Based on stepwise-logistic models, this study finds that financial leverage, capital turnover, asset composition and firm size are significant factors associated with fraudulent financial reporting Prediction results suggest that these models outperform a nae strategy of classifying all firms as nonfraud firms for all levels of relative costs of type I and type II errors. The models also correctly identify a large percentage of fraud firms and misclassify a relatively small percentage of nonfraud firms when realistic relative error costs are assumed.
The purpose of this study is to identify those approaches and techniques from the literature which auditors could usefully employ to detect fraudulently compiled financial statements. The classification which results includes a brief analysis of a number of studies classified within the following categories: prediction models, expert systems, neural networks, Benford's law, computer technology assistance, simple analytical procedure, 'red flag' groupings and broad strategies. We conclude on how this analysis could be useful to future researchers and to auditors in the field who are seeking to improve their approach to detecting material fraud in the financial statements.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.