Comparison of Data Mining Techniques to Correctly Identify Financial Statement Fraud PDF Download
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Author: Sterling Panos
Publisher:
ISBN:
Category : Data mining
Languages : en
Pages : 58
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Book Description
Obtained results from this study indicate that the Support Vector Machine analysis is superior to logistic regression and decision-tree analysis in identifying financial statement fraud; improving the overall prediction rates by approximately 8.5%. Support Vector Machine additionally had a 15.5% prediction rate than neural networking. The findings suggest that Support Vector Machine analysis is an important new tool in identifying financial statement fraud.
Author: Sterling Panos
Publisher:
ISBN:
Category : Data mining
Languages : en
Pages : 58
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Book Description
Obtained results from this study indicate that the Support Vector Machine analysis is superior to logistic regression and decision-tree analysis in identifying financial statement fraud; improving the overall prediction rates by approximately 8.5%. Support Vector Machine additionally had a 15.5% prediction rate than neural networking. The findings suggest that Support Vector Machine analysis is an important new tool in identifying financial statement fraud.
Author: Rohan Ahmed
Publisher: GRIN Verlag
ISBN: 3668709270
Category : Computers
Languages : en
Pages : 18
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Book Description
Seminar paper from the year 2016 in the subject Computer Science - General, grade: 1.7, Heilbronn University, language: English, abstract: In this seminar thesis you will get a view about the Data Mining techniques in financial fraud detection. Financial Fraud is taking a big issue in economical problem, which is still growing. So there is a big interest to detect fraud, but by large amounts of data, this is difficult. Therefore, many data mining techniques are repeatedly used to detect frauds in fraudulent activities. Majority of fraud area are Insurance, Banking, Health and Financial Statement Fraud. The most widely used data mining techniques are Support Vector Machines (SVM), Decision Trees (DT), Logistic Regression (LR), Naives Bayes, Bayesian Belief Network, Classification and Regression Tree (CART) etc. These techniques existed for many years and are used repeatedly to develop a fraud detection system or for analyze frauds.
Author: Zakaria Ouraich
Publisher:
ISBN:
Category : Fraud
Languages : en
Pages : 86
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Book Description
Author: Patil, Bhushan
Publisher: IGI Global
ISBN: 1799830543
Category : Computers
Languages : en
Pages : 583
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Book Description
Analyzing data sets has continued to be an invaluable application for numerous industries. By combining different algorithms, technologies, and systems used to extract information from data and solve complex problems, various sectors have reached new heights and have changed our world for the better. The Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics is a collection of innovative research on the methods and applications of data analytics. While highlighting topics including artificial intelligence, data security, and information systems, this book is ideally designed for researchers, data analysts, data scientists, healthcare administrators, executives, managers, engineers, IT consultants, academicians, and students interested in the potential of data application technologies.
Author: Tarjo
Publisher:
ISBN:
Category :
Languages : en
Pages : 8
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Book Description
Objective - This research is expected to improve the weaknesses in the research conducted by Tarjo and Herawati (2015). The objective of this study was to analyse two data mining methods in detecting financial fraud based on Beneish m-score model.Methodology/Technique - The research data were companies who committed fraud based Database Case Sanctions Issuers and Public Companies which was released by the Financial Services Authority in the period 2001-2014. For comparison, researchers also used data from companies that did not commit fraud. Companies were selected based on the same industry group of companies committing fraud for the purposes of classification.Findings - The results show that data mining methods can be used to detect financial fraud based on Beneish m-score model. However, there are differences in the classification. In the logit regression, the results are only limited to the accuracy of classification and weak. While the K-Nearest Neighbor model, in addition, it is capable of performing high classification accuracy.Novelty - The study indicates a better method for detecting financial fraud.
Author: 江玟諭
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Simon Grima
Publisher: Emerald Group Publishing
ISBN: 1838676376
Category : Business & Economics
Languages : en
Pages : 272
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Book Description
In the 18 chapters in this volume of Contemporary Studies in Economic and Financial Analysis, expert contributors gather together to examine the extent and characteristics of forensic accounting, a field which has been practiced for many years, but is still not internationally regulated yet.
Author: Adalbert F.X. Wilhelm
Publisher: Springer
ISBN: 3319252267
Category : Computers
Languages : en
Pages : 640
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Book Description
This book offers a snapshot of the state-of-the-art in classification at the interface between statistics, computer science and application fields. The contributions span a broad spectrum, from theoretical developments to practical applications; they all share a strong computational component. The topics addressed are from the following fields: Statistics and Data Analysis; Machine Learning and Knowledge Discovery; Data Analysis in Marketing; Data Analysis in Finance and Economics; Data Analysis in Medicine and the Life Sciences; Data Analysis in the Social, Behavioural, and Health Care Sciences; Data Analysis in Interdisciplinary Domains; Classification and Subject Indexing in Library and Information Science. The book presents selected papers from the Second European Conference on Data Analysis, held at Jacobs University Bremen in July 2014. This conference unites diverse researchers in the pursuit of a common topic, creating truly unique synergies in the process.
Author: Oluwatoyin E. Akinbowale
Publisher: AOSIS
ISBN: 1991271085
Category : Computers
Languages : en
Pages : 392
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Book Description
The book covers the overview of cyberfraud and the associated global statistics. It demonstrates practicable techniques that financial institutions can employ to make effective decisions geared towards cyberfraud mitigation. Furthermore, the book contains some emerging technologies, such as information and communication technologies (ICT), forensic accounting, big data technologies, tools and analytics employed in fraud mitigation. In addition, it highlights the implementation of some techniques, such as the fuzzy analytical hierarchy process (FAHP) and system thinking approach to address information and security challenges. The book combines a case study, empirical findings, a systematic literature review and theoretical and conceptual concepts to provide practicable solutions to mitigate cyberfraud. The major contributions of this book include the demonstration of digital and emerging techniques, such as forensic accounting for cyber fraud mitigation. It also provides in-depth statistics about cyber fraud, its causes, its threat actors, practicable mitigation solutions, and the application of a theoretical framework for fraud profiling and mitigation.
Author: Joseph T. Wells
Publisher:
ISBN: 9781889277080
Category : Auditing, Internal
Languages : en
Pages : 564
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Book Description
