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This work is cited by the following items of the Benford Online Bibliography:
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Cabarle, C (2019). Predicting the Risk of Fraud in Equity Crowdfunding Offers and Assessing the Wisdom of the Crowd. PhD Thesis, Temple University, ProQuest Dissertations Publishing, 2019. 13863507.





Druica, E, Oancea, B and Valsan, C (2018). Benford's law and the limits of digit analysis. International Journal of Accounting Information Systems 31, pp. 75–82. DOI:10.1016/j.accinf.2018.09.004.





González, F (2019). Detecting Anomalous Data in Household Surveys: Evidence for Argentina. Journal of Social and Economic Statistics 8(2), pp. 110. DOI:10.2478/jses20190001.





González, F (2020). Selfreported income data: are people telling the truth?. To appear in Journal of Financial Crime. DOI:10.1108/JFC0820190113.





Nigrini, MJ (2019). The patterns of the numbers used in occupational fraud schemes. Managerial Auditing Journal 34(5), pp. 606626. DOI:10.1108/MAJ1120171717.





Pavlović, V, Knežević, G, Joksimović, M and Joksimović, D (2019). Fraud Detection in Financial Statements Applying Benford's Law with Monte Carlo Simulation. Acta oeconomica 69(2), pp.217239. DOI:10.1556/032.2019.69.2.4.




