Feiyan Zhan
ABSTRACT
Bank fraud transaction has brought huge losses to consumers and banks, and the original rule-based fraud detection method is not suitable for various new fraud way. According to the fact that bank fraud transaction is a typical unbalanced data classification problem, two neural network models of DNN and LSTM are established, with a new type loss function, Focalloss is used to train and test on the Kaggle's TESTIMON Dataset. As the test results, LSTM-Focalloss's network was able to detect fraud transactions significantly higher than other methods, indicating that this network model is very effective in detecting bank fraud transactions.
- R Brause, T Langsdorf, M Hepp. 1999. Neural data mining for credit card fraud detection. IEEE International Conference on Tools with Artificial Intelligence, Chicago, IL. https://doi.org/10.1109/TAI.1999.809773Google Scholar
Cross Ref
- Shao H, zhao H, Chang G R. 2002. Applying data mining to detect fraud behavior in customs declaration, Machine Learning and Cybernetics, proceeding International Conference on. IEEE 2002, 3: 1241-1244, https://doi.org/10.1109/ICMLC.2002.1167400Google Scholar
- Y. Sahin, E. Duman. 2011. Detecting Credit Card Fraud by Decision Trees and Support Vector Machines, Proceeding of the International MultiConference of Engineers and Computer Scientists, IMECS 2011: 442-447.Google Scholar
- Robinson W N. Aria A. 2018. Sequential fraud detection for prepaid cards using hidden Markov model divergence. Expert Systems with Applications, 2018,91:235-251. https://doi.org/10.1016/j.eswa.2017.08.043Google ScholarCross Ref
- Jurgovsky J, Granitzer M, Ziegler K, 2018. Sequence classification for credit-card fraud detection. Expert Systems with Applications, 2018,100:234-245. https://doi.org/10.1016/j.eswa.2018.01.037Google ScholarCross Ref
- S. Benson Edwin Raj, A. Annie Portia. 2011. Analysis on credit card fraud detection methods. 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET). Tamilnadu, India. https://doi.org/10.1109/ICCCET.2011.5762457Google ScholarCross Ref
- Donglin L. Credit card fraud identification based on unbalanced data set based on fusion model[C]. 2019 IEEE 1st International Conference on Civil Aviation Safety and Information Technology (ICCASIT). https://doi.org/10.1109/ICCASIT48058.2019.8973167Google Scholar
- Tsung Y L, Priya G, Ross G, Kaiming H, 2017. Focal Loss for Dense Object Detection[C]. Conference: 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy. https://doi.org/10.1109/ICCV.2017.324Google Scholar
Recommendations
Fraud detection in credit card transaction using neural networks
SCA '19: Proceedings of the 4th International Conference on Smart City ApplicationsBanking transactions, such as online transactions, credit card transactions and the mobile wallet, are gaining popularity. People are shopping more and more using credit cards. Credit cards have become a necessity, to the virtual world, for digitized ...
Fraud Transaction Recognition: A Money Flow Network Approach
CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge ManagementIn this paper, we provide some insights into analysis of fraud transaction recognition on Alipay's Money Flow Network. We first show that the Money Flow Network follows a power-law distribution on daily, monthly or yearly basis, based on which we ...
Auto-Encoder and LSTM-Based Credit Card Fraud Detection
AbstractThe increased fraud risk due to the most recent methods of paying with a credit card, such as real-time payments and cards with near-field communication (NFC) capabilities, makes detecting credit card fraud an essential topic of study. Deep ...
Comments