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In this paper, several methods based on signal processing on graphs are proposed to improve the performance of credit card fraud detection. The proposed methods consist of a variant of the classic iterative amplitude adjusted Fourier transform (IAAFT) and two methods that we have called iterative surrogate signals on graph algorithms (ISSG). The objective is to generate surrogate samples from the...
This paper presents a signal processing framework for the problem of automatic credit card fraud detection. This is a critical problem affecting large financial companies that has increased due to the rapid expansion of information and communication technologies. The framework establishes relationships between signal processing and pattern recognition issues around a detection problem with a very...
Banks collect large amount of historical records corresponding to millions of credit cards operations, but, unfortunately, only a small portion, if any, is open access. This is because, e.g., the records include confidential customer data and banks are afraid of public quantitative evidence of existing fraud operations. This paper tackles this problem with the application of surrogate techniques to...
Fraud detection is a critical problem affecting large financial companies that has increased due to the growth in credit card transactions. This paper presents a new method for automatic detection of frauds in credit card transactions based on non-linear signal processing. The proposed method consists of the following stages: feature extraction, training and classification, decision fusion, and result...
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