Using Benford's law and neural networks as a review procedure

Introduces a new analytical review procedure that measures the degree to which a data set's digit distribution deviates from a Benford digit distribution. This deviation can indicate potential manipulation and can be used to signal the need for further audit testing. An artificial neural network is used to distinguish between "normal" and "manipulated" financial data. The results show that if data have been contaminated (at a 10 per cent level or more) a Benford analytical review procedure will detect this 68 per cent of the time. If the data are not contaminated, the test will indicate that the data are "clean" 67 per cent of the time. Because analytical review procedures are not used in isolation, these results probably understate the effectiveness and potential of a digits-based analytical review procedure. This procedure's fraud detection results compare favorably to traditional analytical review procedures. Importantly, its unique analysis procedure allows it to complement traditional analytical review procedures. A key limitation of this study is that it uses simulated data, rather than actual data. Such an enhancement will be a critical step in future research. This method appears to have potential merit and provides many opportunities for new research.