  With the development of high technology, the scope of fraud is increasing,
resulting in annual losses of billions of dollars worldwide. The preventive
protection measures become obsolete and vulnerable over time, so effective
detective tools are needed. In this paper, 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, which generalize the concepts of
Benford's Law for fraud anomalies detection. Our results showed that B-tests
and W-tests give a significant increase to the quality of our anti-fraud
models. The SpiderNet code is available at
https://github.com/aasmirnova24/SpiderNet
