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Days Traveled

Create a field called DATE_DIFF using the equation of

@Age(END_DATE, START_DATE)

There are 78 records with DATE_DIFF. The ones with differences of more than 60 suggest that those records contain claims where the employee traveled over 60 days. These may have errors in either the START_DATE or END_DATE, or have errors in both. It may be possible that several claims over a spread of time were combined into one claim. A review of the source documents and receipts can easily clear this up.

Various analyses can be done using the DATE_DIFF field. Using the criteria of DATE_DIFF = = 0, 972 records were obtained, which means that 972 of the 2,800 claims/records were for travel that started and ended on the same day. Conversely, 1,828 of the records showed travels of 1 day or more.

Same Day Traveled with Accommodation Charges

To obtain transactions where there are accommodation costs and both the start and end of the travel claim are on the same date, we extract the records using this formula:

DATE_DIFF = = 0 .AND. ACCOMMODATION > 0 There were 124 records obtained as shown in Figure 11.4.

Same Day Traveled with Flight Charges

To obtain transactions where there are airfare costs and both the start and end of the travel claim are on the same date, we extract the records using this formula:

DATE_DIFF = = 0 .AND. AIR_FARE > 0

Results of Accommodations with No Overnight Travel

FIGURE 11.4 Results of Accommodations with No Overnight Travel

There were 311 records obtained.

Same Day Traveled with Both Flight and Accommodation Charges

W here the start and end dates are on the same day that contains airfare amounts and accommodation costs, we extract the matching records with this formula:

DATE_DIFF = 0 .AND. AIR_FARE > 0 .AND. ACCOMMODATION > 0

There are 273 records outputted.

Traveled Overnight with Both Flight and Accommodation Charges

W here the start and end dates are not on the same day that contains airfare amounts and accommodation costs, we extract the matching records with this formula:

DATE_DIFF > 0 .AND. AIR_FARE > 0 .AND. ACCOMMODATION > 0

There are 116 records outputted.

Traveled Overnight with Flight but No Accommodation Charges

From the travel expense data set, we can look for travel claims that were for more than the same day with an airfare expense but not accommodations.

Overnight Travel with Flight without Accommodations

FIGURE 11.5 Overnight Travel with Flight without Accommodations

Using this formula, 116 records matched, as displayed in Figure 11.5.

DATE_DIFF > 0 .AND. AIR_FARE > 0 .AND. ACCOMMODATION = 0

Top-10 Flight Charges with No Accommodation Charge and Traveled Overnight

Top-10 Airfare Amounts

FIGURE 11.6 Top-10 Airfare Amounts

Here are examples of the top-10 highest airfare claims where there are no accommodations and the employees did not return home on the same day. From the "Travel Expenses" database, we extract to a new file using the equation of DATE_DIFF > 0 .AND. AIR_FARE > 0 .AND. ACCOMMODATION=0. We then index on the AIR_FARE field by descending order for us to be able to review the top-10 or any of the 116 resulting records, as shown in Figure 11.6.

Another method to arrive at the top-10 flight costs that match our criteria is to use the results from Figure 11.5 , apply the Top Records Extraction option of IDEA to them, and input 10 as the number of records to extract for the AIR_FARE field. This illustrates, again, that there are many ways to arrive at the same audit objectives within IDEA. It is a matter of personal choice of which methods and which equations are preferred.

Some of these resulting records need to be audited by going to the expense report and the attached receipts to answer the anomalies. It is up to the auditor's professional judgment as to which records to examine based on cut-off amounts, sampling, or even intuition.

 
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