数字信用交易反欺诈研究进展

doi:10.3778/j.issn.1673-9418.2211087

摘要/Abstract

摘要： 数字技术的发展加速了金融在线支付方式的转变，带来支付便捷的同时却也增加了欺诈交易的隐患，反欺诈研究对保护用户财产、防范金融危机尤为重要。伴随数据治理与共享技术的进步，数字支付交易数据呈现海量、多源、异构的新特点，将基于大数据与人工智能的数据智能技术融入到反欺诈研究中具有重要的理论研究意义。信用卡支付与数字支付充分结合发展形成的数字信用支付模式，拥有当下最成熟的数据积累和理论基础，为反欺诈模型的研究提供了最理想的数据资源与理论支持。从概念出发，首先结合我国实际业务场景，对数字信用反欺诈研究问题的定义、研究难点、数据框架进行介绍；其次基于建模策略，分别从数据均衡和模型优化两方面对数字信用交易反欺诈研究的前沿进展进行综述，重点介绍了各类机器学习算法与深度学习算法在反欺诈研究中的理论基础、适用场景、最新成果，并基于上述内容展开综合评估；最后结合研究现状，从需求的角度切入，对包含反欺诈研究的泛化性、可解释性、面对新型欺诈交易模式敏感性在内的三大研究热点进行总结，并对未来的研究方向进行展望。

关键词: 数字信用支付, 欺诈交易识别, 数据智能, 不均衡分类

Abstract: The development of digital technology has accelerated the transformation of financial online payment methods, bringing convenience to payment but also increasing the hidden dangers of fraudulent transactions. Anti-fraud research is particularly essential to protect users’ property and prevent financial crises. With the advancement of data governance and sharing technology, digital payment transaction data present new characteristics of massive, multi-source and heterogeneous. Integrating data intelligence technology based on big data and artificial intelligence into anti-fraud research has important theoretical research significance. The digital credit payment model formed by the full combination of credit card payment and digital payment has the most mature data accumulation and theoretical basis at present, providing the most ideal data resources and theoretical support for the research of anti-fraud models. Starting from the concept, this paper firstly introduces the definition, research difficulties, and data framework of the digital credit anti-fraud research problem in combination with the actual business scenarios in China. Secondly, based on the modeling strategy, the frontier progress of digital credit transaction anti-fraud research is reviewed from two aspects of data balance and model optimization. This paper focuses on the theoretical basis, applicable scenarios, and latest achievements of various machine learning algorithms and deep learning algorithms in anti-fraud research, and based on the above content, a comprehensive evaluation is made. Finally, combined with the research status and from the perspective of demand, this paper summarizes the three major hotspots including the generalization and interpretability of anti-fraud research, and the sensitivity to new fraudulent transaction models, and concludes with an outlook on future research directions.

Key words: digital credit payment, fraudulent transaction identification, data intelligence, imbalance classification

刘华玲, 曹世杰, 许珺怡, 陈尚辉. 数字信用交易反欺诈研究进展[J]. 计算机科学与探索, 2023, 17(10): 2300-2324.

LIU Hualing, CAO Shijie, XU Junyi, CHEN Shanghui. Anti-fraud Research Advances on Digital Credit Payment[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(10): 2300-2324.

参考文献

[1] 周子衡. 数字经济发展三阶段: 数字支付、数字法币与数字财政[J]. 清华金融评论, 2018(4): 63-64.
ZHOU Z H. Three stages of digital economy development: digital payment, digital legal currency and digital finance[J]. Tsinghua Financial Review, 2018(4): 63-64.
[2] BANSAL A, GARG H. An efficient techniques for fraudulent detection in credit card dataset: a comprehensive study[J].IOP Conference Series: Materials Science and Engineering, 2021, 1116(1): 012181.
[3] 中国人民银行支付结算司. 2020年支付体系运行总体情况[J]. 金融会计, 2021(4): 77-80.
Payment and Settlement Department of the People’s Bank of China. The overall operation of the payment system in 2020[J]. Financial Accounting, 2021(4): 77-80.
[4] POPAT R R, CHAUDHARY J. A survey on credit card fraud detection using machine learning[C]//Proceedings of the 2018 2nd International Conference on Trends in Elec-tronics and Informatics, Tirunelveli, May 11-12, 2018. Piscataway: IEEE, 2018: 1120-1125.
[5] NIU X, WANG L, YANG X. A comparison study of credit card fraud detection: supervised versus unsupervised[J]. arXiv:1904.10604, 2019.
[6] MOHARI A, DOWERAH J, DAS K, et al. Credit card fraud detection techniques: a review[J]. Soft Computing for Intelligent Systems, 2021: 157-166.
[7] RYMAN-TUBB N F, KRAUSE P, GARN W. How artificial intelligence and machine learning research impacts payment card fraud detection: a survey and industry benchmark[J]. Engineering Applications of Artificial Intelligence, 2018, 76: 130-157.
[8] PANDEY K, SACHAN P, GANPATRAO N G. A review of credit card fraud detection techniques[C]//Proceedings of the 2021 5th International Conference on Computing Methodologies and Communication, Erode, Apr 8-10, 2021. Piscataway: IEEE, 2021: 1645-1653.
[9] AL-HASHEDI K G, MAGALINGAM P. Financial fraud detection applying data mining techniques: a comprehensive review from 2009 to 2019[J]. Computer Science Review, 2021, 40: 100402.
[10] 张远. 我国商业银行信用卡风险及其防范探析[J]. 征信,2020, 38(11): 87-92.
ZHANG Y. Analysis of credit card risks of commercial banks and their prevention in China[J]. Credit Reference, 2020, 38(11): 87-92.
[11] TAHA A A, MALEBARY S J. An intelligent approach to credit card fraud detection using an optimized light gradient boosting machine[J]. IEEE Access, 2020, 8: 25579-25587.
[12] 巴曙松, 侯畅, 唐时达. 大数据风控的现状, 问题及优化路径[J]. 金融理论与实践, 2016(2): 23-26.
BA S S, HOU C, TANG S D. Current situation, problems and optimization of the big data risk control[J]. Financial Theory & Practice, 2016(2): 23-26.
[13] CZARNECKI W M, RATAJ K. Compounds activity predic-tion in large imbalanced datasets with substructural relations fingerprint and EEM[C]//Proceedings of the 2015 IEEE Trustcom/BigDataSE/ISPA, Helsinki, Aug 20-22, 2015. Piscat-away: IEEE, 2015: 192.
[14] WEI W, LI J, CAO L, et al. Effective detection of sophisticated online banking fraud on extremely imbalanced data[J]. World Wide Web, 2013, 16(4): 449-475.
[15] CHAWLA N V, JAPKOWICZ N, KOTCZ A. Special issue on learning from imbalanced data sets[J]. ACM SIGKDD Explorations Newsletter, 2004, 6(1): 1-6.
[16] ZHANG X, HAN Y, XU W, et al. HOBA: a novel feature engineering methodology for credit card fraud detection with a deep learning architecture[J]. Information Sciences, 2021, 557: 302-316.
[17] GROVER A, LESKOVEC J. Node2vec: scalable feature learning for networks[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, Aug 13-17, 2016. New York: ACM, 2016: 855-864.
[18] WANG D, CUI P, ZHU W. Structural deep network embedding[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, Aug 13-17, 2016. New York: ACM, 2016: 1225-1234.
[19] VAN VLASSELAER V, BRAVO C, CAELEN O, et al. APATE: a novel approach for automated credit card transaction fraud detection using network-based extensions[J]. Decision Support Systems, 2015, 75: 38-48.
[20] 王成, 王昌琪. 一种面向网络支付反欺诈的自动化特征工程方法[J]. 计算机学报, 2020, 43(10): 1983-2001.
WANG C, WANG C Q. An automated feature engineering method for online payment fraud detection[J]. Chinese Journal of Computers, 2020, 43(10): 1983-2001.
[21] HAMILTON W, YING Z, LESKOVEC J. Inductive represen- tation learning on large graphs[C]//Advances in Neural Information Processing Systems 30, Long Beach, Dec 4-9, 2017: 1024-1034.
[22] ZHANG L, ZHANG D. Evolutionary cost-sensitive extreme learning machine[J]. IEEE Transactions on Neural Networks and Learning Systems, 2016, 28(12): 3045-3060.
[23] NAMI S, SHAJARI M. Cost-sensitive payment card fraud detection based on dynamic random forest and k-nearest neighbors[J]. Expert Systems with Applications, 2018, 110: 381-392.
[24] KHAN S, HAYAT M, BENNAMOUN M, et al. Cost-sensitive learning of deep feature representations from imbalanced data[J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(8): 3573-3587.
[25] CHAWLA N V, BOWYER K W, HALL L O, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16: 321-357.
[26] HE H, BAI Y, GARCIA E A, et al. ADASYN: adaptive synthetic sampling approach for imbalanced learning[C]//Proceedings of the 2008 International Joint Conference on Neural Networks, Part of the IEEE World Congress on Com- putational Intelligence, Hong Kong, China, Jun 1-6, 2008. Piscataway: IEEE, 2008: 1322-1328.
[27] BAABDULLAH T, ALZAHRANI A, RAWAT D B. On the comparative study of prediction accuracy for credit card fraud detection with imbalanced classifications[C]//Proceedings of the 2020 Spring Simulation Conference, Fairfax, May 18-21, 2020. Piscataway: IEEE, 2020: 1-12.
[28] ZHANG Z, HUANG S. Credit card fraud detection via deep learning method using data balance tools[C]//Proceedings of the 2020 International Conference on Computer Science and Management Technology, Shanghai, Nov 20-22, 2020. Piscataway: IEEE, 2020: 133-137.
[29] BATISTA G E, PRATI R C, MONARD M C. A study of the behavior of several methods for balancing machine learning training data[J]. ACM SIGKDD Explorations Newsletter, 2004, 6(1): 20-29.
[30] HAN H, WANG W Y, MAO B H. Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning[C]//LNCS 3644: Proceedings of the 2005 International Conference on Intelligent Computing, Hefei, Aug 23-26, 2005. Berlin, Heidelberg: Springer, 2005: 878-887.
[31] BUNKHUMPORNPAT C, SINAPIROMSARAN K, LUR-SINSAP C. Safe-Level-SMOTE: safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem[C]//LNCS 5476: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining, Thailand, Apr 23-30, 2009. Berlin, Heidelberg: Springer, 2009: 475-482.
[32] MACIEJEWSKI T, STEFANOWSKI J. Local neighbourhood extension of SMOTE for mining imbalanced data[C]//Proceedings of the 2011 IEEE Symposium on Compu-tational Intelligence and Data Mining, Paris, Apr 11-15, 2011. Piscataway: IEEE, 2011: 104-111.
[33] NAPIERALA K, STEFANOWSKI J. Types of minority class examples and their influence on learning classifiers from imbalanced data[J]. Journal of Intelligent Information Systems, 2016, 46(3): 563-597.
[34] 王芳, 吴文通, 张立立, 等. 邻域自适应SMOTE算法研究[J]. 计算机应用研究, 2021, 38(6): 1673-1677.
WANG F, WU W T, ZHANG L L, et al. Research on neighborhood adaptive SMOTE algorithm[J]. Application Research of Computers, 2021, 38(6): 1673-1677.
[35] 梅大成, 陈江, 郑涛. 边界与密度适应的SMOTE算法研究[J]. 计算机应用研究, 2022, 39(5): 1478-1482.
MEI D C, CHEN J, ZHENG T. Research on SMOTE algorithm based on boundary and density adaptation[J]. Application Research of Computers, 2022, 39(5): 1478-1482.
[36] 张忠良, 陈愉予, 唐佳怡, 等. 一种基于高斯过采样的集成学习算法[J]. 系统工程理论与实践, 2021, 41(2): 513-523.
ZHANG Z L, CHEN Y Y, TANG J Y, et al. An ensemble learning algorithm with Gaussian-based oversampling[J]. Systems Engineering-Theory & Practice, 2021, 41(2): 513-523.
[37] OMAR B, RUSTAM F, MEHMOOD A, et al. Minimizing the overlapping degree to improve class-imbalanced learning under sparse feature selection: application to fraud detection[J]. IEEE Access, 2021, 9: 28101-28110.
[38] GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Advances in Neural Infor-mation Processing Systems 27, Montreal, Dec 8-13, 2014: 2672-2680.
[39] FIORE U, DE S A, PERLA F, et al. Using generative adversarial networks for improving classification effectiveness in credit card fraud detection[J]. Information Sciences, 2019, 479: 448-455.
[40] 赵海霞, 石洪波, 武建, 等. 基于条件生成对抗网络的不平衡学习研究[J]. 控制与决策, 2021, 36(3): 619-628.
ZHAO H X, SHI H B, WU J, et al. Research on imbalanced learning based on conditional generative adversarial networks[J]. Control and Decision, 2021, 36(3): 619-628.
[41] XU W, LIU Y. An optimized SVM model for detection of fraudulent online credit card transactions[C]//Proceedings of the 2012 International Conference on Management of e-Commerce and e-Government, Beijing, Oct 20-21, 2012. Piscataway: IEEE, 2012: 14-17.
[42] MAREESWARI V, GUNASEKARAN G. Prevention of credit card fraud detection based on HSVM[C]//Proceedings of the 2016 International Conference on Information Com-munication and Embedded Systems, India, Feb 25-26, 2016. Piscataway: IEEE, 2016: 1-4.
[43] GYAMFI N K, ABDULAI J D. Bank fraud detection using support vector machine[C]//Proceedings of the 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, Vancouver, Nov 1-3, 2018. Piscataway: IEEE, 2018: 37-41.
[44] ITOO F, SINGH S. Comparison and analysis of logistic regression, Na?ve Bayes and KNN machine learning algorithms for credit card fraud detection[J]. International Journal of Information Technology, 2021, 13(4): 1503-1511.
[45] ZHANG A, LIPTON Z C, LI M, et al. Dive into deep learning[J]. arXiv:2106.11342, 2021.
[46] RANDHAWA K, LOO C K, SEERA M, et al. Credit card fraud detection using AdaBoost and majority voting[J]. IEEE Access, 2018, 6: 14277-14284.
[47] GHOBADI F, ROHANI M. Cost sensitive modeling of credit card fraud using neural network strategy[C]//Pro-ceedings of the 2016 2nd International Conference of Signal Processing and Intelligent Systems, Tehran, Dec 14-15, 2016. Piscataway: IEEE, 2016: 1-5.
[48] 杨莲, 石宝峰. 基于Focal Loss修正交叉熵损失函数的信用风险评价模型及实证[J]. 中国管理科学, 2022, 30(5): 65-75.
YANG L, YANG B F. Credit risk evaluation model and empirical research based on focal loss modified cross-entropy loss function[J]. Chinese Journal of Management Science, 2022, 30(5): 65-75.
[49] 张军阳, 王慧丽, 郭阳, 等. 深度学习相关研究综述[J]. 计算机应用研究, 2018, 35(7): 1921-1928.
ZHANG J Y, WANG H L, GUO Y, et al. Review of deep learning[J]. Application Research of Computers, 2018, 35(7): 1921-1928.
[50] 肖京, 王磊, 杨余久, 等. 感知认知技术在金融风险预警中的应用研究[J]. 智能系统学报, 2021, 16(5): 941-961.
XIAO J, WANG L, YANG Y J, et al. A systematic review of perceptual cognitive technology and its application in the field of financial risk early warning[J]. CAAI Transactions on Intelligent Systems, 2021, 16(5): 941-961.
[51] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
[52] CHO K, VAN Z B, BAHDANAU D, et al. On the properties of neural machine translation: encoder-decoder approaches[J]. arXiv:1409.1259, 2014.
[53] OWOLAFE O, OGUNRINDE O B, THOMPSON A F B. A long short term memory model for credit card fraud detection[M]//Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities. Cham: Springer, 2021: 369-391.
[54] ROY A, SUN J, MAHONEY R, et al. Deep learning detecting fraud in credit card transactions[C]//Proceedings of the 2018 Systems and Information Engineering Design Symposium, Charlottesville, Apr 27, 2018. Piscataway: IEEE, 2018: 129-134.
[55] BENCHAJI I, DOUZI S, EL OUAHIDI B, et al. Enhanced credit card fraud detection based on attention mechanism and LSTM deep model[J]. Journal of Big Data, 2021, 8(1): 1-21.
[56] 刘颖, 杨轲. 基于深度集成学习的类极度不均衡数据信用欺诈检测算法[J]. 计算机研究与发展, 2021, 58(3): 539-547.
LIU Y, YANG K. Credit fraud detection for extremely imbalanced data based on ensembled deep learning[J]. Journal of Computer Research and Development, 2021, 58(3): 539-547.
[57] FOROUGH J, MOMTAZI S. Ensemble of deep sequential models for credit card fraud detection[J]. Applied Soft Computing, 2021, 99: 106883.
[58] EL BOUCHTI A, CHAKROUN A, ABBAR H, et al. Fraud detection in banking using deep reinforcement learning[C]//Proceedings of the 2017 7th International Conference on Innovative Computing Technology, Luton, Aug 16-18, 2017. Piscataway: IEEE, 2017: 58-63.
[59] COVER T, HART P. Nearest neighbor pattern classification[J]. IEEE Transactions on Information Theory, 1967, 13(1): 21-27.
[60] MALINI N, PUSHPA M. Analysis on credit card fraud identification techniques based on KNN and outlier detection[C]//Proceedings of the 2017 3rd International Conference on Advances in Electrical, Electronics, Information, Com-munication and Bio-Informatics, Chennai, Feb 27-28, 2017. Piscataway: IEEE, 2017: 255-258.
[61] AWOYEMI J O, ADETUNMBI A O, OLUWADARE S A. Credit card fraud detection using machine learning techniques: a comparative analysis[C]//Proceedings of the 2017 Inter-national Conference on Computing Networking and Infor-matics, Lagos, Oct 29-31, 2017. Piscataway: IEEE, 2017: 1-9.
[62] DIGHE D, PATIL S, KOKATE S. Detection of credit card fraud transactions using machine learning algorithms and neural networks: a comparative study[C]//Proceedings of the 2018 4th International Conference on Computing Com- munication Control and Automation, Pune, Aug 16-18, 2018. Piscataway: IEEE, 2018: 1-6.
[63] ?Z?ELIK M H, DUMAN E, I?IK M, et al. Improving a credit card fraud detection system using genetic algorithm[C]//Proceedings of the 2010 International Conference on Networking and Information Technology, Manila, Jun 11-12, 2010. Piscataway: IEEE, 2010: 436-440.
[64] OLABODE A O. Performance evaluation of genetic algorithm and counter propagation neural network for anomaly detection in online transaction[J]. Annals. Computer Science Series, 2019, 17(2).
[65] ALOTAIBI A, ALQHTANI M, ALZAIDI A, et al. Identifying credit card fraud using genetic and clonal selection algorithms[J]. International Journal of Innovative Science, Engineering & Technology, 2019, 6(12).
[66] BRABAZON A, CAHILL J, KEENAN P, et al. Identifying online credit card fraud using artificial immune systems[C]//Proceedings of the 2010 IEEE Congress on Evolutionary Computation, Barcelona, Jul 18-23, 2010. Piscataway: IEEE, 2010: 1-7.
[67] BENCHAJI I, DOUZI S, EL O B. Using genetic algorithm to improve classification of imbalanced datasets for credit card fraud detection[C]//Proceedings of the 2018 International Conference on Advanced Information Technology, Services and Systems, Mohammedia, Oct 17-18, 2018. Cham: Springer, 2018: 220-229.
[68] KHO J R D, VEA L A. Credit card fraud detection based on transaction behavior[C]//Proceedings of the TENCON 2017— 2017 IEEE Region 10 Conference, Penang, Nov 5-8, 2017. Piscataway: IEEE, 2017: 1880-884.
[69] BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.
[70] ZAREAPOOR M, SHAMSOLMOALI P. Application of credit card fraud detection: based on bagging ensemble classifier[J]. Procedia Computer Science, 2015, 48: 679-685.
[71] XUAN S, LIU G, LI Z, et al. Random forest for credit card fraud detection[C]//Proceedings of the 2018 IEEE 15th International Conference on Networking, Sensing and Control, Zhuhai, Mar 27-29, 2018. Piscataway: IEEE, 2018: 1-6.
[72] CHEN T, GUESTRIN C. Xgboost: a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD Inter-national Conference on Knowledge Discovery and Data Mining, San Francisco, Aug 13-17, 2016. New York: ACM, 2016: 785-794.
[73] KE G, MENG Q, FINLEY T, et al. LightGBM: a highly efficient gradient boosting decision tree[C]//Advances in Neural Information Processing Systems 30, Long Beach, Dec 4-9, 2017: 3146-3154.
[74] 陈荣荣, 詹国华, 李志华. 基于XGBoost算法模型的信用卡交易欺诈预测研究[J]. 计算机应用研究, 2020, 37(S1):111-112.
CHEN R R, ZHAN G H, LI Z H. Research on credit card transaction fraud prediction based on XGBoost algorithm model[J]. Application Research of Computers, 2020, 37(S1): 111-112.
[75] 张涛, 汪御寒, 李凯, 等. 基于样本依赖代价矩阵的小微企业信用评估方法[J]. 同济大学学报（自然科学版）, 2020, 48(1): 149-158.
ZHANG T, WANG Y H, LI K, et al. Credit scoring of small and micro enterprises based on sample-dependent cost matrix[J]. Journal of Tongji University (Natural Science), 2020, 48(1): 149-158.
[76] 陈芮, 杨新, 罗珺方, 等. 基于GANs-LightGBM的序贯三支异常用户检测研究[J]. 重庆邮电大学学报(自然科学版), 2021, 33(5): 816-825.
CHEN R, YANG X, LUO J F, et al. Research on sequential three-way abnormal user detection based on GANs-LightGBM[J]. Journal of Chongqing University of Posts and Telecom-munications (Natural Science Edition), 2021, 33(5): 816-825.
[77] KHAN A, SINGH T, SINHAL A. Implement credit card fraudulent detection system using observation probabilistic in hidden Markov model[C]//Proceedings of the 2012 Nirma University International Conference on Engineering, Ahmedabad, Dec 6-8, 2012. Piscataway: IEEE, 2012: 1-6.
[78] BHUSARI V, PATIL S. Study of hidden Markov model in credit card fraudulent detection[C]//Proceedings of the 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare, Coimbatore, Feb 29-Mar 1, 2016. Piscataway: IEEE, 2016: 1-4.
[79] WANG X, WU H, YI Z. Research on bank anti-fraud model based on K-means and hidden Markov model[C]//Proceedings of the 2018 IEEE 3rd International Conference on Image, Vision and Computing, Chongqing, Jun 27-29, 2018. Piscataway: IEEE, 2018: 780-784.
[80] 张树森, 梁循, 齐金山. 社会网络角色识别方法综述[J]. 计算机学报, 2017, 40(3): 649-673.
ZHANG S S, LIANG X, QI J S. A review on role identification methods in social networks[J]. Chinese Journal of Computers, 2017, 40(3): 649-673.
[81] YANG Y, XU Y, WANG C, et al. Understanding default behavior in online lending[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, Nov 3-7, 2019. New York: ACM, 2019: 2043-2052.
[82] 金利娜, 于炯, 杜旭升, 等. 基于生成对抗网络和变分自编码器的离群点检测算法[J]. 计算机应用研究, 2022, 39(3): 774-779.
JIN L N, YU J, DU X S, et al. Generative adversarial network and variational auto-encoder based outlier detection[J]. Application Research of Computers, 2022, 39(3): 774-779.
[83] RAI A K, DWIVEDI R K. Fraud detection in credit card data using unsupervised machine learning based scheme[C]//Proceedings of the 2020 International Conference on Electronics and Sustainable Communication Systems, Coim- batore, Jul 2-4, 2020. Piscataway: IEEE, 2020: 421-426.
[84] ZHENG M, ZHOU C, WU J, et al. FraudNE: a joint embedding approach for fraud detection[C]//Proceedings of the 2018 International Joint Conference on Neural Networks, Rio de Janeiro, Jul 8-13, 2018. Piscataway: IEEE, 2018: 1-8.
[85] PUMSIRIRAT A, YAN L. Credit card fraud detection using deep learning based on auto-encoder and restricted Boltzmann machine[J]. International Journal of Advanced Computer Science and Applications, 2018, 9(1): 18-25.
[86] SELIYA N, ABDOLLAH ZADEH A, KHOSHGOFTAAR T M. A literature review on one-class classification and its potential applications in big data[J]. Journal of Big Data, 2021, 8(1): 1-31.
[87] JERAGH M, ALSULAIMI M. Combining auto encoders and one class support vectors machine for fraudulant credit card transactions detection[C]//Proceedings of the 2018 2nd World Conference on Smart Trends in Systems, Security and Sustainability, London, Oct 30-31, 2018. Piscataway: IEEE, 2018: 178-184.
[88] KHAN S S, TAATI B. Detecting unseen falls from wearable devices using channel-wise ensemble of autoencoders[J]. Expert Systems with Applications, 2017, 87: 280-290.
[89] ZHENG P, YUAN S, WU X, et al. One-class adversarial nets for fraud detection[C]//Proceedings of the 33rd AAAI Conference on Artificial Intelligence, the 31st Innovative Applications of Artificial Intelligence Conference, the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, Honolulu, Jan 27-Feb 1, 2019. Menlo Park:AAAI, 2019: 1286-1293.
[90] LEBICHOT B, BRAUN F, CAELEN O, et al. A graph-based, semi-supervised, credit card fraud detection system[C]//Proceedings of the 2017 International Workshop on Complex Networks and Their Applications. Cham: Springer, 2017: 721-733.
[91] SALAZAR A, SAFONT G, VERGARA L. Semi-supervised learning for imbalanced classification of credit card transaction[C]//Proceedings of the 2018 International Joint Conference on Neural Networks, Rio de Janeiro, Jul 8-13, 2018. Piscataway: IEEE, 2018: 1-7.
[92] XIAO J, ZHOU X, ZHONG Y, et al. Cost-sensitive semi-supervised selective ensemble model for customer credit scoring[J]. Knowledge-Based Systems, 2020, 189: 105-118.
[93] CHARITOU C, GARCEZ A A, DRAGICEVIC S. Semi-supervised GANs for fraud detection[C]//Proceedings of the 2020 International Joint Conference on Neural Networks, Glasgow, Jul 19-24, 2020. Piscataway: IEEE, 2020: 1-8.
[94] LEBICHOT B, VERHELST T, LE BORGNE Y A, et al. Transfer learning strategies for credit card fraud detection[J]. IEEE Access, 2021, 9: 114754-114766.
[95] ELSAYED G F, GOODFELLOW I, SOHL-DICKSTEIN J. Adversarial reprogramming of neural networks[J]. arXiv:1806.11146, 2018.
[96] CHEN L, FAN Y, YE Y. Adversarial reprogramming of pretrained neural networks for fraud detection[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Queensland, Nov 1-5, 2021. New York: ACM, 2021: 2935-2939.
[97] 吴俊杰, 刘冠男, 王静远, 等. 数据智能: 趋势与挑战[J]. 系统工程理论与实践, 2020, 40(8): 2116-2149.
WU J J, LIU G N, WANG J Y, et al. Data intelligence: trends and challenges[J]. Systems Engineering-Theory & Practice, 2020, 40(8): 2116-2149.
[98] RIBEIRO M T, SINGH S, GUESTRIN C. “Why should I trust you?” Explaining the predictions of any classifier[C]//Proceedings of the 22nd ACM SIGKDD International Con-ference on Knowledge Discovery and Data Mining, San Francisco, Aug 13-17, 2016. New York: ACM, 2016: 1135-1144.
[99] BINDER A, BACH S, MONTAVON G, et al. Layer-wise relevance propagation for deep neural network architectures[C]//Proceedings of the 2016 International Conference on Information Science and Applications. Cham: Springer, 2016: 913-922.
[100] WU T, WANG Y. Locally Interpretable one-class anomaly detection for credit card fraud detection[J]. arXiv:2108. 02501, 2021.
[101] 董路安, 叶鑫. 基于改进教学式方法的可解释信用风险评价模型构建[J]. 中国管理科学, 2020, 28(9): 45-53.
DONG L A, YE X. Interpretable credit risk assessment modeling based on improved pedagogical method[J]. Chinese Journal of Management Science, 2020, 28(9): 45-53.
[102] CARCILLO F, LE BORGNE Y A, CAELEN O, et al. Combining unsupervised and supervised learning in credit card fraud detection[J]. Information Sciences, 2021, 557: 317-331.
[103] MICENKOVá B, MCWILLIAMS B, ASSENT I. Learning outlier ensembles: the best of both worlds—supervised and unsupervised[C]//Proceedings of the ACM SIGKDD 2014 Workshop on Outlier Detection and Description under Data Diversity, New York, Aug 24, 2014. New York: ACM, 2014: 51-54.
[104] KAIROUZ P, MCMAHAN H B, AVENT B, et al. Advances and open problems in federated learning[J]. Foundations and Trends? in Machine Learning, 2021, 14(1/2).
[105] 郭艳卿, 王鑫磊, 付海燕, 等. 面向隐私安全的联邦决策树算法[J]. 计算机学报, 2021, 44(10): 2090-2103.
GUO Y Q, WANG X L, FU H Y, et al. Federated decision tree algorithm for privacy security[J]. Chinese Journal of Computers, 2021, 44(10): 2090-2103.