Credit card fraud detection method and system based on undersampling, medium and equipment

Abstract

The invention provides a credit card fraud detection method and system based on undersampling, a medium and equipment. The method comprises: fitting majority class samples of a training set in a dataset by using a Gaussian mixture model; predicting probability density values of minority class samples in the training set by using the fitted Gaussian mixture model, and selecting a maximum value inthe probability density values as a cross edge of the two classes of samples; taking the cross edge as a center, extending upwards and downwards from the cross edge to set a sampling upper bound and asampling lower bound so as to carry out undersampling to obtain an undersampling data set, and combining the undersampling data set with the minority class sample set to form an equalization trainingset; training a machine learning classifier according to the balanced training set; and detecting a credit card transaction data set by using the trained machine learning classifier. The Gaussian mixture model is used for grabbing the samples with the two types of samples distributed at the crossed edges, more useful information is provided for recognition of the two types of samples, and the recognition accuracy of the classifier in the field of credit card fraud detection is improved.

Description

technical field [0001] The invention relates to a credit card fraud detection method, in particular to a credit card fraud detection method, system, medium and equipment based on undersampling. Background technique [0002] In recent years, with the popularization of the Internet and mobile Internet, e-commerce has developed rapidly, business has become more and more diverse and convenient, and the volume of online transactions has increased sharply. new risks. For the loopholes in the electronic trading platform or the use of certain means to conduct electronic transaction fraud incidents frequently occur, the security of electronic transactions is constantly threatened, which seriously endangers the property safety of the country and citizens. Therefore, it is necessary to detect electronic transaction fraud and build a safe and credible electronic transaction platform. [0003] To solve the increasingly serious problem of e-commerce fraud, many machine learning solution...

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Problems solved by technology

[0005] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a credit card fraud detection method based on undersampling, a system, a medium, and a device, which are used to solve the problem of selecting most samples by traditional undersampling techniques in the prior art. problem of blindness

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