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Collections, Predictive Analytics and Taxpayer Compliance Management
John McCalden McCalden Consulting
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Agenda Some Collection Theory Decision Analytics
Taxpayer Compliance Management Q & A
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Percent of Cases Aging per Month (SC)
0% 20% 40% 60% 80% 100% 12 24 36 48 60 Age in Months Percent Source: South Carolina ARMS
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Percent of Cases Aging per Month (SC)
100% 80% 60% Percent 40% y = x R 2 = 20% 0% 12 24 36 48 60 Age in Months Power ( )
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Collection Rates, Based on Different Levels of Performance
100% 80% 60% Percent Remaining 40% 20% 0% 12 24 36 48 60 Age in Months Forecast: -0.2 Forecast; -0.5 Forecast: Forecast: -0.7 Forecast: -1.0 Forecast: -2.0
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Effect of Raising the Level of Performance From -0.6291 to -0.7
100% 80% 60% Percent Remaining 40% y = 1x R 2 = 1 20% y = 1x -0.7 R 2 = 1 0% 12 24 36 48 60 Age in Months Forecast -0.7
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Average Balance by Age of Case
$2,500 y = Ln(x) y = Ln(x) R 2 = R 2 = $2,000 $1,500 Average Balance ($) $1,000 $500 $0 12 24 36 48 60 Months Average Per Case Log. (Average Per Case) Source: South Carolina ARMS: Summary Receivables Report 12/31/2004
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Application of Aging Curve (-0
Application of Aging Curve ( ) and Average Balance Curve to a Hypothetical Cohort of 10,000 Cases Month Aging Cases Model 1 Total $ 1 10000 $5,763,100 3 5010 $4,963,770 6 3239 $4,056,100 12 2095 $3,171,340 18 1623 $2,705,105 24 1354 $2,403,706 36 1049 $2,022,712 48 876 $1,784,201 60 761 $1,614,037
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Percent of # and $ Collected, per Month of Aging
0% 20% 40% 60% 80% 100% 12 24 36 48 60 Age in Months Percent Collected % # Collected 1 % $ Collected 1
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Comparison of Percent of # and $ Collected, per Month of Aging
0% 20% 40% 60% 80% 100% 12 24 36 48 60 Age in Months Percent Collected % # Collected 1 % $ Collected 1 % # Collected 2 % $ Collected 2
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Hypothetical Improvement in Collections When Exponent Increases From -0.6291 to -0.7
Aging Aging Cases Total $ Cases Total $ # $ # $ Month Model 1 Model 1 Model 2 Model 2 Difference Difference Improvement Improvement 1 10000 $5,763,100 10000 $5,763,100 $0 0.00% 0.00% 3 5010 $4,963,770 4635 $4,592,230 375 $371,540 3.75% 6.45% 6 3239 $4,056,100 2853 $3,572,724 386 $483,376 3.86% 8.39% 12 2095 $3,171,340 1756 $2,658,173 339 $513,167 3.39% 8.90% 18 1623 $2,705,105 1322 $2,203,419 301 $501,686 3.01% 8.71% 24 1354 $2,403,706 1081 $1,919,059 273 $484,647 2.73% 8.41% 36 1049 $2,022,712 814 $1,569,578 235 $453,134 2.35% 7.86% 48 876 $1,784,201 665 $1,354,445 211 $429,756 2.11% 7.46% 60 761 $1,614,037 569 $1,206,816 192 $407,221 1.92% 7.07%
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How Do We Transition to a Higher Level of Performance?
100% 80% 60% Percent Remaining 40% y = 1x R 2 = 1 20% y = 1x -0.7 R 2 = 1 0% 12 24 36 48 60 Age in Months Forecast -0.7
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Use Decision Analytics!!
Use Information Intelligently to Make Business Decisions: Optimize Collection Activity Prioritize Audit Candidates Supply Education to the Needy! And Repeat (Taxpayer Compliance Management Program!)
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How Do We Use Information Intelligently?
Forecast Performance (models) Appropriate Actions (decision strategies/treatment scenarios) Controlled Experiments (champion/challenger) Performance Reporting
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Actual and Forecast 'Good' Probabilities for Repeat Filers (SC)
0% 20% 40% 60% 80% 100% 130 180 230 280 330 380 430 480 530 Score Range 'Good' Probability 1000 2000 3000 4000 5000 6000 7000 Actual Good Rate Forecast Good Rate N Cases
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BUSINESS - Collections Decision Strategy (VA)
REASON CODE BUSINESS CLASS DISTRICT OFFICE ASSESSMENTS BALANCE RISK (existing) LIEN SOURCE INDICATOR FIELD Accelerated Treatment FLEA No Action High Value, Low Risk (modified) BEGIN 241 Other Moderate High < $100 Yes No > = $100 < $1000 > = $1000 Lien after 60 days, then $100-$1000 send to OCAs $ send to field 86 Low B=241 33% B-FLEA 58% B-NOACT 75% B-LOW 63% B-MOD 61% 62% B-CALL 47% B=FSD$ 45% Filing Frequency Field X B = X 69% =Data elements used to segment accounts = Account groups for strategy implementation Lien after 30 days, then 53,523 576 179 3,914 623 30,242 5,062 2,057 10,288 582
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Treatment Scenarios Allow low-risk cases to self-cure
‘Accelerate’ high-risk cases to enforced collection actions Focus collector resource on medium-risk cases All scenarios end with enforced collection actions
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Low-Risk Treatment Scenarios (SC)
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Medium-Risk Treatment Scenarios (SC)
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High-Risk Treatment Scenarios (SC)
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Treatment Scenarios in MA (Initial Design)
Treatment A NOA Phone Auto - Call & RP NOD FN & Call & Open or Yes Field [ Med . Risk ] Research ( trustee ) RP Deem Auto - Levy Assets ( High Balance ) No Case FR Auto - Levy OCA Assigned Treatment B Yes Assign LIEN [ High Risk ] NOA Phone Auto - NOD NIL Auto - Levy Open or FN Call ( Low Balance ) Research Assets RP Bus . No FR Auto - Levy OCA Treatment C [ High Risk ] Phone Auto - Wage Yes Wage Levy LIEN NOA NOD NIL ( High Balance ) Research Auto - Levy Levy ( Low Balance ) Case Ind . No FR Auto - Levy OCA LIEN Treatment D LIEN Field [ High Risk ] NOA Phone Auto - Call NOD Open or Yes FN ( High Balance ) Research RP - Propose Assets Call Bus . No Deem RP Low FR Auto - Levy OCA Treatment E ( Very High NOA Call Med Balance ) Ind . Assign High Low Treatment F ( Very High NOA Call Med Balance ) Bus . Assign High Day Day Day Day Day Day Day Day Day Day 1 2 14 30 45 61 90 97 105 111
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Champion-Challenger Evaluation
Primary Primary Challenger 1 Challenger 1 90% Grossed Up % Grossed Up $ Available $450 $500 $ $500 $ Collected $ 90 $100 $ $110 Collection % 20% 20% 22% %
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2004 State Tax Revenue . California 85,721 2,388 9 7.2 15 15,127 1
Kentucky 8,463 2,041 21 7.7 1,493 23 Louisiana 8,026 1,777 34 6.8 24 1,416 Colorado 7,051 1,533 48 4.5 49 1,244 25 Alabama 7,018 1,549 46 5.9 42 1,238 26 South Carolina 6,804 1,621 43 6.3 1,201 27 Oklahoma 6,427 1,824 33 6.9 22 1,134 28 Oregon 6,103 1,698 40 6 41 1,077 29 Arkansas 5,581 2,027 8.4 8 985 30 Kansas 5,284 1,931 6.6 932 31 South Dakota 1,063 1,378 4.8 47 188 50 U.S. Total 593,489 2,025 6.5 104,733 State Total Taxes ($ million) Per Capita Rank % of Pers. Income Tax Gap (15%) Tax Gap Source: FTA Web Site :- U.S. Bureau of the Census and Bureau of Economic Anaylsis.
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Probability of Making an Assessment – PA Data
0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0 < 270 315 + Score Range Probability Actual Forecast
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Sort Candidates by Cell and Probability (PA)
cum_ Obs hours cum_yield myrank .
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Collection Action Transition Probabilities (Markov-Chain Analysis)
To FTF Assessment Payment Levy Lien Field Visit Revoke Seize Responsible Cure Notice Plan Party New 0.55 0.35 0.00 FTF Notice 0.00 0.70 0.10 0.20 Notice of Assessment 0.20 Assessment 0.20 0.20 0.05 0.25 0.30 Payment Plan 0.05 0.05 0.20 0.70 Levy 0.60 From Lien 0.70 Field Visit 0.40 Revoke 0.10 Seize 0.50 Responsible Party 0.80
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Probability of Curing by Age of CE and Type of Collection Action
1 3 6 12 18 24 36 48 60 New 0.10 0.00 FTF Notice 0.20 Notice of Assessment 0.05 Assessment 0.30 Payment Plan 0.70 0.80 0.60 0.50 Levy 0.40 Lien 6.00 Field Visit Revoke Seize Responsible Party Action Age (Months)
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TAXPAYER COMPLIANCE MANAGEMENT
Inbound Information Channels Customer/Taxpayer Information Sources Tax Processing External Sources • Customer Contact History Billing History Detailed Return Data Payment History Filing History & Methods Other A/R History Original Registration Data Registration Status Updates Federal Return Data RAR, CP2000 Fed Audits Industry Trend Data SIC Code Standards Other States ’ Tax Credit Bureau Data Other State Agency Taxpayer Interactions Phone Calls Letters Returns E - File Telefile Internet Imaged Payments Electronic Office Visits Case Management Contact Recording Federal Data Sharing Programs External Interfaces RESPONSE TREATMENT TAXPAYER COMPLIANCE MANAGEMENT Decision Information Outbound WEB site Field Visits Faxes Mailings Other? Delivery Systems Integrated Billing & Correspondence Supporting Systems Audit Caseload Collections Caseload Non Filer Caseload Education Caseload Autodialer & Intelligent Call Management Case Management and Workflow Compliance Strategy Management Compliance Strategy Decision Engine Integrated Preventative/Curative Strategies Decision Delivery Decisions Education Strategy Registration Guidance Compliance Initiatives Collections Strategy Filer Strategy Audit Strategy Challenger Strategy Referral Generation Compliance Management Initiatives Reusable Referral Generation Utilities Referrals Under reporters Compliance initiatives Filers Educational needs payers Data Warehouse Acquisition & Cleansing Create/Add to Customer Profile Mining Customer Profile Database Data Aggregation/ Performance Summary Extracts Events Performance Reporting Behavior Modeling Tables Populate/Update Data Access Referral Queue
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