Results 281 to 290 of about 2,388,437 (341)
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Digital finance and corporate financial fraud
International Review of Financial Analysis, 2023Guanglin Sun +3 more
semanticscholar +3 more sources
Fraud Triangle terhadap Financial Statement Fraud
Portofolio: Jurnal Ekonomi, Bisnis, Manajemen, dan Akuntansi, 2022This study aims to analyze the effect of the fraud triangle on the financial statement fraud. Based on the theory of fraud triangle, there are three factors: pressure, opportunity, and rationalization. Sampling is done by purposive sampling method and obtained 19 sample companies with 57 observational data of manufacturing companies consumer goods ...
Carmel Meiden, Steven Steven
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Financial Statement Fraud Detection with Fraud Triangle
International Journal of Emerging Trends in Social Sciences, 2022The 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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Financial fraud detection using quantum graph neural networks
Quantum Machine Intelligence, 2023Financial fraud detection is essential for preventing significant financial losses and maintaining the reputation of financial institutions. However, conventional methods of detecting financial fraud have limited effectiveness, necessitating the need for
Nouhaila Innan +9 more
semanticscholar +1 more source
Internet Financial Fraud Detection Based on Graph Learning
IEEE Transactions on Computational Social Systems, 2023The rapid development of information technology such as the Internet of Things, Big Data, artificial intelligence, and blockchain has changed the transaction mode of the financial industry and greatly improved the convenience of financial transactions ...
Ranran Li +4 more
semanticscholar +1 more source
IEEE Open Journal of the Computer Society
As financial systems become increasingly complex and interconnected, traditional fraud detection methods struggle to keep pace with sophisticated fraudulent activities.
Yiwen Cui +5 more
semanticscholar +1 more source
As financial systems become increasingly complex and interconnected, traditional fraud detection methods struggle to keep pace with sophisticated fraudulent activities.
Yiwen Cui +5 more
semanticscholar +1 more source
Proceedings of the 10th annual conference on Genetic and evolutionary computation, 2008
A major difficulty for anomaly detection lies in discovering boundaries between normal and anomalous behavior, due to the deficiency of abnormal samples in the training phase. In this paper, a novel coevolutionary algorithm which attempts to simulate territory establishment in ecology is conceived to tackle anomaly detection problems.
Shelly Xiaonan Wu, Wolfgang Banzhaf
openaire +1 more source
A major difficulty for anomaly detection lies in discovering boundaries between normal and anomalous behavior, due to the deficiency of abnormal samples in the training phase. In this paper, a novel coevolutionary algorithm which attempts to simulate territory establishment in ecology is conceived to tackle anomaly detection problems.
Shelly Xiaonan Wu, Wolfgang Banzhaf
openaire +1 more source
arXiv.org
This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sector.
Yisong Chen +3 more
semanticscholar +1 more source
This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sector.
Yisong Chen +3 more
semanticscholar +1 more source
IEEE Access, 2023
Fraudulent transactions and how to detect them remain a significant problem for financial institutions around the world. The need for advanced fraud detection systems to safeguard assets and maintain customer trust is paramount for financial institutions,
Tomisin Awosika +2 more
semanticscholar +1 more source
Fraudulent transactions and how to detect them remain a significant problem for financial institutions around the world. The need for advanced fraud detection systems to safeguard assets and maintain customer trust is paramount for financial institutions,
Tomisin Awosika +2 more
semanticscholar +1 more source
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods
arXiv.orgTraditional machine learning models often prioritize predictive accuracy, often at the expense of model transparency and interpretability. The lack of transparency makes it difficult for organizations to comply with regulatory requirements and gain ...
F. Almalki, Mehedi Masud
semanticscholar +1 more source