“Catch me if you can” : identifying, predicting and reducing the opportunities for fraud

This thesis includes three papers that helps in identifying, predicting, and reducing the opportunities to commit fraud. Specifically, my first paper focuses on identifying fraud in academic publications; second paper focuses on identifying and predicting fraud using financial statements and third paper focuses on reducing the opportunities to commit fraud by identifying partner characteristics that could improve audit quality. In my first paper I investigate whether Benford’s Law can be utilised to determine the validity of data employed within published academic studies. I use the case of Professor James Hunton who had 37 of his articles retracted because there were grave concerns that they contained misstated or fabricated datasets. Based on first significant digits contained in the articles I construct several Benford conformity measures to determine whether Hun ton’s retracted papers differ significantly from a control group of non-retracted articles by competing authors. My results clearly indicate that Hunton’s retracted papers significantly deviate from Benford Law, relative to the control group of papers. Moreover, my results also suggest Hunton’s remaining 19 nonretracted articles may need further examination. My findings suggest both co-authors and journal editors should consider implementing a validation and review process that employs Benford Law as a ‘doping’ or ‘signalling’ mechanism with a view to decreasing the risk of fraudulent activity and thereby enhancing the credibility of academic papers and journals. In my second paper, I provide two different models that could help predict financial misconducts. Misreporting continues to have huge impacts on both the domestic and global market. Based on criminology literature the path to financial misreporting is a slippery slope rather than a cliff edge phenomenon and deviations from Benford Law. The first model is based on the proportional hazard model and the second model plots the cumulative deviations and comparing the same with an empirical distribution. Based on prior literature I created a sample of firms that has either received an Accounting and Auditing Enforcement Releases or receipt of a Security 8 Class Action or restatement of financials. I find under both model specifications I can correctly predict the firms that are more likely to misreport in future with a low rate of false positives. In my third paper, I examine the association between the gender of the first-born audit partner’s child and audit quality. Based on the protectionism and socialisation theory parental values and preferences are shaped by having a first-born daughter. Specifically, prior literature parents with daughters are found to be more cautious, conservative, risk adverse and adopt many female preferences and values. I investigate and find conclusive evidence that these daughter effects potentially promote characteristics that leads to a higher audit quality. The findings are confirmed by an array of robustness check and thus contribute to the literature that an audit partner’s family environment shapes audit outcomes. These findings potentially have direct implications for internal audit firm management as well as investors and regulator