Results 261 to 270 of about 3,160,195 (300)

Gerontologists combating fraudulent research participants: recommendations from design to dissemination. [PDF]

Gerontologist
Sefcik JS, Boon JT, Buck HG, Frechman E, Wallace CL, Carpenter JG.   +5 more
europepmc   +1 more source

HQRNN-FD: A Hybrid Quantum Recurrent Neural Network for Fraud Detection. [PDF]

Entropy (Basel)
Li YC, Zhang YF, Xu RQ, Zhou RG, Dong YL.   +4 more
europepmc   +1 more source

Auditor Relational Strategy and Risk of Fraudulent Financial Statements

Asian Journal of Accounting and Governance
openaire   +1 more source

Optimizing credit card fraud detection with random forests and SMOTE.

Sci Rep
Sundaravadivel P, Isaac RA, Elangovan D, KrishnaRaj D, Rahul VVL, Raja R.   +5 more
europepmc   +1 more source

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Data Mining techniques for the detection of fraudulent financial statements

Expert Systems With Applications, 2007
This paper explores the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. In accomplishing the task of management fraud detection, auditors could be facilitated in their work by using Data Mining techniques. This
Efstathios Kirkos, Charalambos Spathis, Yannis Manolopoulos   +2 more
exaly   +3 more sources

Identification of fraudulent financial statements using linguistic credibility analysis

Decision Support Systems, 2011
The strategic use of deceptive language in managerial financial fraud is investigated with linguistic cues extracted from 202 publicly available financial disclosures. Those crafting fraudulent disclosures use more activation language, words, imagery, pleasantness, group references, and less lexical diversity than non-fraudulent ones.
Judee K. Burgoon, Mary B Burns
exaly   +2 more sources

Multi-instance Learning for Predicting Fraudulent Financial Statements

2008 Third International Conference on Convergence and Hybrid Information Technology, 2008
This paper explores the effectiveness of multi-instance learning techniques in detecting firms that issue fraudulent financial statements (FFS). For this reason, a number of experiments have been conducted using representative learning algorithms, which were trained using a data set of 164 fraud and non-fraud Greek firms.
Sotiris Kotsiantis, Dimitris Kanellopoulos   +1 more
exaly   +2 more sources

Semi-supervised forecasting of fraudulent financial statements

Proceedings of the 20th Pan-Hellenic Conference on Informatics, 2016
Prediction of potential fraudulent activities may prevent both the stakeholders and the appropriate regulatory authorities of national or international level from being deceived. The objective difficulties on collecting adequate data that are obsessed by completeness affects the reliability of the most supervised Machine Learning methods.
Stamatis Karlos, Nikos Fazakis, Sotiris Kotsiantis, Kyriakos N. Sgarbas   +3 more
openaire   +2 more sources