Consumer Taxation Program Branch

IFTA Fraud

Part 1 - Administrative Focus

September 22, 2005

Introduction

• Purpose– Examine potential tools to identify carriers for

audit:• Data Mining - Review quarterly return information

for anomalies, trends and variances • Sighting Reports - Compare quarterly return

information to vehicle sightings

• Overall Objective– Improved compliance and audit recovery

Background

• 3% Audit requirement (A310)– About 66% of jurisdictions meet this

requirement

• High cost to audit – Average 45 hours per audit

• Low Recovery per Audit– Average $50 per audit hour

Data Mining

• Generally jurisdictions with a formal audit selection process have:– Higher recoveries (i.e., $60 vs. $20 per audit

hour);

– Higher percentage of assessments/credits per audit (i.e., 100% vs. 50%)

Data Mining• Selection Criteria

– Fuel purchase and consumption trends • Abnormally high or low fuel consumption in a quarter

(e.g., less than 1 or greater than 7 mpg)

• Consistent consumption between quarters • Average consumption by decal• Percent change by quarter or year • Fuel purchased by jurisdictions

– Tax Amounts• Always in a Refund Position• Always netting to zero

Data Mining• Selection Criteria (continued)

– Distances travelled• High distances per vehicle/decal (e.g., 125,000 miles per quarter)

• Multiple quarters no out-of-jurisdiction travel • Missing jurisdictions (e.g., BC, WA, CA but no OR) • Percent change/growth by quarter or year

– Leads from field inspectors/enforcement• Sighting Reports• Parking tickets and other violations

Data Mining• Selection Criteria (continued)

– Carriers in receivership or experiencing financial difficulties

– Registration/Renewal Information• Matching new carriers with existing problem carriers

– Dual fuel users • Bulk fuel storage• Access to coloured fuel

Data Mining• Selection Criteria (continued)

– Historical Information (frequency of):• Math errors and amended returns• Late returns and renewals• Suspensions/revocations • Past audit results

– Numerical Data • Consistent numbers (e.g., 123, 124, 123)• Rounding numbers (e.g., 10,000 miles) • The number of vehicles/decals issued• IRP & IFTA numerical differences

Data Mining• Notes

– Anomalies don’t guarantee a problem only things that might be worth a “closer” look

– Don’t pick the same criteria as everyone else:• Variety is good• Different factors and weights for different

jurisdictions

– Move selection criteria around• Law of diminishing returns

Data Mining• Notes (continued)

– Build filters/sort functions because of the volume of data.

• Distances travelled by range (e.g., 0-9,999; 10,000-99,999; 100,000-

999,000)

• Fuel Consumption by range (e.g., 5.0-6.9mpg, 7.0-8.9mpg)

• Fuel consumption by vehicle/decal issued – A formal selection process should never replace:

• The gut feeling/hunches from processing staff• Input from others (e.g., Industry Associations, Anonymous Tips, Road side

enforcement (safety and weigh scales, and Sighting reports)

Data Mining

Average KPL 1999 2000 2001 2002 2003 2004 All Filings Carrier 1 1.31 1.38 1.35 1.35 0.69 1.27Carrier 2 4.22 4.69 3.6 2.87 4.31 3.75Carrier 3 1.84 0.46 0.97Carrier 4 1.81 1.75 1.95 2.08 1.67 NA 1.8Carrier 5 2.67 2.04 2.18 1.39 0.49 0.53 1.98Carrier 6 1.58 1.84 1.68 1.77 1.55 1.23 1.51Carrier 7 1.44 2.24 1.94 2.01 1.09 4.99 1.85Carrier 8 3.96 3.08 2.36 1.42 2.13 2.29Carrier 9 2.99 3.34 6.36 3.8Carrier 10 1.96 1.85 1.89 1.48 2.42 1.73Carrier 11 4.19 3.91 3.88 4.03 3.77 3.53 3.97Carrier 12 3.04 1.95 5.42 2.82Carrier 13 2.25 2.26 2.22 0.47 2.45 1.98All Taxpayers 2.21 2.22 2.33 2.23 2.20 2.23 2.21

• Example - Annual Fuel Consumption

Data Mining

Avg KPL No Of Decals Ltrs / Decal Distance / DecalCarrier 1 3.96 4 308,547 1,207,042 Carrier 2 1.89 1 358,637 690,725 Carrier 3 1.78 4 249,800 596,279 Carrier 4 1.84 61 272,544 499,781 Carrier 5 2.46 1 186,701 465,080 Carrier 6 2.79 2 165,572 420,665 Carrier 7 1.96 1 212,098 415,665 Carrier 8 2.8 1 139,193 405,168 Carrier 9 2.52 24 129,982 336,414 Carrier 10 2.17 6 140,733 314,270 Carrier 11 2.12 1 140,745 304,299 All Taxpayers 2.2 12,425 44,744 94,496

• Example: Distance Travelled per Decal

Data Mining

• Conclusion– Improved compliance and audit recovery

– Level playing field between businesses

– Reduced impact to carriers by catching errors earlier

– Generally higher recoveries (i.e., $60 vs. $20 per audit hour);

Sighting Reports• Objective

– To test the effectiveness of IFTA Sighting Reports as a compliance tool

• Summary – 11 participating jurisdictions– Road side enforcement fax at least one sighting

report to each of the other ten participating jurisdictions

– Low profile– Sighting reports compared to carrier’s returns

Sighting ReportJurisdiction Carriers Sighted No Return % Not Reported %

(AL) Alabama 30 5 35.71% 0.00%

(BC) British Columbia 18 1 7.14% 1 0.31%

(CA) California 44 0.00% 1 0.31%

(FL) Florida 49 0.00% 4 1.23%

(KS) Kansas 21 1 7.14% 0.00%

(KY) Kentucky 21 2 14.29% 0.00%

(MB) Manitoba 32 0.00% 0.00%

(MS) Mississippi 10 0.00% 0.00%

(ON) Ontario 36 3 21.43% 2 0.62%

(QC) Quebec 36 2 14.29% 2 0.62%

(UT) Utah 27 0.00% 0.00%

TOTALS 324 14 4.32% 10 3.09%

Sighting Reports

• Results– 3.09% of test population equals 8,378 potential

non-compliant carriers (based on 2004 population of 271,146 accounts)

– Estimated revenue impact unknown until audit results obtained (FL did some analysis and calculated a minimum loss of $593)

• Potential variables– Non-reporting carriers– Level of profile seen by carriers when sighting

report information recorded– Sighting errors

Sighting Reports

• Conclusion– To early for a definitive answer, need:

• Audit results• Non-reporting carriers• Larger sample population (0.119% is too small)

– Continue the experiment.

Open Discussion

Questions/Comments