Fraud

The rapid growth of digital transactions and the increasing sophistication of fraudulent activities have necessitated the development of robust and efficient fraud detection techniques, particularly in the financial and healthcare sectors. This dissertation focuses on the use of novel data reduction techniques for addressing the unique challenges associated with detecting fraud in highly imbalanced Big Data, with a specific emphasis on credit card transactions and Medicare claims. The highly imbalanced nature of these datasets, where fraudulent instances constitute less than one percent of the data, poses significant challenges for traditional machine learning algorithms. This dissertation explores novel data reduction techniques tailored for fraud detection in highly imbalanced Big Data. The primary objectives include developing efficient data preprocessing and feature selection methods to reduce data dimensionality while preserving the most informative features, investigating various machine learning algorithms for their effectiveness in handling imbalanced data, and evaluating the proposed techniques on real-world credit card and Medicare fraud datasets.
This dissertation covers a comprehensive examination of datasets, learners, experimental methodology, sampling techniques, feature selection techniques, and hybrid techniques. Key contributions include the analysis of performance metrics in the context of newly available Big Medicare Data, experiments using Big Medicare data, application of a novel ensemble supervised feature selection technique, and the combined application of data sampling and feature selection. The research demonstrates that, across both domains, the combined application of random undersampling and ensemble feature selection significantly improves classification performance.

Theft and fraud within family firms can have a significant impact on local, national, and international economies, given that most businesses operating throughout the world are family firms. According to familybusiness.com, 62% of the US workforce is employed by family businesses. Yet, we do not know much about how family firms respond to theft and fraud committed within their firms or the factors that influence their responses. The goal of this dissertation is to better understand a family firm owner’s decision to report theft and fraud committed by family and non-family employees, and whether kinship strength and race/ethnicity have any discernable effects on these reporting intentions. To achieve that goal, this study integrates insights from family firm, sociology, and psychology literatures. It presents a conceptual model and three sets of hypotheses that were tested in this empirical study. The results extend previous literature by providing support that kinship not only influences family employee theft intentions, but family owner reporting intentions as well. In addition, egalitarianism, or race avoidance, was shown to interact with kinship to influence owner reporting intentions.