1. Consumer Taxation Program Branch IFTA Fraud Part 1 - Administrative Focus 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