Accounts and Management Fraud

This case is based on an actual fraud that occurred and provides you with an opportunity to develop fraud examination skills, which include document examination, searching public records, financial statement analysis, and communicating the results of your work. Such skills benefit all accounting students regardless of the career path they may choose (e.g., a fraud investigator, an auditor, a consultant, a tax accountant). This case also: (1) exemplifies the complexity often found in fraud cases, and (2) illustrates how fraud examinations differ from financial statement audits. While some of the names of the parties involved have been changed, no facts in the case have been altered. Interstate Business College (IBC), founded in 1912, collapsed in the wake of allegations of top management fraud. The allegations became public when 23 former students filed a lawsuit against the director and owner of IBC, alleging misappropriation of student funds. You will assume the role of the fraud investigator hired by their attorney to determine if there is evidence to support their claim. Upon completion of the case, you will have a sense of the amount of documents, detail, and work involved when resolving fraud allegations.

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Biological Data Mining

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch099 ◽

2008 ◽

pp. 1696-1705

Author(s):

George Tzanis ◽

Christos Berberidis ◽

Ioannis Vlahavas

Keyword(s):

Data Mining ◽

New Technologies ◽

Noisy Data ◽

Large Data ◽

Biological Data ◽

Customer Relationship ◽

Mass Storage ◽

Stock Management ◽

Management Fraud ◽

Storage Media

At the end of the 1980s, a new discipline named data mining emerged. The introduction of new technologies such as computers, satellites, new mass storage media, and many others have lead to an exponential growth of collected data. Traditional data analysis techniques often fail to process large amounts of, often noisy, data efficiently in an exploratory fashion. The scope of data mining is the knowledge extraction from large data amounts with the help of computers. It is an interdisciplinary area of research that has its roots in databases, machine learning, and statistics and has contributions from many other areas such as information retrieval, pattern recognition, visualization, parallel and distributed computing. There are many applications of data mining in the real world. Customer relationship management, fraud detection, market and industry characterization, stock management, medicine, pharmacology, and biology are some examples (Two Crows Corporation, 1999).

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Treuhandkonten im Rahmen der Abschlussprüfung. Bankbestätigungen und Einwendung des Mitverschuldens bei Top Management Fraud

10.5771/9783748925439-785 ◽

2021 ◽

pp. 785-802

Author(s):

Joachim Hennrichs

Keyword(s):

Top Management ◽

Management Fraud

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For Better Healthcare Mining Health Data

Advances in Data Mining and Database Management - Intelligent Multidimensional Data Clustering and Analysis ◽

10.4018/978-1-5225-1776-4.ch006 ◽

2017 ◽

pp. 135-158

Author(s):

Güney Gürsel

Keyword(s):

Data Mining ◽

Nearest Neighbor ◽

Healthcare Management ◽

Support Vector ◽

Privacy And Security ◽

Healthcare Data ◽

Management Fraud ◽

Use Of Data ◽

Customer Relation Management ◽

Vector Machines

Data mining has great contributions to the healthcare such as support for effective treatment, healthcare management, customer relation management, fraud and abuse detection and decision making. The common data mining methods used in healthcare are Artificial Neural Network, Decision trees, Genetic Algorithms, Nearest neighbor method, Logistic regression, Fuzzy logic, Fuzzy based Neural Networks, Bayesian Networks and Support Vector Machines. The most used task is classification. Because of the complexity and toughness of medical domain, data mining is not an easy task to accomplish. In addition, privacy and security of patient data is a big issue to deal with because of the sensitivity of healthcare data. There exist additional serious challenges. This chapter is a descriptive study aimed to provide an acquaintance to data mining and its usage and applications in healthcare domain. The use of Data mining in healthcare informatics and challenges will be examined.

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