Spidernet: fully connected residual network for fraud detection

In this work, we propose a convolutional neural network architecture SpiderNet designed to solve fraud detection problems. We noticed that the principles of pooling and convolutional layers in neural networks are very similar to the way antifraud analysts work when conducting investigations. Moreover, the skip-connections used in neural networks make the usage of features of various power in antifraud models possible. Our experiments have shown that SpiderNet provides better quality compared to Random Forest and adapted for antifraud modeling problems 1D-CNN, 1D-DenseNet, F-DenseNet neural networks. We also propose new approaches for fraud feature engineering called B-tests and W-tests. The SpiderNet code is available at: https://github.com/aasmirnova24/SpiderNet.