Abstract

A convolutional neural network architecture SpiderNet designed for fraud detection has been proposed. The principles of pooling and convolutional layers in neural networks are very similar to the methods used by antifraud analysts in their research. In addition, the skip-connections used in neural networks make it possible to use features of various power in antifraud models. Our experiments have shown that SpiderNet provides better quality compared to Random Forest, CNN, DenseNet, and F-DenseNet (adapted for antifraud modeling problems) neural networks. Also, new approaches for anti-fraud rules (B-tests and W-tests) have been proposed. The SpiderNet code is available at https://github.com/aasmirnova24/SpiderNet.

中文翻译：

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SpiderNet：用于欺诈检测的全连接残差网络

摘要

提出了一种专为欺诈检测而设计的卷积神经网络架构 SpiderNet。神经网络中池化层和卷积层的原理与反欺诈分析师在研究中使用的方法非常相似。此外，神经网络中使用的跳跃连接使得在反欺诈模型中使用各种功率的特征成为可能。我们的实验表明，与随机森林、CNN、DenseNet 和 F-DenseNet（适用于反欺诈建模问题）神经网络相比，SpiderNet 提供了更好的质量。此外，还提出了反欺诈规则的新方法（B 测试和 W 测试）。 SpiderNet 代码可在 https://github.com/aasmirnova24/SpiderNet 上获取。