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Collections, Predictive Analytics and Taxpayer Compliance Management

Collections, Predictive Analytics and Taxpayer Compliance Management. John McCalden McCalden Consulting john@mccalden.biz. Agenda. Some Collection Theory Decision Analytics Taxpayer Compliance Management Q & A. 100%. 80%. 60%. Percent. 40%. 20%. 0%. 0. 12. 24. 36. 48. 60.

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Collections, Predictive Analytics and Taxpayer Compliance Management

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  1. Collections, Predictive Analytics and Taxpayer Compliance Management John McCalden McCalden Consulting john@mccalden.biz

  2. Agenda • Some Collection Theory • Decision Analytics • Taxpayer Compliance Management • Q & A

  3. 100% 80% 60% Percent 40% 20% 0% 0 12 24 36 48 60 Age in Months 97 - 99 Percent of Cases Aging per Month (SC)

  4. Percent of Cases Aging per Month (SC) 100% 80% 60% Percent 40% -0.6291 y = 1.0489x 2 R = 0.9988 20% 0% 0 12 24 36 48 60 Age in Months 97 - 99 Power (97 - 99)

  5. Collection Rates,Based on Different Levels of Performance 100% 80% 60% Percent Remaining 40% 20% 0% 0 12 24 36 48 60 Age in Months Forecast: -0.2 Forecast; -0.5 Forecast: -0.6291 Forecast: -0.7 Forecast: -1.0 Forecast: -2.0

  6. Effect of Raising the Level of Performance From -0.6291 to -0.7 100% 80% 60% Percent Remaining 40% -0.6291 y = 1x 2 R = 1 20% -0.7 y = 1x 2 R = 1 0% 0 12 24 36 48 60 Age in Months 97 - 99 Forecast -0.7

  7. Average Balance by Age of Case $2,500 y = 390.49Ln(x) + 529.3 y = 390.49Ln(x) + 529.3 2 R = 0.9334 2 R = 0.9334 $2,000 $1,500 Average Balance ($) $1,000 $500 $0 0 12 24 36 48 60 Months Average Per Case Log. (Average Per Case)

  8. Aging Cases Total $ Month Model 1 Model 1 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 Application of Aging Curve (-0.6291) and Average Balance Curve to a Hypothetical Cohort of 10,000 Cases

  9. 100% 80% 60% Percent Collected 40% 20% 0% 0 12 24 36 48 60 Age in Months % # Collected 1 % $ Collected 1 Percent of # and $ Collected, per Month of Aging

  10. 100% 80% 60% Percent Collected 40% 20% 0% 0 12 24 36 48 60 Age in Months % # Collected 1 % $ Collected 1 % # Collected 2 % $ Collected 2 Comparison of Percent of # and $ Collected, per Month of Aging

  11. 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 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%

  12. How Do We Transition to a Higher Level of Performance? 100% 80% 60% Percent Remaining 40% -0.6291 y = 1x 2 R = 1 20% -0.7 y = 1x 2 R = 1 0% 0 12 24 36 48 60 Age in Months 97 - 99 Forecast -0.7

  13. 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!)

  14. How Do We Use Information Intelligently? • Forecast Performance (models) • Appropriate Actions (decision strategies/treatment scenarios) • Controlled Experiments (champion/challenger) • Performance Reporting

  15. 100% 7000 6000 80% 5000 60% 4000 'Good' Probability 3000 40% 2000 20% 1000 0% 0 130 180 230 280 330 380 430 480 530 Score Range Actual Good Rate Forecast Good Rate N Cases Actual and Forecast 'Good' Probabilities for Repeat Filers (SC)

  16. BUSINESS - Collections Decision Strategy (VA) BUSINESS 62% BEGIN Other BUSINESS Other Filing REASON CLASS 53,523 CODE Frequency CODE BUSINESS (existing) X 582 241 10,288 576 86 Field DISTRICT OFFICE B = X 69% ASSESSMENTS < $100 B=241 33% > = $1000 BALANCE < $1000 High 179 RISK BALANCE Low FLEA Moderate > = $100 B-FLEA 58% 30,242 BALANCE LIEN Yes BUSINESS SOURCE (modified) INDICATOR 3,914 < $100 > = $100 B-MOD 61% 623 No No Action 5,062 2,057 Lien after 30 days, then High Value, $100-$1000 send to OCAs Low Risk B-NOACT 75% $1000 + send to field Accelerated FIELD Treatment B-LOW 63% =Data elements used to segment accounts Lien after 60 days, then B-CALL 47% B=FSD$ 45% = Account groups for strategy implementation $100-$1000 send to OCAs $1000 + send to field

  17. 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

  18. Low-Risk Treatment Scenarios (SC)

  19. Medium-Risk Treatment Scenarios (SC)

  20. High-Risk Treatment Scenarios (SC)

  21. Treatment Scenarios in MA(Initial Design) Treatment A Yes Field Phone Auto - Call & RP FN & Call & Open or NOA NOD Auto - Levy [ Med . Risk ] Research ( trustee ) RP Deem Assets ( High Balance ) No FR Auto - Levy OCA Case Assigned LIEN Treatment B Assign Yes FN Phone Auto - Open or NOA NOD NIL Call [ High Risk ] Auto - Levy Research Assets RP ( Low Balance ) Bus . No FR Auto - Levy OCA Treatment C Wage Levy LIEN Yes [ High Risk ] Phone Auto - Wage NOA NOD NIL Auto - Levy ( High Balance ) Research Levy ( Low Balance ) Case No Ind . FR Auto - Levy OCA LIEN Treatment D LIEN Field Phone Auto - Call Open or NOA NOD [ High Risk ] Yes FN Research RP - Propose Assets ( High Balance ) Call Bus . Deem RP No Low FR Auto - Levy OCA Treatment E NOA ( Very High Call Med Balance ) Ind . Assign High Low Treatment F NOA ( Very High 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

  22. Champion-Challenger Evaluation Primary Primary Challenger 1 Challenger 1 90% Grossed Up 10% Grossed Up $ Available $450 $500 $50 $500 $ Collected $ 90 $100 $11 $110 Collection % 20% 20% 22% 22%

  23. Total % of Taxes Pers. Tax Gap (15%) Tax Gap State ($ million) Per Capita Rank Income Rank ($ million) Rank 15,127 California 85,721 2,388 9 7.2 15 1 . . . . . . 1,493 23 Kentucky 8,463 2,041 21 7.7 9 1,416 24 Louisiana 8,026 1,777 34 6.8 24 1,244 25 Colorado 7,051 1,533 48 4.5 49 1,238 26 Alabama 7,018 1,549 46 5.9 42 1,201 27 South Carolina 6,804 1,621 43 6.3 34 1,134 28 Oklahoma 6,427 1,824 33 6.9 22 1,077 29 Oregon 6,103 1,698 40 6 41 985 30 Arkansas 5,581 2,027 23 8.4 8 932 31 Kansas 5,284 1,931 29 6.6 29 . . . . . . 188 50 South Dakota 1,063 1,378 49 4.8 47 104,733 U.S. Total 593,489 2,025 6.5 2004 State Tax Revenue

  24. 100.0 90.0 80.0 70.0 60.0 Probability 50.0 40.0 30.0 20.0 10.0 0.0 315 + < 270 290 - 294 305 - 309 310 - 314 270 - 274 295 - 299 280 - 284 300 - 304 275 - 279 285 - 289 Score Range Probability of Making an Assessment – PA Data Actual Forecast

  25. Sort Candidates by Cell and Probability (PA) cum_ Obs hours cum_yield myrank . . . 3507 676851 196963641 3507 3508 677044 197019804 3508 3509 677237 197075967 3509 3510 677430 197132130 3510 3511 677623 197188293 3511 3512 677816 197244456 3512 3513 678009 197300619 3513 3514 678202 197356782 3514 3515 678395 197412945 3515 . . .

  26. 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

  27. Age (Months) Action 1 3 6 12 18 24 36 48 60 New 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 FTF Notice 0.20 0.10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Notice of Assessment 0.10 0.10 0.05 0.00 0.00 0.00 0.00 0.00 0.00 Assessment 0.30 0.20 0.10 0.00 0.00 0.00 0.00 0.00 0.00 Payment Plan 0.70 0.80 0.70 0.60 0.50 0.30 0.20 0.10 0.10 Levy 0.60 0.70 0.70 0.60 0.60 0.40 0.40 0.30 0.20 Lien 0.10 0.60 6.00 0.60 0.50 0.40 0.30 0.30 0.30 Field Visit 0.40 0.40 0.50 0.50 0.50 0.30 0.30 0.20 0.20 Revoke 0.10 0.10 0.10 0.10 0.00 0.00 0.00 0.00 0.00 Seize 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70 Responsible Party 0.80 0.80 0.80 0.80 0.80 0.80 0.80 0.80 0.80 Probability of Curing by Age of CE and Type of Collection Action

  28. TAXPAYER COMPLIANCE MANAGEMENT TAXPAYER COMPLIANCE MANAGEMENT Inbound Inbound Customer/Taxpayer Customer/Taxpayer Compliance Compliance Compliance Compliance Compliance Compliance Customer Contact Customer Contact Outbound Outbound Information Information Information Sources Information Sources Data Warehouse Data Warehouse Referral Generation Referral Generation Strategy Management Strategy Management Delivery Systems Delivery Systems Information Information Channels Channels Channels Channels Compliance Strategy Compliance Strategy Case Management Case Management Compliance Management Compliance Management Tax Processing Tax Processing Acquisition & Cleansing Acquisition & Cleansing Decision Engine Decision Engine and Workflow and Workflow Initiatives Initiatives • • Phone Calls Phone Calls • • Phone Calls Phone Calls • • Letters Letters • • Letters Letters • • Customer Contact Customer Contact Integrated Integrated • • Returns Returns • • Office Visits Office Visits Audit Caseload Audit Caseload History History Populate/Update Populate/Update Populate/Update Preventative/Curative Preventative/Curative Reusable Referral Reusable Referral • • E E - - File File • • WEB site WEB site • • Billing History Billing History Strategies Strategies Generation Utilities Generation Utilities • • Telefile Telefile • • Email Email • • Detailed Return Data Detailed Return Data • • Internet Internet • • Field Visits Field Visits • • Payment History Payment History • • Imaged Imaged • • Faxes Faxes Create/Add to Create/Add to • • Filing History & Filing History & • • Payments Payments • • Mailings Mailings Under Under - - reporters reporters Non Non - - Filer Caseload Filer Caseload Customer Profile Customer Profile Methods Methods • • Electronic Electronic • • Other? Other? • • Other A/R History Other A/R History • • Internet Internet • • Original Registration Original Registration • • Imaged Imaged Data Data Non Non - - Filers Filers • • Office Visits Office Visits Data Data • • Registration Status Registration Status • • Case Case Education Caseload Education Caseload Mining Mining Updates Updates Management Management Non Non Non Contact Contact - - - Data Access Data Access Educational needs Educational needs Recording Recording Customer Customer External Sources External Sources Audit Strategy Audit Strategy Audit Strategy Compliance Initiatives Compliance Initiatives Compliance Initiatives Collections Strategy Collections Strategy Collections Strategy Challenger Strategy Challenger Strategy Challenger Strategy Education Strategy Education Strategy Education Strategy Registration Guidance Registration Guidance Registration Guidance • • Email Email Profile Profile Filer Strategy Filer Strategy Filer Strategy Behavior Behavior • • Federal Data Federal Data Collections Caseload Collections Caseload Database Database Modeling Modeling • • Federal Return Data Federal Return Data Sharing Sharing Compliance initiatives Compliance initiatives • • RAR, CP2000 Fed RAR, CP2000 Fed Programs Programs Audits Audits • • External External Extracts Extracts • • Industry Trend Data Industry Trend Data Interfaces Interfaces Events Events • • SIC Code SIC Code Non Non - - payers payers Supporting Systems Supporting Systems Standards Standards • • Other States Other States ’ ’ Tax Tax Data Aggregation/ Data Aggregation/ Data Data Integrated Billing & Integrated Billing & Referrals Referrals Performance Summary Performance Summary • • Credit Bureau Data Credit Bureau Data Correspondence Correspondence • • Other State Agency Other State Agency Data Data Decisions Decisions Autodialer Autodialer & Intelligent & Intelligent Performance Performance Performance Performance Compliance Compliance Call Management Call Management Reporting Reporting Tables Tables Referral Referral Decision Delivery Decision Delivery Queue Queue Decision Information Decision Information RESPONSE RESPONSE TREATMENT TREATMENT Taxpayer Taxpayer Interactions Interactions

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