2019/07/07 by Hung Ba, Ba, Hung
Business, Management and Accounting · Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Economics and business #Financial Distress and Bankruptcy Prediction #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Risk Management (q-fin.RM) #Vehicle License Plate Recognition
paper · pdf · doi:10.48550/arxiv.1907.03355
openalex publication_date 2019/07/07 · openalex created_date 2019/07/12 · openalex updated_date 2026/07/28
In this study, we employ Generative Adversarial Networks as an oversampling method to generate artificial data to assist with the classification of credit card fraudulent transactions. GANs is a generative model based on the idea of game theory, in which a generator G and a discriminator D are trying to outsmart each other. The objective of the generator is to confuse the discriminator. The objective of the discriminator is to distinguish the instances coming from the generator and the instances coming from the original dataset. By training GANs on a set of credit card fraudulent transactions, we are able to improve the discriminatory power of classifiers. The experiment results show that the Wasserstein-GAN is more stable in training and produce more realistic fraudulent transactions than the other GANs. On the other hand, the conditional version of GANs in which labels are set by k-means clustering does not necessarily improve the non-conditional versions of GANs.