TITLE:
Detection of Fraud Patterns in Accounting Accounts Using Data Mining Techniques
AUTHORS:
Alexander Báez Hernández, Debrayan Bravo Hidalgo
KEYWORDS:
Recognition, Financial Fraud, Tool, Data Mining
JOURNAL NAME:
Open Journal of Business and Management,
Vol.8 No.4,
July
20,
2020
ABSTRACT: 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.