A Review of Anomalies Detection Based on Association Rules Techniques

Sadgali, Imane; Sael, Nawal; Benabbou, Faouzia

doi:10.1007/978-3-030-66840-2_88

Cited by 1 publication

(1 citation statement)

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“…Through our state of art [23], we noticed that fuzzy association rules are more suitable for the behavior layer than other techniques. That was confirmed in our pervious study and argute our choice [24]. The use of rough set theory model with fuzzy association rules technique will be a plus, since in other contexts it improves detection rate [15].…”

Section: Proposed Approachsupporting

confidence: 73%

Human behavior scoring in credit card fraud detection

Sadgali¹,

Sael²,

Benabbou³

2021

IJ-AI

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<span lang="EN-US">Now days, the analysis of the behavior of cardholders is one of the important fields in electronic payment. This kind of analysis helps to extract behavioral and transaction profile patterns that can help financial systems to better protect their customers. In this paper, we propose an intelligent machine learning (ML) system for rules generation. It is based on a hybrid approach using rough set theory for feature selection, fuzzy logic and association rules for rules generation. A score function is defined and computed for each transaction based on the number of rules, that make this transaction suspicious. This score is kind of risk factor used to measure the level of awareness of the transaction and to improve a card fraud detection system in general. The behavior analysis level is a part of a whole financial fraud detection system where it is combined to intelligent classification to improve the fraud detection. In this work, we also propose an implementation of this system integrating the behavioral layer. The system results obtained are very convincing and the consumed time by our system, per transaction was 6 ms, which prove that our system is able to handle real time process.</span>

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Section: Proposed Approachsupporting

confidence: 73%