The Nature of Fraud

In addition as companies have given it to the pressures to meet Wall Streets earnings  expectation and as these pressures to meet the numbers have intensified, some very large financial statement frauds have been committed. Hundred million of even billion dollar frauds are not unusual and in some cases the decline in market value of the company`s stock has been in the billions of dollars.

To understand how costly fraud is to organization consider  what happens when fraud is committed against a company.  Losses incurred from fraud reduce a firm`s income on a dollar for dollar basis. This means that for every $1 of fraud net income is reduced by $1. Since fraud reduces net income it takes significantly more revenue to recover  the effect of the fraud  on net income. Illustrate consider  the $436  million fraud loss that a U.S automobile manufacturer experienced a few years ago. If the automobile manufacturers profit margin net income divided by revenues at the time was 10 percent, the company would have to generate up to $4.36 billion  in additional revenue or 10 times the amount of the fraud to restore the effect on net income.  If we assume and average selling price of $20, 000 per car, the company must take and sell an additional 218 000 cars. Considered this way, fighting fraud is a serious business. The automobile company can spend its efforts manufacturing and marketing additional new cars or trying or reduce fraud, or a combination of both.

As another example, a large bank was the victim of several frauds that totaled approximately $100 million in one year. With a profit  margin of 5 percent, and assuming that the bank made 100% per checking account one year, how many  new checking accounts must the bank generate  to compensate for the fraud  losses? The answer, of course, it up to 20 million new checking account = 20 million new accounts).

Firms are not the only victims of fraud. In the aggregate, national  economies also pick up the tab. Continuing the logic of the automobile fraud described above, consider a fictitious economy comprised of only three firms. If Economy A, whose profit margin is 10 percent, losses $500 million to fraud, it must generate $5 billion of additional revenue to offset the loss to net income. If Economy  B, whose profit margin is 5 percent losses $200 million to fraud it, must generate $2 billion. Finally, If Economy C, whose profit margin is 5 percent losses $100 million to fraud, it must also generate $2 billion. In all, an economy hit by $2 billion. In all, an economy hit by $800 million of fraud must create $8 billion of additional revenue to recover the loss to aggregate income. The strain fraud imposes on the economy is tremendous. If just one fraud is prevented billions of dollars of resources are saved; resources that can be invested in building the economy. Given this analysis it is easy to see how difficult it is for countries with high amounts of corruption and fraud to ever compete with countries with low rates  of corruption and fraud. High corruption countries are constantly trying to catch up for the corruption and frauds that have been committed, while low-corruption countries are growing and moving ahead. Economists ,lawmakers  and regulators can spend their efforts enhancing business and trade or trying to reduce fraud, all to the same end: growing their economies.

Now assume that the GDP in the fictitious economy described above was $20 billion in the year prior to the frauds (fraud 1). If that economy were growing at 5 percent its GDP in year 2would have been $21 billion but since the frauds reduce aggregate income by $800 million. The GDP in year 2 is only $ 20.2 billion, and the economy has grown by only 1 percent. If no frauds were committed in year 2 (and GDP had reached $21billion), assuming a rate of growth of 5 percent in year 3, GDP in year 3 would have been $22.1 billion. However, reducing the GDO by the amount of the fraud committed  in year 2 before increasing it by 5 percent means that the economy only  reaches a GDP of $21.2 billion in year 3 (setting it back by almost a full year`s growth).