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CHAPTER FIVE. Data Analytical Tests

DATA ANALYSIS USES TECHNOLOGY to detect anomalies, patterns, and risk indicators within the data set. It can be used to establish a hypothesis or to quantify detected issues if the hypothesis was found to correctly identify fraud.

The true power of data analytics is that the entire data set of the transactions can be tested. Unlike sampling where only a part of the population is tested, data analytics can test 100 percent of the transactions. Resulting anomalies can then all be reviewed or, if in large quantities, sampled.

While the analysis can provide a list of anomalies, it is not a list of fraudulent transactions. Unlike statistical sampling, there is no mathematical formula that provides the auditor with a listing of frauds.

The auditor needs to apply professional judgment, employ analytical skills, and use intuition. Typically, the auditor reviews the list of anomalies, audits some of the transactions, revises the hypothesis, adjusts the test, and performs additional analytical procedures to reii ne the list to reduce false-positive transactions. There will be numerous false positives of true data anomalies that are not fraud. This is a product of data analytics.

The circular process may continue several times. When completed, the test identifies transactions with a high risk of fraud. This manageable number of transactions can then be examined using fraud-audit procedures. Once a single fraudulent transaction is detected, the audit plan should be revised to expand the review and investigation.

Once the auditor is familiar with the data, the business systems, and the business environment, a number of general analytical tests can be applied against the entire data set or to a specific category or class of transactions.

General data analytical tests are those that can and should be applied against the entire data set to provide the auditor or investigator with a starting point for further audit or review. The general tests output transactions that are outliers, anomalies, or suspicious items. The tests can go through 100 percent of the transactions looking for the defined anomalies. It reduces potentially millions of transactions to a reasonable number to review. The tests may also reveal patterns of interest about what should not be there or should be there. General tests show the power of data analytics. It allows the auditor to perform much more than sampling and allows them to test hypothesis and potential fraud scenarios. Being able to examine transactions at the source level assures the auditor of the integrity of the information. It allows the auditor to obtain insights to potential indicators of fraud and to the effectiveness of internal controls. Often, small anomalies are missed but it is these small anomalies that indicate weaknesses in internal controls that can be exploited. Data analytics of transactional data is a proactive approach in detecting fraud.

 
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