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IFTA Fraud Part 1 - Administrative Focus

IFTA Fraud Part 1 - Administrative Focus. Consumer Taxation Program Branch. September 22, 2005. Introduction. Purpose Examine potential tools to identify carriers for audit: Data Mining - Review quarterly return information for anomalies, trends and variances

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IFTA Fraud Part 1 - Administrative Focus

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  1. IFTA Fraud Part 1 - Administrative Focus Consumer Taxation Program Branch September 22, 2005

  2. 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

  3. 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

  4. 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%)

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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)

  11. Data Mining • Example - Annual Fuel Consumption

  12. Data Mining • Example: Distance Travelled per Decal

  13. 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);

  14. 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

  15. Sighting Report

  16. 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

  17. 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.

  18. Open Discussion Questions/Comments

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