In recent years data mining techniques have been used to identify companies who issue fraudulent financial state- ments. However most of the research conducted thus far use datasets that are balanced. This does not always represent reality especially in fraud applications. In this paper we demonstrate the effectiveness of cost-sensitive classifiers to detect financial statement fraud using South African market data. The study also shows how different levels of cost affect overall accuracy sensitivity specificity recall and precision using PCA and Factor Analysis. Weighted Support Vector Machines (SVM) were shown superior to the cost-sensitive Naive Bayes (NB) and K-Nearest Neighbors classifiers. Use the Paper to answer the following discussion question: Answer each question on separate line 1. In this paper the author sought to demonstrate what in their work? 2. What were the author’s motivation behind doing this work 3. In the financial fraud research domain the data contains what two classes? Describe the two classes of data and how this leads to problems? 4. What is the goal of cost sensitive learning? 5. List the data mining techniques other researchers have utilized to predict the manipulation of financial data. 6. Name two feature selection techniques the authors utilized in their work. 7. Describe the data set the researchers used (ie. Where the data come from how many features describe the features used 8. What was the minority class in dataset? 9. In the final dataset used how many features and observations were there? And how many were fraud? 10. How many principal components explained 90 percent of the total variation of the dataset? How were these components used for factor analysis? 11. What were some limitations to this study? NO OUTSIDE SOURCE NO PLAGRISM
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