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Optimizing Case Managementwith Predictive Tax Compliance
SPSS
Benjamin Chard
Senior Solution [email protected]
CGI
Ted London
Executive ConsultantTax and Revenue [email protected]
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Predictive Tax Compliance
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Where Does Predictive TaxCompliance Fit
Existing Data Operational Setting
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Data Warehouse Solution
•Reporting•Case Mgt
•Return Scoring•Enforcement
•Historical Returns•Current Returns
•Collections•Case Management
DWS – Existing Data Operational Setting
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From DWS to Prediction
•Reporting•Case Mgt
•Return Scoring•Enforcement
Historical Returns•Current Returns
•Collections•Case Management
DWS – Existing Data Operational SettingPREDICTION
Examine Data in Entire Dimensionality
Learn Trends Relationships Interactions
Unveil Emerging Shifts in Behavior
FEEDBACK
Build Models
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Predictive Modeling Scenarios
Revenue: better return on limited resources Audit Outcome Predict $per audit hour Collections – Treatment Streams, Collection Activities
No-Change: Decrease the number of false-positiveinvestigations Hit Ratio Self Cure
Proactive Approach / Reaction Time Coverage Legislative Concerns Taxpayer Attrition Quicker Enforcement Emerging Trends
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We are talking aboutReturn on Information (ROI)
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Existing Enterprise Data Sources
DATA MINING &PREDICTIVE ANALYTICS
Data Warehouse Solutions
OPERATIONAL SYSTEMS
Predictive ModelingDashboards
Case Load Distribution
Statistical/TrendAnalysis
Return On Information (ROI)
Predictive ModelResults Case Management
ROIROI
FBKFBK
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SPSS: Building Predictive Models
DATA MINING &PREDICTIVE ANALYTICS
Predictive ModelResults
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Three classes ofdata miningalgorithms:
Predict who is likelyto exhibit specificbehavior in thefuture.
Associate
“Patterns”
Predict
“Relationships”
Cluster
“Differences”
Data
Mining
Group casesthat exhibitsimilarcharacteristics.
What eventsoccur together?Given a series ofactions; whataction is likely tooccur next?
Predictive Analytics
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Concept Extraction
Bag of « Words » extraction
Expressions extraction
Named Entities extraction
Events/SentimentExtraction
Combined with structured data
70’s 80’s 90’s Now
Mr.Smithaka
Ahmed wasseenonthe
cornerof
ChurchEtc.
Mr. Smithwas seen
Mr. Ahmedcorner
Church St.Magnolia Ave.
Nov 13thMr. Smith -> Person
Mr. Ahmed-> Personaka -> Alias
was seen -> locationChurch St. -> Address
Magnolia Ave. -> AddressNov 13th -> Date
Mr. Smith (Person) -> aka (Alias) -> Mr. Ahmed (Person)was seen (location) -> Church and Magnolia (address) ->
November 13 (Date)
Mr. Ahmed in database wanted for questioning
Suspect-> send agent to this
location
“Mr. Smith aka Mr. Ahmed was seen on the corner of Church St. and Magnolia Ave. on Nov 13th”
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Predictive Asset Management
In single project there is the potential to createa large number of models and versions ofmodels: different predictions different algorithms different settings different training samples.
X # different data sets
X # different users
X # different locations.
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Feedback Collection
FEEDBACK
Existing Enterprise Data Sources
Data Warehouse Solutions
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Predictive Modeling Scenarios by User
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Predictive Tax Compliance
Tax Collection•Treatment•Activities
• Audit Selection • Filing Fraud
Non-Filer Discovery•ID Fraud
•Prioritization Models
Register Assess Collect
DATA WAREHOUSE
DATA MINING & PREDICTIVE ANALYTICS TOOLS
Right work to the right resources at the right time
Fraud Detection
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Non-Filer/Fraud Discovery Process
Internal Records: Tax Filers External Records:• Registration Databases• Other Agencies• Commercial Databases
HARD MATCHING
SOFT MATCHING PRIORITIZATION
TAXDUE
NO TAXDUE
PredictiveModeling
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Audit Selection ProcessLEAD GENERATION
MANDATORYAUDIT PROG.
DATABASEAIDED SELECTION
“BIRD DOGGING”
SPIN-OFFS
LEAD CARDS
AUDITING
WORKLOADASSIGMENT
DISPUTERESOLUTION
$$$ NTC $$$
Tax Assessment Refund
No Tax Change
PredictiveModeling
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Tax Collection Process
Non-Filers
Non-Payers
Tax Due Estimation
PrioritizationRisk Assessment
Day 1
Day 15
Day 30
Day 1
Day 15
Day 30
Day 1
Day 30
Day 60
High Risk Medium Risk Low Risk
Predictive Modeling
Case InformationTaxpayer HistoryTaxpayer Profile
Outcome
Collections - Putting Data Analytics intoOperation
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Collections Scenario – Putting Data Analyticsinto Operation
Existing Enterprise Data Sources
Clementine – Data Mining Workbench
Data Warehouse Solutions
CGI CACS for Government
Predictive ModelResuls Case ManagementFBKFBK
ROIROI
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Putting Data Analytics into OperationCGI’s CACS-G Collection Application
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Data Mining Workbench
Case Study in Audit Selection
Evaluating Model Performance
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Case Study in Audit Selection
Build models to predict different outcomes. Positive Adjustment (Y/N). DPH Return (Group Membership). Actual $$ Adjustment.
Historical Cases selected for model build Cases with Prior audit – prior audit and organizational data. All Cases – organizational data only.
Deployment For each outcome combine predictions for those with and
without previous audit data . For each outcome predict using organizational data only.
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Evaluating Predictive Modeling
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Optimizing Case Managementwith Predictive Tax ComplianceCome see us in the Exhibit Hall
SPSS
Benjamin ChardSenior Solution [email protected]
Lisa BarberAccount [email protected]
CGI
Ted LondonExecutive ConsultantTax and Revenue [email protected]
© 2007 SPSS Inc. 28
SPSS Inc.
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ROI – Return on Information
Deploy Scores
Build the model thencreate and save thescores to a data tablewhich Case Managementcan access.
Deploy Model
Build the model and thensave the model andscoring process to filesthat can be executedreal-time.
