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CASH LARCENY

Cash larceny can include both cash and other negotiable items such as checks. It can take place at the point of sales, during the bank deposit process, or during incoming payments for accounts receivable. Since the revenues are recorded on the books already, it takes additional efforts to cover up any significant amounts of larceny.

Small amounts taken from the cash register till are normally accepted by the organization and written off as cash shortages. This occurs in any type of organization that transacts in cash. There will always be some errors where the customers paid too much or too little by mistake. Customers may not know that they were given too much change or undercharged, but even if they are aware, they may not complain when it is in their favor.

Other forms of covering up may be taking the cash from someone else's cash register or reversing recorded sales as voids or refunds.

Proceeds from a sale can be retained by the employee if the employee can debit the accounts receivable of a fictitious account or another real account that is due to be written off as a bad debt. If the employee can have the equivalent amount taken offset by recording discounts of the same total amount, the books remain balanced. In cases where receipts for cash payments are issued but no receipts are issued for payment by checks, checks can be substituted for cash taken and the receipts register would still reconcile to the total.

CASE STUDY

We will use the sample sales files included with the IDEA software. There are sales for two separate years. The current year sales file is called "Sample—Detailed Sales" with 2011 transactions and the other is called "Sample—Detailed Previous Year" and contains transactions for 2010.

The files each have 10 fields, including an invoice date field, as shown in Figure 7.1. We may be performing analysis based on the month of the sale, so we append an eleventh field called MONTH, using the function and equation of @Month(INV_DATE) to isolate the month from within the invoice date i eld.

Fields for Both the 2011 and 2010 Sample Detailed Sales Files

FIGURE 7.1 Fields for Both the 2011 and 2010 Sample Detailed Sales Files

A review of the field statistics for both years shows that there was only one negative sales amount in 2011 and no negative amounts in 2010. Also of interest was that in 2010, there were significant sales being done on the weekend and less so in 2011.

We can summarize each of the detailed sales files using various keys that will provide us with additional information. First we can summarize by the sale representative and total on the sales before taxes. We can then apply the Z-score, as discussed in Chapter 5, to see how far the total sales for each sales representative is away from the mean for each of the two years.

Summarized by Sales Representatives' Totals on Sales before Taxes with Z-Scores

FIGURE 7.2 Summarized by Sales Representatives' Totals on Sales before Taxes with Z-Scores

Having set the view to vertically display the two files with the 2010 summary on the left side and the 2011 summary on the right side, as in Figure 7.2, we can see that, in2011, ten additional sales representatives were hired. They were assigned the numbers of 119 to 128.

For 2010, the Z-scores on sales before taxes for each sales representative do not have exceptionally positive or negative swings away from the mean or center of the amounts. The sales representatives with high negative Z-scores can be examined for potential skimming and those with high positive Z-scores should be examined for potential commission fraud as discussed in Chapter 10. For 2011, sales representatives number 108 and 118 each have a high Z-score above 3.00, which is unexpected. These Z-scores were calculated on sales. To detect potential skimming or larceny, Z-scores should also be performed on refunds, price adjustments, voids, and returns where the information is available.

The two summarized files can be combined by using the Join feature of IDEA.

Preparing to Join the 2010 and 2011 Summarized Files by Sales Representatives

FIGURE 7.3 Preparing to Join the 2010 and 2011 Summarized Files by Sales Representatives

The primary file selected is the 2011 file with the 2010 file as the secondary file. For the secondary file, we will select all fields to bring in with the exception of the SALESREP_NO field as shown in Figure 7.3. IDEA will add a number 1 to the field name from the secondary file if it encounters an identical field name in the primary file. The fields can be renamed after the join to something more meaningful. We will match on the SALESREP_NO field.

Results of the Join

FIGURE 7.4 Results of the Join

We had renamed the fields to include the year for ease of identification as displayed in Figure 7.4. By using the chart feature, a visual comparison would give us better insights.

Chart Comparing Sale Representative Sales for 2010 and 2011

FIGURE 7.5 Chart Comparing Sale Representative Sales for 2010 and 2011

Sales differences between the two years can be easily compared. It is obvious from Figure 7.5 that the two most significant changes were for sales representative 108 and 118. We can also see the less-startling spread between the two years of the other sales representatives. Some sales representatives may need to be reviewed in more detail as to why the change in increased or decreased sales.

Another chart of interest would be the number of records or transactions comparison of each sales representative for the two years.

Comparison of the Number of Sales by Sales Representatives for 2010 and 2011

FIGURE 7.6 Comparison of the Number of Sales by Sales Representatives for 2010 and 2011

Sales representative 108 did not have a significant increase in the number of sales, whereas sales representative 118 not only had an improvement in the number of sales but also in the total amount of sales, as seen in Figure 7.6. If desired, you can create two new fields that calculate the increases or decreases between the two years. One can be done for the number of records and the other on the sales amounts.

Since we had created a month field in the detailed sales database, we can create a new file by first summarizing on the SALESREP_NO field and then by the MONTH field totaling on the SALES_BEF_TAX field. The results can be viewed using IDEA's Pivot Table feature.

Pivot Table of Sales by Month for Each Sales Representative

FIGURE 7.7 Pivot Table of Sales by Month for Each Sales Representative

Figure 7.7 allows us to see how much each sales representative sold month to month. It is also simple to compare sales representatives. The information can be filtered by sales representatives so only those of interest are displayed. By selecting the dropdown box headed as SALESRE at the upper left corner in Figure 7.7, you can uncheck those that you do not wish to be displayed. Similarly, from the MONTH dropdown box, you can select only those months you wish displayed.

Another view to explore would be by sales representatives as compared to products. First we summarize the 2011 file by the SALESREP_NO field and then by the PROD_ CODE field totaling on the SALES_BEF_TAX field. Once completed, use the pivot table to view by product numbers as in Figure 7.8.

Products Sold by Each Sales Representative for 2011

FIGURE 7.8 Products Sold by Each Sales Representative for 2011

This pivot table view allows us to see that all representatives sold product number 05 but only a few sold product number 06 and product number 01. Again, sales representatives 108 and 118 stand out but this time we know that it is because of selling product 06. It is also an anomaly that only one other sales representative, number 104, had made sales of product 06 but at significantly lower totals than the other two representatives. This could be a red flag of larceny or possibly the other two representatives are involved in a commission scheme.

More analysis is needed and a good place to start would be to use the same pivot table for the 2010 years as a comparison, displayed in Figure 7.9.

Comparing the 2010 sales to the 2011 sales, product numbers 01, 02, and 03 dropped in 2011 while product numbers 04, 05, and 06 increased. Commission rates for each product should be examined to ensure that a sales representative maximizing their commissions was not detrimental to the organization sales strategy. We also note

Products Sold by Each Sales Representative for 2010

FIGURE 7.9 Products Sold by Each Sales Representative for 2010

that in 2010, only sales representative 108 sold product number 06 as opposed to two additional representatives selling the same product in 2011.

Might the increased sales for product 06 or any product be a result of heavy price discounting? Recall that one way to cover up larceny is to offset money taken by recording equivalent discounts. On the assumption that the UNIT_PRICE field is net of discounts, an easy way of determining whether any items are sold below regular prices or discounts given would be to summarize the 2011 detailed sales file by PROD_CODE. Select the UNIT_PRICE_SUM field to be totaled and averaged. It is optional to select the SALES_BEF_TAX to be totaled and averaged.

If the UNIT_PRICE_AVERAGE field amount for a product is the same as the unit prices visually tested in the detailed sales file, then you know that there were no sales for that product at below the regular sales price. An example is shown in Figure 7.10.

Results of Summarizing by Product, Totaling, and Averaging on the Unit Price for 2011

FIGURE 7.10 Results of Summarizing by Product, Totaling, and Averaging on the Unit Price for 2011

A quick examination assures you that no sales representative sold any products at other than the listed prices. Similarly, an analysis for 2010 determined the same results. It is only noted that there were price increases for all products in 2011. Increased prices are a good example of why not only dollar values should be included in any analysis, but also the number of transactions should be included, as in Figure 7.6.

There are many other tests that can be performed that may produce unusual results that can be investigated, such as:

- Write-offs of accounts receivable that are inappropriate, either due to frequency, amounts, or contravening write-off policies of the organization.

- Extract partial payments that reduce overdue customer accounts receivable that may be booked to avoid attention resulting from routine aging analysis of accounts receivable.

- Review accounts receivable in a credit balance position. Any unusual results should be traced to the postings to ensure that they were proper, as they may be associated with larceny.

- Apply trend analysis to those fulltime employees with low sales and high discounts.

- For service employees, analyze low hours booked, as this may be an indication of working and charging the customers directly. At the very least, inefficiencies in the operations would be revealed.

- Compare selling prices, net of discounts and adjustments, by different sales locations. Correlation can simplify the comparison process.

The best tests are those that employ other databases, especially those from other business systems. Examples of independent databases that can be compared to accounting system data follow.

- Compare sales returns and other adjustments, such as voids, to the inventory database. Periodic verification of physical inventory needs to be done in order for this test to be effective.

- Match access logs to the accounts receivable module of the accounting system to employees who are in sales.

- Extract computer or access-card logs for nonbusiness hour access to the sales location or to the accounting system.

- Compare the POS or cash register system information to cash-receipt reports. Reconciliation will show any discrepancies and high numbers of small differences are suspicious.

- Compare purchases made by debit and credit cards and refunds. Extract and review transactions where customers made the purchase on one card but had it refunded to different debit or credit cards.

- Compare sales prices to the cost of goods in the inventory system and extract out items sold at below cost that are not considered obsolete. A percentage below expected gross margins of products may also be used.

CONCLUSION

Every organization that accepts cash payments is susceptible to skimming and cash larceny. Cash is the most vulnerable to fraud as it is the most liquid of all assets.

Employees may handle large sums of cash, which makes it tempting for a fraudster to retain some of it for personal use. Misappropriation of any other asset, such as inventory, requires that the goods be converted to cash before the fraudster can enjoy the results. Conversion involves extra steps, which means extra risks for the fraudster.

With technology these days, skimming can occur faster and result in larger losses. It is no longer as simple as an apartment manager not recording that an apartment has been rented out and keeping the rent money collected. Some of today's fraudsters have a high degree of IT skills that help them to perpetrate fraud or in covering up fraudulent transactions. There are a number of cases where managers and managing shareholders would use software programs called zappers to delete sales so they can defraud their employer or silent partners/shareholders along with the taxation authorities. Since the sales are only temporarily recorded in the point-of-sales system and deleted before being entered into the accounting system, this type of fraud can be considered as skimming. Further discussion of zapper fraud is in Chapter 16.

 
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