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KNN-Smote-LSTM based consumer financial risk detection model: a case credit card fraud detection. (Chinese. English summary) Zbl 1474.91231

Summary: As the number of mobile web applications and electronic payment services continues to grow, the situation of credit card fraud has also shown a rapid growth trend, which has brought enormous challenges to financial institutions and operators. The fraud detection problem is essentially an unbalanced sequence two-class problem. The data samples of this type are large in scale, high in computational complexity, extremely uneven in data distribution, and there is a sequence relationship between data and data. This paper uses long short term memory networks (LSTM) combined with historical transaction sequences, and integrates the synthetic minority oversampling technique (Smote) algorithm and k nearest neighbors (kNN) for the characteristics of transaction data imbalance. The classification algorithm designed and constructed a credit card fraud detection network model based on kNN-Smote-LSTM, which can continuously filter out safe generated samples to improve the performance of the model through kNN discriminant classifier. The blindness and limitations of the Smote algorithm in generating new samples demonstrate that the structured fusion of the kNN-Smote-LSTM model can greatly improve the misclassification of most classes in the model combination, and demonstrate superior risk detection performance.

MSC:

91G40 Credit risk
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
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