Data mining techniques identify relationships, patterns, trends, and predictive information form large and complex databases. This study demonstrates the use of a data mining technique to assess the risk of management fraud. We use a data mining tool to analyze the management fraud data, presence or absence of red flags in fraud and no fraud cases, collected by a Big Six firm. The ensuing results compare favorably with the statistical and neural network results obtained by the other studies. The study illustrates the ease of using data mining techniques by demonstrating the rapid development of models, querying capabilities, and ease of encoding statistical models in audit decision making
The Association of Certified Fraud Examiners (ACFE) reported [1] that a typical organization loses a...
Data mining is the process of extracting useful information from very large data sources. Data minin...
The purpose of this dissertation was to study why corporate fraud detection models are often met wit...
ABSTRACT: This paper is a survey on the blooming concept of Data mining and its applications in the ...
Fraud is a million dollar business and it’s increasing every year. The numbers are shocking, all the...
M.Com. (Computer Auditing)Fraud is a major problem in South Africa and the world and organisations l...
The need for data mining in the auditing field is growing rapidly. As the online systems and the hi-...
Detecting, evaluating and understanding fraud reports, called as misstated reports, has a long histo...
The paper presents application of data mining techniques to fraud analysis. We present some classifi...
Data mining is a subset of databases management and it mainly applicable to large and complex databa...
Corporate fraud these days represents a huge cost to our economy. Academic literature already concen...
Fraud entails deception in order to obtain illegal gains; thus, it is mainly evidenced within financ...
Among the knowledge areas in which process mining has had an impact, the audit domain is particularl...
This paper explores the effectiveness of machine learning techniques in detecting firms that issue f...
This paper gives a comprehensive revision of the state-of-the-art research in detecting financial fr...
The Association of Certified Fraud Examiners (ACFE) reported [1] that a typical organization loses a...
Data mining is the process of extracting useful information from very large data sources. Data minin...
The purpose of this dissertation was to study why corporate fraud detection models are often met wit...
ABSTRACT: This paper is a survey on the blooming concept of Data mining and its applications in the ...
Fraud is a million dollar business and it’s increasing every year. The numbers are shocking, all the...
M.Com. (Computer Auditing)Fraud is a major problem in South Africa and the world and organisations l...
The need for data mining in the auditing field is growing rapidly. As the online systems and the hi-...
Detecting, evaluating and understanding fraud reports, called as misstated reports, has a long histo...
The paper presents application of data mining techniques to fraud analysis. We present some classifi...
Data mining is a subset of databases management and it mainly applicable to large and complex databa...
Corporate fraud these days represents a huge cost to our economy. Academic literature already concen...
Fraud entails deception in order to obtain illegal gains; thus, it is mainly evidenced within financ...
Among the knowledge areas in which process mining has had an impact, the audit domain is particularl...
This paper explores the effectiveness of machine learning techniques in detecting firms that issue f...
This paper gives a comprehensive revision of the state-of-the-art research in detecting financial fr...
The Association of Certified Fraud Examiners (ACFE) reported [1] that a typical organization loses a...
Data mining is the process of extracting useful information from very large data sources. Data minin...
The purpose of this dissertation was to study why corporate fraud detection models are often met wit...