| Title | A StackNet Based Model for Fraud Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Chen, Liiie, Guan, Qihan, Chen, Ning, YiHang, Zhou |
| Conference Name | 2021 2nd International Conference on Education, Knowledge and Information Management (ICEKIM) |
| Keywords | CatBoost, Companies, compositionality, Credit cards, expandability, Forestry, fraud detection, GradientBoosting, Information management, LightGBM, Measurement, network architecture, pubcrawl, Random Forest, Resiliency, Stacked, Training |
| Abstract | With the rapid development of e-commerce and the increasing popularity of credit cards, online transactions have become increasingly smooth and convenient. However, many online transactions suffer from credit card fraud, resulting in huge losses every year. Many financial organizations and e-commerce companies are devoted to developing advanced fraud detection algorithms. This paper presents an approach to detect fraud transactions using the IEEE-CIS Fraud Detection dataset provided by Kaggle. Our stacked model is based on Gradient Boosting, LightGBM, CatBoost, and Random Forest. Besides, implementing StackNet improves the classification accuracy significantly and provides expandability to the network architecture. Our final model achieved an AUC of 0.9578 for the training set and 0.9325 for the validation set, demonstrating excellent performance in classifying different transaction types. |
| DOI | 10.1109/ICEKIM52309.2021.00079 |
| Citation Key | chen_stacknet_2021 |
A StackNet Based Model for Fraud Detection