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Case study: an analytical model for the fraud detection In company purchases
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Orientador(es)
Resumo(s)
In a world where the business landscape is changing as a consequence of the increasing importance of the digital area, the financial and reporting environment are also being reshaped, resulting in several challenges for audit committees and auditors. Assurance services are responsible for transmitting clear information and nowadays auditing has become an increasingly demanding task. In this regard, detection fraud is one of the areas explored by assurance, including studying the risk of companies committing fraud, and also the hypothesis of employees committing these kinds of misconduct. In fact, in the modern digital world, it becomes easier to commit fraud, therefore it is quite relevant to study its impacts and causes. Theories related to the fraud triangle as well as fraud related to the utilities sector will be explored and will be the basis of this study.
Having this in mind, this study aims to identify the most significant variables in detecting the risk of fraud in company purchases, using some companies’ data in order to help this analysis. Having this in mind, the conclusions about the selected model were to consider two different approaches: Keeping All Variables and Removing Some Variables. For the first option, the best model was Linear Regression and for the other one was Neural Networks, considering Misclassification Rate and Captured Response as significant statistics. Thus, this study aims to fill the gap of information and studies in this area by providing relevant inputs that may be used on other studies in this field.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and Management
Palavras-chave
Fraud Detection Risk Analytical Models Company Purchases Assurance