SAS:CMS Project Background - Scope
Fraud project focused on Florida HHA providers
► Florida was the only location with a list of known fraudsters (32)
► HHA Fraud in Florida is heavily characterized by outlier payments
● Definition of target provided by OACT
● “Outlier Payment” = claim cost exceeds predetermined threshold, which triggers “outlier” flag & government pays 80% of claim value
● 40% or greater “outlier payments” = probable fraud
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SAS:CMS Project Highlights - Results
18 competing models were created
► More than 40 variables included as model inputs
► 3 target definitions (2 addressed in this presentation)
4 patterns of questionable or suspicious provider behavior were identified using historical claims data
Top 5% of scored providers captured:
► Additional $800M potentially at risk
► 54 additional potential fraudulent providers
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Sophisticated Criminal – Mr. Calculating
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Unsophisticated Criminal: Mr. Obvious
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