Home // IMMM 2015, The Fifth International Conference on Advances in Information Mining and Management // View article
Authors:
Duarte Trigueiros
Carolina Sam
Keywords: Type of Information Mining; Knowledge Extraction; Accounting Fraud Mining
Abstract:
Considerable effort has been devoted to the development of Artificial Intelligence tools able to support the detection of fraudulent accounting reports. Published results are promising but, till the present date, the use of such research has been limited, due to the ``black box'' character of the developed tools and the cumbersome input task they require. The tool described in this paper solves both problems while improving specificity of diagnostics. It is based on Web Mining and on Multilayer Perceptron classifiers where a modified learning method leads to meaningful representations. Such representations are then input to a features' map where trajectories towards or away from fraud and other features are identified. The final result is a robust Web Mining-based, self-explanatory fraud detection tool.
Pages: 23 to 26
Copyright: Copyright (c) IARIA, 2015
Publication date: June 21, 2015
Published in: conference
ISSN: 2326-9332
ISBN: 978-1-61208-415-2
Location: Brussels, Belgium
Dates: from June 21, 2015 to June 26, 2015