AIRCC's International Journal of Computer Science and Information Technology

Bruno Carneiro da Rocha ¹, Rafael Timoteo de Sousa ²

Affiliations
1 Bank of Brazil, Brasilia-DF, BR
2 Network Engineering Laboratory, University of Brasilia (UnB), Brasilia-DF, BR

Abstract

This article aims to evaluate the use of techniques of decision trees, in conjunction with the management model CRISP-DM, to help in the prevention of bank fraud. This article offers a study on decision trees, an important concept in the field of artificial intelligence. The study is focused on discussing how these trees are able to assist in the decision making process of identifying frauds by the analysis of information regarding bank transactions. This information is captured with the use of techniques and the CRISP-DM management model of data mining in large operational databases logged from internet bank transactions.

Keywords

Fraud Detection, Fraud Prevention, Decision Taking, Machine Learning, Decision Trees, Data Mining.

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