Recent high profile accounting scandals have revealed the need for automated methods which can quickly analyze large amounts of financial data and signal when unusual observations are present. The literature suggests that many financial (and other) data sets conform to the first digit frequency distribution known as Benford's Law. In this paper, various methods of testing whether observed frequencies of first significant digits agree with Benford's law are presented and compared in terms of their power. Theoretical and empirical results are used to compare these methods. Some recommendations are given on how these procedures may be employed in the field of accounting to detect unusual observations, fraud or error.
Group for Research in Decision Analysis