Fraud detection within bankcard enrollment on mobile device based payment using machine learning

The rapid growth of mobile Internet technologies has induced a dramatic increase in mobile payments as well as concomitant mobile transaction fraud. As the first step of mobile transactions, bankcard enrollment on mobile devices has become the primary target of fraud attempts. Although no immediate financial loss is incurred after a fraud attempt, subsequent fraudulent transactions can be quickly executed and could easily deceive the fraud detection systems if the fraud attempt succeeds at the bankcard enrollment step. In recent years, financial institutions and service providers have implemented rule-based expert systems and adopted short message service (SMS) user authentication to address this problem. However, the above solution is inadequate to face the challenges of data loss and social engineering. In this study, we introduce several traditional machine learning algorithms and finally choose the improved gradient boosting decision tree (GBDT) algorithm software library for use in a real system, namely, XGBoost. We further expand multiple features based on analysis of the enrollment behavior and plan to add historical transactions in future studies. Subsequently, we use a real card enrollment dataset covering the year 2017, provided by a worldwide payment processor. The results and framework are adopted and absorbed into a new design for a mobile payment fraud detection system within the Chinese payment processor.

[1]  DJ Hand,et al.  Performance criteria for plastic card fraud detection tools , 2008, J. Oper. Res. Soc..

[2]  Gianluca Bontempi,et al.  Learned lessons in credit card fraud detection from a practitioner perspective , 2014, Expert Syst. Appl..

[3]  M. Krivko,et al.  A hybrid model for plastic card fraud detection systems , 2010, Expert Syst. Appl..

[4]  Hongfeng Chai,et al.  Research and practice on system engineering management of a mobile payment project , 2017 .

[5]  Cheng Wu,et al.  Layer-wise domain correction for unsupervised domain adaptation , 2018, Frontiers of Information Technology & Electronic Engineering.

[6]  Jianhua Li,et al.  Big Data Analysis-Based Secure Cluster Management for Optimized Control Plane in Software-Defined Networks , 2018, IEEE Transactions on Network and Service Management.

[7]  Björn E. Ottersten,et al.  Cost Sensitive Credit Card Fraud Detection Using Bayes Minimum Risk , 2013, 2013 12th International Conference on Machine Learning and Applications.

[8]  Hanqing Lu,et al.  Recent advances in efficient computation of deep convolutional neural networks , 2018, Frontiers of Information Technology & Electronic Engineering.

[9]  Divya Murli,et al.  Credit Card Fraud Detection Using Neural Networks , 2015 .

[10]  Liqing Zhang,et al.  Credit Card Fraud Detection Using Convolutional Neural Networks , 2016, ICONIP.

[11]  Björn E. Ottersten,et al.  Example-Dependent Cost-Sensitive Logistic Regression for Credit Scoring , 2014, 2014 13th International Conference on Machine Learning and Applications.

[12]  Xin Liu,et al.  A secure and high-performance multi-controller architecture for software-defined networking , 2016, Frontiers of Information Technology & Electronic Engineering.

[13]  Djamila Aouada,et al.  Feature engineering strategies for credit card fraud detection , 2016, Expert Syst. Appl..

[14]  Michael Granitzer,et al.  Sequence classification for credit-card fraud detection , 2018, Expert Syst. Appl..

[15]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[16]  Wei Yu,et al.  Transaction Fraud Detection Using GRU-centered Sandwich-structured Model , 2017, 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design ((CSCWD)).

[17]  David J. Hand,et al.  Statistical fraud detection: A review , 2002 .

[18]  Yu Hu,et al.  Towards human-like and transhuman perception in AI 2.0: a review , 2017, Frontiers of Information Technology & Electronic Engineering.

[19]  Siddhartha Bhattacharyya,et al.  Data mining for credit card fraud: A comparative study , 2011, Decis. Support Syst..

[20]  J. M. Serrano,et al.  Association rules applied to credit card fraud detection , 2009, Expert Syst. Appl..

[21]  David J. Hand,et al.  Statistical Classification Methods in Consumer Credit Scoring: a Review , 1997 .

[22]  Yutao Zhang,et al.  Consumer preference–enabled intelligent energy management for smart cities using game theoretic social tie , 2018, Int. J. Distributed Sens. Networks.