Search Results

CEO tenure and environmental fraud for listed family firms.

• Authors : Wang, Yang¹ (AUTHOR); Zhang, Yifei² (AUTHOR) ; Chang, Xiao³ (AUTHOR)

Subjects: *Environmental regulations; Fraud; Family-owned business enterprises

• Source: Business Strategy & the Environment (John Wiley & Sons, Inc). Mar2024, Vol. 33 Issue 3, p1887-1905. 19p.

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Entropy and improved k‐nearest neighbor search based under‐sampling (ENU) method to handle class overlap in imbalanced datasets.

• Authors : Kumar, Anil (AUTHOR); Singh, Dinesh (AUTHOR); Yadav, Rama Shankar (AUTHOR)

Subjects: ENTROPY; BIOLOGICAL classification; FRAUD investigation

• Source: Concurrency & Computation: Practice & Experience. 1/25/2024, Vol. 36 Issue 2, p1-36. 36p.

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How to detect and forecast corporate fraud by media reports? An approach using machine learning and qualitative comparative analysis.

• Authors : Qiu, Shi; Luo, Yuansheng

Subjects: FRAUD; FRAUD investigation; COMPARATIVE studies

• Source: Journal of Forecasting; Jan2024, Vol. 43 Issue 1, p58-80, 23p

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Bagging or boosting? Empirical evidence from financial statement fraud detection.

• Authors : Chen, Xiaowei; Zhai, Cong

Subjects: FRAUD investigation; FINANCIAL statements; FRAUD

• Source: Accounting & Finance; Dec2023, Vol. 63 Issue 5, p5093-5142, 50p

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Fraud detection through data sharing using privacy‐preserving record linkage, digital signature (EdDSA), and the MinHash technique: Detect fraud using privacy preserving record links.

• Authors : Thomas, Satish; Sluss, James

Subjects: FRAUD investigation; INFORMATION sharing; DIGITAL signatures

• Source: Journal of Engineering; Dec2023, Vol. 2023 Issue 12, p1-12, 12p

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Applied Science & Technology Full Text (H.W. Wilson)

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Shuffled shepherd political optimization‐based deep learning method for credit card fraud detection.

• Authors : Ganji, Venkata Ratnam (AUTHOR); Chaparala, Aparna (AUTHOR); Sajja, Radhika (AUTHOR)

Subjects: CREDIT card fraud; FRAUD investigation; OPTIMIZATION algorithms

• Source: Concurrency & Computation: Practice & Experience. 5/1/2023, Vol. 35 Issue 10, p1-19. 19p.

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Detecting insurance fraud using supervised and unsupervised machine learning.

• Authors : Debener, Jörn; Heinke, Volker; Kriebel, Johannes

Subjects: INSURANCE crimes; MACHINE learning; SUPERVISED learning

• Source: Journal of Risk & Insurance; Sep2023, Vol. 90 Issue 3, p743-768, 26p, 3 Diagrams, 8 Charts, 1 Graph

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Financial statement comparability and accounting fraud.

• Authors : Blanco, Belen; Dhole, Sandip; Gul, Ferdinand A.

Subjects: ACCOUNTING fraud; FORENSIC accounting; FINANCIAL statements

• Source: Journal of Business Finance & Accounting; Jul/Aug2023, Vol. 50 Issue 7/8, p1166-1205, 40p

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Insurance fraud detection: A statistically validated network approach.

• Authors : Tumminello, Michele; Consiglio, Andrea; Vassallo, Pietro

Subjects: INSURANCE crimes; FRAUD investigation; BIPARTITE graphs

• Source: Journal of Risk & Insurance; Jun2023, Vol. 90 Issue 2, p381-419, 39p, 5 Diagrams, 8 Charts, 11 Graphs

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