Using Nonfinancial Measures to Assess Fraud Risk

This post comes to us from Joseph F. Brazel of North Carolina State University, Keith L. Jones of George Mason University, and Mark F. Zimbelman of Brigham Young University.

 

In our paper, Using Nonfinancial Measures to Assess Fraud Risk, which is forthcoming in the Journal of Accounting Research, we investigate whether publicly available nonfinancial measures (NFMs), such as the number of retail outlets, warehouse space, or employee headcounts, can be used to assess the likelihood of fraud.

More specifically, we test whether inconsistencies between financial and NFM data can be used to detect fraud. By doing so, we also implicitly provide evidence on whether systematic NFM manipulation is occurring at fraud firms. We also test whether NFMs can be used to detect when a firm’s reported financial performance does not accurately portray its economic performance. This study also expands the NFM literature by providing an empirical test of their potential to verify current financial results, as the extant NFM research looks at the ability of NFMs to predict future firm performance. We believe both roles of NFMs are valuable—one to validate and the other to forecast. We find that the relation between reported financial performance and NFMs can distinguish fraud from non-fraud firms.

Our fraud sample includes firms charged by the SEC with having fraudulently reported revenue in at least one 10-K filing. We do not include frauds that involve quarterly data and we also limit our sample to firms for which we were able to access the original 10-K filing and subsequent filings of restated data. Students enrolled in undergraduate and graduate auditing courses at three universities selected the non-fraud competitors and collected NFM data for our sample of fraud firms. Our sample includes NFMs that are quantitative, non-financial, non-employee related, and relate to firm capacity. Using this matched-pair sample, we document that fraud firms are more likely than non-fraud firms to report inconsistent revenue growth relative to their growth in NFMs. We analyze the growth from the year prior to the fraud to the first year of the fraud for each matched-pair. When we include a variable that measures the difference between a firm’s financial performance and its NFM performance in a model that includes other factors that have been found to be indicative of fraud, we find the difference is a significant discriminator between fraud and non-fraud firms. Thus, we provide evidence showing that comparisons between financial measures and NFMs can be effectively used to assess fraud risk.

Overall, our results provide empirical evidence suggesting that nonfinancial measures can be effectively used to assess the likelihood of fraud.

The full paper is available for download here.

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