Results 271 to 280 of about 9,355 (308)

Financial Statement Fraud Detection with Fraud Triangle

International Journal of Emerging Trends in Social Sciences, 2022
The purpose of this study was to analyze elements in the fraud triangle to clarify the possibility of financial statement fraud in the consumer goods industry subsector. The population in this study is all manufacturing companies in the consumer goods industry sub-sector measured by the M-score model.
Meel Akbar, Basyiruddin Nur, Budi Andru
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Detecting Fraud in Financial Reports

2012 European Intelligence and Security Informatics Conference, 2012
Fraud in public companies has a large financialimpact, and yet is only weakly detected by those who look for it, many incidents have been detected only when whistleblowers have come forward. We examine the problem of detecting fraud from the textual component of the quarterly and annual reports that public companies are required to file.
David B. Skillicorn, Lynnette D. Purda
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Intelligent financial fraud detection: A comprehensive review

Computers and Security, 2016
Maumita Bhattacharya
exaly   +2 more sources

MCDM method for Financial Fraud Detection

Proceedings of the 4th International Conference on Big Data and Internet of Things, 2019
Financial fraud has a big impact on the financial sector, governments, companies and ordinary consumers. With reliance on new technologies such as cloud and mobile computing, the impact of this problem has become very dangerous. The overall losses caused by financial fraud are uncountable, which makes its detection indispensable to prevent the ...
Maryeme Taher, Hatim Guermah, Mahmoud Nassar   +2 more
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A financial fraud detection indicator for investors: an IDeA

Annals of Operations Research, 2019
Fraud detection is a key issue for investors and financial authorities. The Ponzi schemeorganized by Bernard Madoff is a magnified example of a financial fraud, always possiblewhen well-orchestrated. Traditional methods to detect fraud require costly and lengthyinvestigations that involve complex financial and legal knowledge, as well as highly ...
Bernard, Philippe, El Mekkaoui de Freitas, Najat, Maillet, Bertrand   +2 more
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Deep learning for detecting financial statement fraud

Decision Support Systems, 2020
Abstract Financial statement fraud is an area of significant consternation for potential investors, auditing companies, and state regulators. The paper proposes an approach for detecting statement fraud through the combination of information from financial ratios and managerial comments within corporate annual reports.
Patricia Craja, Alisa Kim, Stefan Lessmann   +2 more
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Fuzzy Ranking of Financial Statements for Fraud Detection

2006 IEEE International Conference on Fuzzy Systems, 2006
Automatic detection of anomalies in financial statements can decrease the risk of exposure to fraudulent corporate behavior. This paper proposes a method to convert fraud classification rules learned from a genetic algorithm to a fuzzy score representing the degree to which a company's financial statements match those rules.
Wei Chai, Bethany Hoogs, Benjamin T. Verschueren   +2 more
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Comparing ML Algorithms on Financial Fraud Detection

Proceedings of the 2019 2nd International Conference on Data Science and Information Technology, 2019
The problem of Financial Fraud has reached an alarming scale nowadays. Losses due to the fraud are reaching billions of dollars every year. To reduce it, decision systems that use efficient fraud detection algorithms should be invented. With the support of modern technologies, these systems are able to manage to analyze the information and to create a ...
Chung Min Tae, Phan Duy Hung
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Financial Inclusion, Financial Crime, and Fraud Detection

The objective of this chapter is to discuss the role of financial inclusion in combating financial crime. It was found that financial crime is a challenge in society. Financial crime is any action or omission that leads to unlawful or illegal financial dealings. Many countries are seeking ways to combat financial crime.
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A Memory-Enhanced Framework for Financial Fraud Detection

2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 2018
The rapid development of electronic financial services brings significant convenience to our daily life. However, it also offers criminals the opportunity to exploit financial systems to do fraudulent transactions. Previous studies on fraud detection only deal with single type transactions and cannot adapt well to evolving environment in reality.
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