A generative adversarial network–based method for generating negative financial samples

Zhaohui Zhang, Lijun Yang, Ligong Chen, Qiuwen Liu, Ying Meng, Pengwei Wang and Maozhen Li

International Journal of Distributed Sensor Networks, 2020, vol. 16, issue 2, 1550147720907053

Abstract: In financial anti-fraud field, negative samples are small and sparse with serious sample imbalanced problem. Generating negative samples consistent with original data to naturally solve imbalanced problem is a serious problem. This article proposes a new method to solve this problem. We introduce a new generation model, combined Generative Adversarial Network with Long Short-Term Memory network for one-dimensional negative financial samples. The characteristic association between transaction sequences can be learned by long short-term memory layer, and the generator covers real data distribution by the adversarial discriminator with time-sequence. Mapping data distribution to feature space is a common evaluation method of synthetic data; however, relationships between data attributes have been ignored in online transactions. We define a comprehensive evaluation method to evaluate the validity of generated samples from data distribution and attribute characteristics. Experimental results on real bank B2B transaction data show that the proposed model has higher overall ratings, which is 10% higher than traditional generation models. Finally, well-trained model is used to generate negative samples and form new dataset. The classification results on new datasets show that precision and recall are all higher than baseline models. Our work has a certain practical value and provides a new idea to solve imbalanced problem in whatever fields.

Keywords: Generative adversarial network; long short-term memory network; negative financial samples; evaluation method (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:16:y:2020:i:2:p:1550147720907053

DOI: 10.1177/1550147720907053

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