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(1 - 11 of 11)
- Title
- FRAUD DETECTION IN HIGHLY IMBALANCED BIG DATA WITH NOVEL AND EFFICIENT DATA REDUCTION TECHNIQUES.
- Creator
- Hancock III, John T., Taghi M. Khoshgoftaar, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
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...
Show moreThe 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.
Show less - Date Issued
- 2024
- PURL
- http://purl.flvc.org/fau/fd/FA00014424
- Subject Headings
- Fraud, Big data, Data reduction, Credit card fraud, Medicare fraud
- Format
- Document (PDF)
- Title
- Technologies of language and money: A study of stock manipulation and Internet communication.
- Creator
- Beresford, Annette D., Florida Atlantic University, Miller, Hugh T., College for Design and Social Inquiry, School of Public Administration
- Abstract/Description
-
Internet technologies provide a criminal opportunity for stock manipulation and fraud that costs investors millions of dollars every day. In order to reduce this loss and craft policies and procedures to deter future losses, securities regulators have been seeking to understand the process of Internet securities fraud, including the actions of investors and fraudsters that contribute to that process. The purpose of this study is to determine the properties of the Internet communication...
Show moreInternet technologies provide a criminal opportunity for stock manipulation and fraud that costs investors millions of dollars every day. In order to reduce this loss and craft policies and procedures to deter future losses, securities regulators have been seeking to understand the process of Internet securities fraud, including the actions of investors and fraudsters that contribute to that process. The purpose of this study is to determine the properties of the Internet communication environment associated with fraudulent stock schemes in order to contribute to these efforts of securities regulators. In addition, an aim of this study is to introduce Foucault's concept of power/knowledge as a means for theory development in the fields of finance, criminology and public administration that specifically addresses manipulation and fraud in the stock market.
Show less - Date Issued
- 2002
- PURL
- http://purl.flvc.org/fcla/dt/12012
- Subject Headings
- Foucault, Michel, Internet fraud, Securities fraud
- Format
- Document (PDF)
- Title
- An evaluation of Unsupervised Machine Learning Algorithms for Detecting Fraud and Abuse in the U.S. Medicare Insurance Program.
- Creator
- Da Rosa, Raquel C., Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The population of people ages 65 and older has increased since the 1960s and current estimates indicate it will double by 2060. Medicare is a federal health insurance program for people 65 or older in the United States. Medicare claims fraud and abuse is an ongoing issue that wastes a large amount of money every year resulting in higher health care costs and taxes for everyone. In this study, an empirical evaluation of several unsupervised machine learning approaches is performed which...
Show moreThe population of people ages 65 and older has increased since the 1960s and current estimates indicate it will double by 2060. Medicare is a federal health insurance program for people 65 or older in the United States. Medicare claims fraud and abuse is an ongoing issue that wastes a large amount of money every year resulting in higher health care costs and taxes for everyone. In this study, an empirical evaluation of several unsupervised machine learning approaches is performed which indicates reasonable fraud detection results. We employ two unsupervised machine learning algorithms, Isolation Forest and Unsupervised Random Forest, which have not been previously used for the detection of fraud and abuse on Medicare data. Additionally, we implement three other machine learning methods previously applied on Medicare data which include: Local Outlier Factor, Autoencoder, and k-Nearest Neighbor. For our dataset, we combine the 2012 to 2015 Medicare provider utilization and payment data and add fraud labels from the List of Excluded Individuals/Entities (LEIE) database. Results show that Local Outlier Factor is the best model to use for Medicare fraud detection.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00013042
- Subject Headings
- Machine learning, Medicare fraud, Algorithms
- Format
- Document (PDF)
- Title
- Machine Learning Algorithms with Big Medicare Fraud Data.
- Creator
- Bauder, Richard Andrew, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Healthcare is an integral component in peoples lives, especially for the rising elderly population, and must be affordable. The United States Medicare program is vital in serving the needs of the elderly. The growing number of people enrolled in the Medicare program, along with the enormous volume of money involved, increases the appeal for, and risk of, fraudulent activities. For many real-world applications, including Medicare fraud, the interesting observations tend to be less frequent...
Show moreHealthcare is an integral component in peoples lives, especially for the rising elderly population, and must be affordable. The United States Medicare program is vital in serving the needs of the elderly. The growing number of people enrolled in the Medicare program, along with the enormous volume of money involved, increases the appeal for, and risk of, fraudulent activities. For many real-world applications, including Medicare fraud, the interesting observations tend to be less frequent than the normative observations. This difference between the normal observations and those observations of interest can create highly imbalanced datasets. The problem of class imbalance, to include the classification of rare cases indicating extreme class imbalance, is an important and well-studied area in machine learning. The effects of class imbalance with big data in the real-world Medicare fraud application domain, however, is limited. In particular, the impact of detecting fraud in Medicare claims is critical in lessening the financial and personal impacts of these transgressions. Fortunately, the healthcare domain is one such area where the successful detection of fraud can garner meaningful positive results. The application of machine learning techniques, plus methods to mitigate the adverse effects of class imbalance and rarity, can be used to detect fraud and lessen the impacts for all Medicare beneficiaries. This dissertation presents the application of machine learning approaches to detect Medicare provider claims fraud in the United States. We discuss novel techniques to process three big Medicare datasets and create a new, combined dataset, which includes mapping fraud labels associated with known excluded providers. We investigate the ability of machine learning techniques, unsupervised and supervised, to detect Medicare claims fraud and leverage data sampling methods to lessen the impact of class imbalance and increase fraud detection performance. Additionally, we extend the study of class imbalance to assess the impacts of rare cases in big data for Medicare fraud detection.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00013108
- Subject Headings
- Medicare fraud, Big data, Machine learning, Algorithms
- Format
- Document (PDF)
- Title
- ADDRESSING HIGHLY IMBALANCED BIG DATA CHALLENGES FOR MEDICARE FRAUD DETECTION.
- Creator
- Johnson, Justin M., Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
Access to affordable healthcare is a nationwide concern that impacts most of the United States population. Medicare is a federal government healthcare program that aims to provide affordable health insurance to the elderly population and individuals with select disabilities. Unfortunately, there is a significant amount of fraud, waste, and abuse within the Medicare system that inevitably raises premiums and costs taxpayers billions of dollars each year. Dedicated task forces investigate the...
Show moreAccess to affordable healthcare is a nationwide concern that impacts most of the United States population. Medicare is a federal government healthcare program that aims to provide affordable health insurance to the elderly population and individuals with select disabilities. Unfortunately, there is a significant amount of fraud, waste, and abuse within the Medicare system that inevitably raises premiums and costs taxpayers billions of dollars each year. Dedicated task forces investigate the most severe fraudulent cases, but with millions of healthcare providers and more than 60 million active Medicare beneficiaries, manual fraud detection efforts are not able to make widespread, meaningful impact. Through the proliferation of electronic health records and continuous breakthroughs in data mining and machine learning, there is a great opportunity to develop and leverage advanced machine learning systems for automating healthcare fraud detection. This dissertation identifies key challenges associated with predictive modeling for large-scale Medicare fraud detection and presents innovative solutions to address these challenges in order to provide state-of-the-art results on multiple real-world Medicare fraud data sets. Our methodology for curating nine distinct Medicare fraud classification data sets is presented with comprehensive details describing data accumulation, data pre-processing, data aggregation techniques, data enrichment strategies, and improved fraud labeling. Data-level and algorithm-level methods for treating severe class imbalance, including a flexible output thresholding method and a cost-sensitive framework, are evaluated using deep neural network and ensemble learners. Novel encoding techniques and representation learning methods for high-dimensional categorical features are proposed to create expressive representations of provider attributes and billing procedure codes.
Show less - Date Issued
- 2022
- PURL
- http://purl.flvc.org/fau/fd/FA00014057
- Subject Headings
- Medicare fraud, Big data, Machine learning
- Format
- Document (PDF)
- Title
- An Evaluation of Deep Learning with Class Imbalanced Big Data.
- Creator
- Johnson, Justin Matthew, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-world applications, e.g. anomaly detection. Modeling such skewed data distributions is often very difficult, and non-standard methods are sometimes required to combat these negative effects. These challenges have been studied thoroughly using traditional machine learning algorithms, but very little empirical work exists in the area of deep learning with...
Show moreEffective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-world applications, e.g. anomaly detection. Modeling such skewed data distributions is often very difficult, and non-standard methods are sometimes required to combat these negative effects. These challenges have been studied thoroughly using traditional machine learning algorithms, but very little empirical work exists in the area of deep learning with class imbalanced big data. Following an in-depth survey of deep learning methods for addressing class imbalance, we evaluate various methods for addressing imbalance on the task of detecting Medicare fraud, a big data problem characterized by extreme class imbalance. Case studies herein demonstrate the impact of class imbalance on neural networks, evaluate the efficacy of data-level and algorithm-level methods, and achieve state-of-the-art results on the given Medicare data set. Results indicate that combining under-sampling and over-sampling maximizes both performance and efficiency.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013221
- Subject Headings
- Deep learning, Big data, Medicare fraud--Prevention
- Format
- Document (PDF)
- Title
- Big Data Analytics and Engineering for Medicare Fraud Detection.
- Creator
- Herland, Matthew Andrew, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
The United States (U.S.) healthcare system produces an enormous volume of data with a vast number of financial transactions generated by physicians administering healthcare services. This makes healthcare fraud difficult to detect, especially when there are considerably less fraudulent transactions than non-fraudulent. Fraud is an extremely important issue for healthcare, as fraudulent activities within the U.S. healthcare system contribute to significant financial losses. In the U.S., the...
Show moreThe United States (U.S.) healthcare system produces an enormous volume of data with a vast number of financial transactions generated by physicians administering healthcare services. This makes healthcare fraud difficult to detect, especially when there are considerably less fraudulent transactions than non-fraudulent. Fraud is an extremely important issue for healthcare, as fraudulent activities within the U.S. healthcare system contribute to significant financial losses. In the U.S., the elderly population continues to rise, increasing the need for programs, such as Medicare, to help with associated medical expenses. Unfortunately, due to healthcare fraud, these programs are being adversely affected, draining resources and reducing the quality and accessibility of necessary healthcare services. In response, advanced data analytics have recently been explored to detect possible fraudulent activities. The Centers for Medicare and Medicaid Services (CMS) released several ‘Big Data’ Medicare claims datasets for different parts of their Medicare program to help facilitate this effort. In this dissertation, we employ three CMS Medicare Big Data datasets to evaluate the fraud detection performance available using advanced data analytics techniques, specifically machine learning. We use two distinct approaches, designated as anomaly detection and traditional fraud detection, where each have very distinct data processing and feature engineering. Anomaly detection experiments classify by provider specialty, determining whether outlier physicians within the same specialty signal fraudulent behavior. Traditional fraud detection refers to the experiments directly classifying physicians as fraudulent or non-fraudulent, leveraging machine learning algorithms to discriminate between classes. We present our novel data engineering approaches for both anomaly detection and traditional fraud detection including data processing, fraud mapping, and the creation of a combined dataset consisting of all three Medicare parts. We incorporate the List of Excluded Individuals and Entities database to identify real world fraudulent physicians for model evaluation. Regarding features, the final datasets for anomaly detection contain only claim counts for every procedure a physician submits while traditional fraud detection incorporates aggregated counts and payment information, specialty, and gender. Additionally, we compare cross-validation to the real world application of building a model on a training dataset and evaluating on a separate test dataset for severe class imbalance and rarity.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013215
- Subject Headings
- Big data, Medicare fraud, Data analytics, Machine learning
- Format
- Document (PDF)
- Title
- THE INFLUENCE OF KINSHIP AND RACE/ETHNICITY ON THEFT AND FRAUD REPORTING INTENTIONS IN FAMILY FIRMS.
- Creator
- Howard, Ellison, Kidwell, Roland, Florida Atlantic University, Department of Management Programs, College of Business
- Abstract/Description
-
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...
Show moreTheft 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.
Show less - Date Issued
- 2024
- PURL
- http://purl.flvc.org/fau/fd/FA00014394
- Subject Headings
- Family-owned business enterprises, Fraud, Theft, Kinship
- Format
- Document (PDF)
- Title
- An Analysis of Securities Fraud Class Action Lawsuits: How Overvalued Equity and Related Factors Affect the Likelihood of Dismissals and the Magnitude of Settlements.
- Creator
- Houmes, Robert, Skantz, Terrance R., Florida Atlantic University
- Abstract/Description
-
Under Rule 10b-5 of the Securities Exchange Act of 1934, investors are provided a cause of action for losses resulting from management's intentionally deceptive disclosure or non-disclosure of information. Since lawsuits are costly, managers should be motivated to avoid a securities fraud class action. Prior research argues that managers attempt to mitigate the adverse effects of class actions by preempting negative eamings surprises (Skinner 1994 ). However, this study argues that when a...
Show moreUnder Rule 10b-5 of the Securities Exchange Act of 1934, investors are provided a cause of action for losses resulting from management's intentionally deceptive disclosure or non-disclosure of information. Since lawsuits are costly, managers should be motivated to avoid a securities fraud class action. Prior research argues that managers attempt to mitigate the adverse effects of class actions by preempting negative eamings surprises (Skinner 1994 ). However, this study argues that when a firm is overvalued, managers have incentives to avoid value reducing disclosure, which may lead to the violation of securities fraud laws. I investigate this assertion by testing associations between overvalued equity and the two outcomes of a securities fraud class action: dismissals and settlements. Other relevant factors related to overvalued equity are also tested and measured. These other factors include cases where the lead plaintiff is an institution, the length of the class period, the intrinsic value of exercisable CEO in-the-money stock option holdings, and corporate governance as measured by a corporate governance score and the occurrence of a GAAP violation. Findings show that the likelihood of a non-dismissal increases when an institution is the lead plaintiff and CEOs of overvalued firms hold higher amounts of in-the-money options. In addition, results suggest that for overvalued firms, stronger governance increases the probability of a non-dismissal.
Show less - Date Issued
- 2007
- PURL
- http://purl.flvc.org/fau/fd/FA00000305
- Subject Headings
- Class actions (Civil procedure)--United States, Securities fraud--United States, Corporations--Corrupt practices--United States
- Format
- Document (PDF)
- Title
- The argument of the bishop of New Jersey in reply to the paper read before the Court of Bishops in session at Burlington on Monday, 11 October, 1852, by the bishops of Virginia, Ohio and Maine in answer to the representation from the Diocese of New Jersey.
- Creator
- Doane, George Washington 1799-1859, Southard, Samuel L. (Samuel Lewis) 1819-1859
- Abstract/Description
-
Read before the court by the Rev. Samuel L. Southard. Cover title: Bishop Doane's argument before the Court of Bishops. Notes: Includes bibliographical references. FAU Libraries' copy in original paper wrapper (trimmed to 23 cm); side stitched with cord.
- PURL
- http://purl.flvc.org/fau/fd/fauwsb15f33
- Subject Headings
- Church schools -- New Jersey -- Burlington -- Finance -- 19th century, Doane, George Washington -- 1799-1859 -- Trials, litigation, etc, Ecclesiastical courts -- New Jersey, Episcopal Church -- New Jersey -- Trials, litigation, etc, New Jersey -- Church history -- 19th century -- Sources, Speeches, addresses, etc., American -- 19th century, Trials (Fraud) -- New Jersey
- Format
- E-book
- Title
- Reply of Bishops Meade, M'Ilvaine, and Burgess to the argument presented by the committee of the convention of the diocese of New Jersey, to the court of bishops, in session at Burlington for the trial of Bishop Doane.
- Creator
- Meade, William 1789-1862, Burgess, George 1809-1866
- Abstract/Description
-
Reply of Bishops Meade, McIlvaine, and Burgess to the argument presented by the committee of the convention of the diocese of New Jersey, to the court of bishops, in session at Burlington for the trial of Bishop Doane. Includes bibliographical references. FAU copy in plane white paper wrappers (23 cm); side stitched with cord.
- PURL
- http://purl.flvc.org/fau/fd/fauwsb16f1
- Subject Headings
- Burgess, George -- 1809-1866, Church schools -- New Jersey -- Burlington -- Finance -- 19th century, Doane, George Washington -- 1799-1859 -- Trials, litigation, etc, Ecclesiastical courts -- New Jersey, Episcopal Church -- Diocese of New Jersey -- Bishop (1832-1859 : Doane) -- Trials, litigation, etc, Episcopal Church -- Diocese of New Jersey -- Bishops, Episcopal Church -- New Jersey -- Trials, litigation, etc, McIlvaine, Charles Pettit -- 1799-1873, Meade, William -- 1789-1862, Trials (Fraud) -- New Jersey -- 19th century
- Format
- E-book
