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- W2188202354 abstract "Fraudulence is one of the most popular threats to a company. It is important for a company to detect frauds accurately as soon as possible, so as to protect investors’ and customers’ interests, and assure the company’s revenue. Accounting information systems contain large volume of data that can be analyzed and provide clues for fraud detection. Data mining can be used to analyze users’ behavior and patterns, and gives hints to managers about potential frauds. This paper proposes a hybrid knowledge base system for fraud detection, in which ant colony optimization and artificial neural networks are used and combined with experts’ knowledge to improve the performance of fraud detection. A system prototype is designed and tested, which illustrates the effectiveness of the approach." @default.
- W2188202354 created "2016-06-24" @default.
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- W2188202354 date "2014-01-01" @default.
- W2188202354 modified "2023-09-22" @default.
- W2188202354 title "A Hybrid Knowledge Base System for Fraud Detection Using Accounting Data Research-in-Progress" @default.
- W2188202354 hasPublicationYear "2014" @default.
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