![Today’s Presentation ,[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
Recomendados
Mais conteúdo relacionado
Destaque
Destaque (20)
Semelhante a Audit,fraud detection Using Picalo
Semelhante a Audit,fraud detection Using Picalo (20)
Último
Último (20)
Audit,fraud detection Using Picalo
- 1. Detectlets for Better Fraud Detection Conan C. Albrecht, PhD Marriott School of Management Brigham Young University
- 10. Examples of Data-Based Detection
- 12. The Family Business Work Orders Authorized By Purchaser
- 13. The Family Business Invoice Charges Authorized By Purchaser
- 14. The Family Business Work Orders Given To Contractor Crew
- 16. Systematic Increases In Spending
- 17. Systematic Increases In Spending
- 18. Unexpected Peaks In Spending
- 19. Increases In Only Part Of A Trend
- 20. Caught by his Pool…
- 27. Isn’t there a better way? Reasonable time requirements Within reach of most auditors (highly technical skills not required) Cost effective Integrate easily into different database schemas Integrate AI and auto-detection
- 32. Picalo: The Supporting Platform
- 37. Picalo Level 1 API
Notas do Editor
- One search summed all hours worked by employees within two week periods. It ignored which project it was on, which plant it was at, what type of work it was, etc. We found people that were working over 100 hours per week. This could perceivably happen once or twice, but many workers did this consistently, month after month (as seen in the trend above). Investigation into these employees showed that they were clocking in under two time cards at different locations in the plant, effectively doubling their hours each week.
- The next few slides show the results of a specialized search. We stratefied the data by the amount of work orders that purchasers authorized during each period. As can be seen, purchaser F authorized considerably more work than other purchasers.
- Purchaser F is again shown in this spreadsheet, which is now stratefied by invoice charges. Again, he is authorizing considerably more charges.
- The picture became clearer as we stratefied by contractor crew. The company subcontracted with third-party companies for this type of work, and it is obvious which crew is getting the majority of the work. See the totals across the bottom.
- When we investigated these people on both sides of the transaction, the same last name was found on each side. The individuals came from the same immediate family, and the purchaser was funneling work to his family’s company.
- These next few slides show some sample data patterns that researchers can look for. They are not all-inclusive, but are just examples of what to look for and one way to visualize it. The above time engine results show employee (with names grayed out) trends in spending. The shown trend is increasing regularly.
- This slide shows another increase in spending. Note how the time engine flags the suspicious data points in red.
- This slide shows an unexpected peak in spending. The employee had normal spending until one month where he or she spent significantly more than expected. It is important to understand why this occurred.
- This data pattern illustrates how subtrends need to be analyzed. A simple average (or regression equation) of this trend would be very normal. However, a problem trend is flagged when only the first five data points are considered. The time engine ran repeated analyses on all parts of a trend.