文章基本信息

标题：Detection of Fraud Patterns in Accounting Accounts Using Data Mining Techniques
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作者：Alexander Báez Hernández ; Debrayan Bravo Hidalgo
期刊名称：Open Journal of Business and Management
印刷版ISSN：2329-3284
电子版ISSN：2329-3292
出版年度：2020
卷号：8
期号：4
页码：1609-1618
DOI：10.4236/ojbm.2020.84102
出版社：Scientific Research Publishing
摘要：Accounting databases with fraudulent transactions inside was used to detect fraud patterns by data mining tool. The object was accomplished by the following method: first, inside data, fraudulent transactions according to three fraud patterns were settled, over it the algorithms, Euclidian distance and local outlier factors were run using Rapidminer program. As a result the fraud patterns were shown in different ways according to the specific graphics contributed by the program. In conclusion, clusters grouping by Euclidian distance with k Means algorithm (k = 4) allowed an adequate visualization of the values’ distribution, as consequence was detected the first and third fraud patterns. The application of the outlier’s detection algorithm (LOF) detected the three fraud patterns in a clear way as a consequence of the insolate outliers in different graphics, shown by Rapidminer program, with different variables correlated.
关键词：Recognition;Financial Fraud;Tool;Data Mining