Itsy Bitsy SpiderNet: Fully Connected Residual Network for Fraud Detection

Abstract

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

arXiv comment12 pages, 10 figures, 4 tables

References in corpus (15)

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Improving neural networks by preventing co-adaptation of feature detectors
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• Improved Regularization of Convolutional Neural Networks with Cutout
• SGDR: Stochastic Gradient Descent with Warm Restarts
• Going Deeper with Convolutions
• A Deep Reinforced Model for Abstractive Summarization
• SQuAD: 100,000+ Questions for Machine Comprehension of Text
• FractalNet: Ultra-Deep Neural Networks without Residuals
• CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
• Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters
• Deep Networks with Stochastic Depth
• A Survey of Deep Learning for Scientific Discovery
• Spectrum-based deep neural networks for fraud detection
• Multi-Scale DenseNet-Based Electricity Theft Detection