The Growing Hierarchical Self-Organizing Map
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Camouflaged fraud detection in domains with complex relationships
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining techniques for the detection of fraudulent financial statements
Expert Systems with Applications: An International Journal
International Journal of Intelligent Systems in Accounting and Finance Management
Using GHSOM to construct legal maps for Taiwan's securities and futures markets
Expert Systems with Applications: An International Journal
The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data
IEEE Transactions on Neural Networks
Topological pattern discovery and feature extraction for fraudulent financial reporting
Expert Systems with Applications: An International Journal
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The issue of fraudulent financial reporting has drawn much public as well as academic attention. However, most relevant researches focus on predicting financial distress or bankruptcy. Little emphasis has been placed on exploring the financial reporting fraud itself. This study addresses the challenge of obtaining an enhanced understanding of the financial reporting fraud through the approach with the following four phases: (1) to identify a set of financial and corporate governance indicators that are significantly correlated with fraudulent financial reporting; (2) to use the Growing Hierarchical Self-Organizing Map (GHSOM) to cluster data from listed companies into fraud and non-fraud subsets; (3) to extract knowledge from the fraudulent financial reporting through observing the hierarchical relationship displayed in the trained GHSOM; and (4) to provide justification to the extracted knowledge.