Clustered SVD strategies in latent semantic indexing
Information Processing and Management: an International Journal
Using Multivariate Statistics (5th Edition)
Using Multivariate Statistics (5th Edition)
Data Mining techniques for the detection of fraudulent financial statements
Expert Systems with Applications: An International Journal
CRM Segmentation and Clustering Using SAS Enterprise Miner
CRM Segmentation and Clustering Using SAS Enterprise Miner
Design science in information systems research
MIS Quarterly
The impact of multinationality on firm value: A comparative analysis of machine learning techniques
Decision Support Systems
BizPro: Extracting and categorizing business intelligence factors from textual news articles
International Journal of Information Management: The Journal for Information Professionals
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A computational fraud detection model (CFDM) was proposed for detecting fraud in financial reporting. CFDM uses a quantitative approach on textual data. It incorporates techniques that use essentially all of information contained in the textual data for fraud detection. Extant work provides a foundation for detecting deception in high and low synchronicity computer-mediated communication (CMC). CFDM provides an analytical method that has the potential for automation. It was tested on the Management's Discussion and Analysis from 10-K filings and was able to distinguish fraudulent filings from non-fraudulent ones. CFDM can serve as a screening tool where deception is suspected.