Credit card fraud detection method and device based on clustering sample and limit gradient

Abstract

The invention provides a credit card fraud detection method and equipment based on a clustering sample and a limit gradient. The method comprises steps 1 to 8. According to the credit card fraud detection method, the clustering algorithm, the sample adaptive weight calculation and the oversampling algorithm are combined, the inter-class imbalance problem is solved, the intra-class imbalance problem is effectively avoided, the data quality of artificially synthesized samples is improved, the limit gradient boosting tree serves as a classifier in a credit card fraud detection model, and the classification efficiency of the credit card fraud detection model is improved. A better classification effect can be obtained, and after an oversampling algorithm based on clustering and adaptive weight is combined, the finally generated credit card fraud detection model has good detection accuracy.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of data mining, and in particular, to a method and device for detecting fraudulent credit cards based on clustered samples and limit gradients. Background technique [0002] Before the arrival of the era of big data and artificial intelligence, the establishment of credit card fraud detection models was completed by some traditional methods, including human detection, establishment of detection models based on expert rules, and cost analysis models, etc. However, these traditional methods are accurate Low rate, long detection time and other defects. With the rise and development of big data and artificial intelligence technologies, both traditional statistical-based machine learning algorithms and various deep learning methods that have emerged in recent years have been transformed by domestic and foreign researchers according to the characteristics of the credit card fraud field. appl...

AI Technical Summary

Problems solved by technology

A notable feature of credit card fraud datasets is data imbalance. Common machine learning algorithms, such as logistic regression algorithms and decision tree algorithms, are less effective when directly training such unbalanced datasets.

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