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In dealing with growing volumes of transactional data, auditors depend on sample auditing to determine the vulnerabilities in the system. But while selecting the target for sample auditing, biases can creep in and make the screening process ineffective.
Digit Distribution Analysis (DDA), based on Benford’s law of digits, is an effective algorithm that is independent of scale and context, and does not depend on past patterns of anomalies to identify new ones. DDA is already a well-trusted tool in accounting forensic analytics. This paper explores the effectiveness of DDA in finance and accounting auditing. Some highlights of DDA are as follows:
It functions on the premise that transaction data that has not been altered or manipulated in any way will conform to Benford’s distribution of digits
Any skew or overrepresentation of digits highlights the potential for malpractice or idiosyncratic clustering of data, and offers a scientific basis for audit sample selection
It functions as an effective screening tool, offers 100 percent transaction coverage and overrides biases in identifying targets for sample auditing