基于HMM模型的信用卡欺骗风险检测系统的仿真分析
摘要
随着货币的电子化发展和电子商务的高速发展,信用卡在银行业务中所占的比例越来越大,同时,与信用卡相关的诈骗行为也越来越多,严重扰乱了正常的金融秩序,给银行和持卡人造成了很大的损失。
本文仔细分析了信用卡(虚拟卡)交易的特点,发现隐马尔可夫模型能够有效的发现其内部隐藏的、潜在的异常交易模式。
本文中构建的隐马尔可夫模型,将信用卡交易过程中不同的消费情况作为HMM模型中的随机过程,将交易金额的范围作为可观察到的输出概率矩阵,商品的类型作为模型中的状态,同时,利用客户的消费习惯来确定初始状态向量。使得HMM模型能够很好的应用到信用卡检测系统中。
隐马尔科夫模型的建立是立足于持卡人的消费习惯的,训练阶段是脱机完成,检测阶段是在线进行的。并且在检测阶段不断修正,既能很好的检测出信用卡欺诈风险,又能适应持卡人消费习惯的改变。
As the currency of electronic development and rapid development of e-commerce, credit cards in the banking business, a growing proportion of the same time, and the credit card-related fraud are also increasing, seriously disrupting the normal financial order, to the banks and caused great loss to the cardholder.
This careful analysis of the credit card (virtual card) transaction characteristics, found that hidden Markov models can effectively discover its internal hidden potential unusual trading patterns.
In this article build hidden Markov model, the process of credit card transactions on the spending of the different HMM models as a random process, the scope of the transaction amount, as can be observed in the output probability matrix, the type of goods as a model state, At the same time, the use of customer's spending habits to determine the initial state vector. Makes the HMM model can be applied to credit card a good test system.
Hidden Markov model is based on the cardholder's spending habits, the training phase is completed offline detection stage is conducted on-line. And in testing phase of constant updating in a very good detection of both the risk of credit card fraud, but also to adapt to changes in cardholder spending habits.
本文仔细分析了信用卡(虚拟卡)交易的特点,发现隐马尔可夫模型能够有效的发现其内部隐藏的、潜在的异常交易模式。
本文中构建的隐马尔可夫模型,将信用卡交易过程中不同的消费情况作为HMM模型中的随机过程,将交易金额的范围作为可观察到的输出概率矩阵,商品的类型作为模型中的状态,同时,利用客户的消费习惯来确定初始状态向量。使得HMM模型能够很好的应用到信用卡检测系统中。
隐马尔科夫模型的建立是立足于持卡人的消费习惯的,训练阶段是脱机完成,检测阶段是在线进行的。并且在检测阶段不断修正,既能很好的检测出信用卡欺诈风险,又能适应持卡人消费习惯的改变。
As the currency of electronic development and rapid development of e-commerce, credit cards in the banking business, a growing proportion of the same time, and the credit card-related fraud are also increasing, seriously disrupting the normal financial order, to the banks and caused great loss to the cardholder.
This careful analysis of the credit card (virtual card) transaction characteristics, found that hidden Markov models can effectively discover its internal hidden potential unusual trading patterns.
In this article build hidden Markov model, the process of credit card transactions on the spending of the different HMM models as a random process, the scope of the transaction amount, as can be observed in the output probability matrix, the type of goods as a model state, At the same time, the use of customer's spending habits to determine the initial state vector. Makes the HMM model can be applied to credit card a good test system.
Hidden Markov model is based on the cardholder's spending habits, the training phase is completed offline detection stage is conducted on-line. And in testing phase of constant updating in a very good detection of both the risk of credit card fraud, but also to adapt to changes in cardholder spending habits.
引文
[1].D.R.JESKE,D.V.GOKHALE,L.YE,Generating synthetic data from marginal fitting for testing the efficacy of data-mining tools. Re vision received January 2006.
[2]弋涛,信用卡风险管理研究,学位论文,西南财经大学,2006.
[3]贾宾,朱小燕,罗予频,胡东成.消除溢出问题的精确Baum-Welch算法.软件学报.2000,11(5)：707-710.
[4]Balazs Balasko, Janos Abonyi and Balazs Feil.Fuzzy Clustering and Data Analysis Toolbox—For Use with Matlab. www.codesoso.com
[5]Lawrence R.Rabiner.A Tutorial on Hidden Markov Models and Select Applications in Speech Recognition.IEEE,vol.77,no.2,1989
[6]汪一亭,魏臻.基于Markov模型的离散事件系统稳态与暂态的分析.计算机工程与应用.2009,45(3).
[7]杜世平.混合二阶隐马尔可夫模型的Baum-Welch算法.云南大学学报(自然科学版).2006,28(2)：95-102.
[8]杜世平.混合马尔可夫模型Baum-Welch算法的改进.广西师范学院学报(自然科学版).2005年9月,第22卷第3期.
[9].王岳斌,阳国贵,邝祝芳.基于HMM的数据库异常检测系统设计与实现.计算机应用与软件.2009年1月,第26卷第1期.
[10].乔佩利,张海霞,王艳丽.一种基于隐马尔可夫模型的实时安全评估方法.哈尔滨理工大学学报.2008年12月,第13卷第6期.
[11]徐洪丽,钱旭,刘绍翰.一种基于隐马尔科夫链的网络入侵检测研究.山东农业大学学报(自然科学版),2008,39(2)：648-552.
[12]刘雨.一种基于聚类的Markov预测模型.平顶山工学院学报.2008年11月,第17卷第6期.
[13]张友志,胡国胜,程玉胜.一种改进的Markov预测模型方法.计算机技术与发展.2008年12月第18卷第12期.
[14]Kokkinaki A,On Atypical Database Transactions:Identification of Probable Frauds using Machine Learning for User Profiling, Proc. of IEEE Knowledge and Data Engineering Exchange Workshop,1997:107-113.
[15]Chiu C & Tsai C,A Web Services-Based Collaborative Scheme for Credit Card Fraud Detection,Proc.of 2004 IEEE International Conference on e-Technology,e-Commerce and e-Service,2004.
[16]Ghosh S.& Reilly D.,Credit Card Fraud Detection with a Neural Network, Proc. of 27th Hawaii International Conference on Systems Science,1994(03):621-630.
[17]Aleskerov E.,Freisleben B.& Rao B.,CARDWATCH:A Neural Network-Based Database Mining System for Credit Card Fraud Detection,Proc.of the IEEE/IAFE on ComputationalIntelligence for Financial Engineering,1997:220-226.
[18]Syeda M..Zhang Y.& Pan, Y.,Parallel Granular Neural Networks for Fast Credit Card Fraud Detection, Proc. of the 2002 IEEE International Conference on Fuzzy Systems,2002.
[19]Richard J. Bolton and David J. Hand, Statistical Fraud Detection:A Review, January 2002.
[20]Peter J. Bentley, Jungwon Kim, Gil-Ho Jung, et al, Fuzzy Darwinian Detection of Credit Card Fraud, July 2,2000.
[21]Kim M.& Kim T., A Neural Classifier with Fraud Density Map for Effective Credit Card Fraud Detection, Proc. of IDEAL,2002:378-383.
[22]白丰荣,银行经营信用卡业务与风险管理之研究,学位论文,台湾：铭传大学,2002.
[23]Maes S., Tuyls K., Vanschoenwinkel B., et al, Credit Card Fraud Detection using Bayesian and Neural Networks, Proc. of the 1st International NAISO Congress on Neuro Fuzzy Technologies,2002.
[24]Stolfo S., Lee W., Chan P., et al, Data Mining-Based Intrusion Detectors:An Overview of the Columbia IDS Project, SIGMOD Record,2001,30(04):5-14.
[25]Salvatore J. Stolfo, David W. Fan, Wenke Lee, et al, Credit Card Fraud Detection Using Meta-Learning:Issues and Initial Results, Working Notes AAAI-97,1997.
[26]Philip K. Chan, Wei Fan, Andreas L. Prodromidis, et al, Distributed Data Mining in Credit Card Fraud Detection, IEEE Intelligent Systems, November/December 1999, 14(06):67-74.
[27]Wheeler R.& Aitken S., Multiple Algorithms for Fraud Detection, Knowledge-Based Systems 2000,13(03):93-99.
[28]Fan W., Systematic Data Selection to Mine Concept-Drifting Data Streams, Proc. of SIGKDD04,2004:128-137.
[29]Wang H., Fan W., Yu P., et al, Mining Concept-Drifting Data Streams Using Ensemble Classifiers, Proc. of SIGKDD03,2003:226-235.
[30]黄琮盛,以个人消费行为预测信用卡欺诈事件之研究学位论文,台湾,国立中央大学,2001.
[31]盛昭瀚,柳炳祥,一种基于粗集神经网络的欺诈风险分析方法,应用科学学报,2003(02)：209-213.
[32]柳炳祥,盛昭瀚,一种基于距离的欺诈风险分析方法,中国管理科学,2003(01)：38-41.
[33]柳炳祥,盛昭瀚,一种基于案例推理的欺诈分析方法,控制与决策,2003(04)：494-497.
[34]曾月金,信用卡欺诈侦测模式之研究,学位论文,台湾,铭传大学,2003.
[35].吴镝,蒋鹏.论我国商业银行的信用卡风险管理.现代商贸工业.2008年第7期.
[36].杨华福.论信用卡风险及其防范.科技广场.2004.7.
[37].苏晓军.浅谈我国信用卡风险的防范.万方数据.2009年第3期.
[38].王欣.浅谈我国信用卡风险管理.管理研究.1002-2589(2009)11-0105-02.
[39].苗绘,李海串.信用卡风险的成因及防范.商场现代化.2006年12月(中旬刊),总第488期.
[40].章强.信用卡风险的防范策略.新金融.2002年第3期.
[41].胡勇,张永青.信用卡风险管理.特区经济Special Zone Economy2006年1月.
[42].魏鹏.信用卡风险管理浅析.万方数据.财税金融.
[43].信用卡风险透视与防范.金融观察.2004年第11期.
[44].欧阳天治,霍帅.中国银行信用卡风险的思考及防范.昆明冶金高等专科学校学报.2008年11月,第24卷第6期.
[45].张济珍.中国信用卡风险管理研究.学位论文.复旦大学.20031201.
[46].郭俊丹.信用卡风险控制措施研究.学位论文.西南交通大学.20080901.
[47].魏昆利.商业银行信用卡风险管理研究.学位论文.天津大学.20070501.
[48].赵艳红.中国建设银行信用卡风险管理研究.学位论文.湖南大学.20071030.
[49].Balazs Balasko, Janos Abonyi and Balazs Feil.Fuzzy Clustering and Data Analysis Toolbox—For Use with Matlab. www.codesoso.com
[50].Antoinette Leung,Zhuang Yan,Simon Fong.On Designing a Flexible E-Payment System with Fraud Detection Capability.IEEE International Conference on E-Commerce Technology,2004.
[51].Pallapa Venkataram, B.Sathish Babu, Naveen M.K., and Samyama Gungal G.H.A Method of Fraud & Intrusion Detection for E-payment Systems in Mobile e-Commerce.IEEE,2007.
[52].何强,何英MATLAB扩展编程.清华大学出版社.2002年6月第1版.
[53].李强,赵伟MATLAB数据处理与应用----工程师工具软件应用系列.国防工业出版社.2001年1月第1版.
[2]弋涛,信用卡风险管理研究,学位论文,西南财经大学,2006.
[3]贾宾,朱小燕,罗予频,胡东成.消除溢出问题的精确Baum-Welch算法.软件学报.2000,11(5)：707-710.
[4]Balazs Balasko, Janos Abonyi and Balazs Feil.Fuzzy Clustering and Data Analysis Toolbox—For Use with Matlab. www.codesoso.com
[5]Lawrence R.Rabiner.A Tutorial on Hidden Markov Models and Select Applications in Speech Recognition.IEEE,vol.77,no.2,1989
[6]汪一亭,魏臻.基于Markov模型的离散事件系统稳态与暂态的分析.计算机工程与应用.2009,45(3).
[7]杜世平.混合二阶隐马尔可夫模型的Baum-Welch算法.云南大学学报(自然科学版).2006,28(2)：95-102.
[8]杜世平.混合马尔可夫模型Baum-Welch算法的改进.广西师范学院学报(自然科学版).2005年9月,第22卷第3期.
[9].王岳斌,阳国贵,邝祝芳.基于HMM的数据库异常检测系统设计与实现.计算机应用与软件.2009年1月,第26卷第1期.
[10].乔佩利,张海霞,王艳丽.一种基于隐马尔可夫模型的实时安全评估方法.哈尔滨理工大学学报.2008年12月,第13卷第6期.
[11]徐洪丽,钱旭,刘绍翰.一种基于隐马尔科夫链的网络入侵检测研究.山东农业大学学报(自然科学版),2008,39(2)：648-552.
[12]刘雨.一种基于聚类的Markov预测模型.平顶山工学院学报.2008年11月,第17卷第6期.
[13]张友志,胡国胜,程玉胜.一种改进的Markov预测模型方法.计算机技术与发展.2008年12月第18卷第12期.
[14]Kokkinaki A,On Atypical Database Transactions:Identification of Probable Frauds using Machine Learning for User Profiling, Proc. of IEEE Knowledge and Data Engineering Exchange Workshop,1997:107-113.
[15]Chiu C & Tsai C,A Web Services-Based Collaborative Scheme for Credit Card Fraud Detection,Proc.of 2004 IEEE International Conference on e-Technology,e-Commerce and e-Service,2004.
[16]Ghosh S.& Reilly D.,Credit Card Fraud Detection with a Neural Network, Proc. of 27th Hawaii International Conference on Systems Science,1994(03):621-630.
[17]Aleskerov E.,Freisleben B.& Rao B.,CARDWATCH:A Neural Network-Based Database Mining System for Credit Card Fraud Detection,Proc.of the IEEE/IAFE on ComputationalIntelligence for Financial Engineering,1997:220-226.
[18]Syeda M..Zhang Y.& Pan, Y.,Parallel Granular Neural Networks for Fast Credit Card Fraud Detection, Proc. of the 2002 IEEE International Conference on Fuzzy Systems,2002.
[19]Richard J. Bolton and David J. Hand, Statistical Fraud Detection:A Review, January 2002.
[20]Peter J. Bentley, Jungwon Kim, Gil-Ho Jung, et al, Fuzzy Darwinian Detection of Credit Card Fraud, July 2,2000.
[21]Kim M.& Kim T., A Neural Classifier with Fraud Density Map for Effective Credit Card Fraud Detection, Proc. of IDEAL,2002:378-383.
[22]白丰荣,银行经营信用卡业务与风险管理之研究,学位论文,台湾：铭传大学,2002.
[23]Maes S., Tuyls K., Vanschoenwinkel B., et al, Credit Card Fraud Detection using Bayesian and Neural Networks, Proc. of the 1st International NAISO Congress on Neuro Fuzzy Technologies,2002.
[24]Stolfo S., Lee W., Chan P., et al, Data Mining-Based Intrusion Detectors:An Overview of the Columbia IDS Project, SIGMOD Record,2001,30(04):5-14.
[25]Salvatore J. Stolfo, David W. Fan, Wenke Lee, et al, Credit Card Fraud Detection Using Meta-Learning:Issues and Initial Results, Working Notes AAAI-97,1997.
[26]Philip K. Chan, Wei Fan, Andreas L. Prodromidis, et al, Distributed Data Mining in Credit Card Fraud Detection, IEEE Intelligent Systems, November/December 1999, 14(06):67-74.
[27]Wheeler R.& Aitken S., Multiple Algorithms for Fraud Detection, Knowledge-Based Systems 2000,13(03):93-99.
[28]Fan W., Systematic Data Selection to Mine Concept-Drifting Data Streams, Proc. of SIGKDD04,2004:128-137.
[29]Wang H., Fan W., Yu P., et al, Mining Concept-Drifting Data Streams Using Ensemble Classifiers, Proc. of SIGKDD03,2003:226-235.
[30]黄琮盛,以个人消费行为预测信用卡欺诈事件之研究学位论文,台湾,国立中央大学,2001.
[31]盛昭瀚,柳炳祥,一种基于粗集神经网络的欺诈风险分析方法,应用科学学报,2003(02)：209-213.
[32]柳炳祥,盛昭瀚,一种基于距离的欺诈风险分析方法,中国管理科学,2003(01)：38-41.
[33]柳炳祥,盛昭瀚,一种基于案例推理的欺诈分析方法,控制与决策,2003(04)：494-497.
[34]曾月金,信用卡欺诈侦测模式之研究,学位论文,台湾,铭传大学,2003.
[35].吴镝,蒋鹏.论我国商业银行的信用卡风险管理.现代商贸工业.2008年第7期.
[36].杨华福.论信用卡风险及其防范.科技广场.2004.7.
[37].苏晓军.浅谈我国信用卡风险的防范.万方数据.2009年第3期.
[38].王欣.浅谈我国信用卡风险管理.管理研究.1002-2589(2009)11-0105-02.
[39].苗绘,李海串.信用卡风险的成因及防范.商场现代化.2006年12月(中旬刊),总第488期.
[40].章强.信用卡风险的防范策略.新金融.2002年第3期.
[41].胡勇,张永青.信用卡风险管理.特区经济Special Zone Economy2006年1月.
[42].魏鹏.信用卡风险管理浅析.万方数据.财税金融.
[43].信用卡风险透视与防范.金融观察.2004年第11期.
[44].欧阳天治,霍帅.中国银行信用卡风险的思考及防范.昆明冶金高等专科学校学报.2008年11月,第24卷第6期.
[45].张济珍.中国信用卡风险管理研究.学位论文.复旦大学.20031201.
[46].郭俊丹.信用卡风险控制措施研究.学位论文.西南交通大学.20080901.
[47].魏昆利.商业银行信用卡风险管理研究.学位论文.天津大学.20070501.
[48].赵艳红.中国建设银行信用卡风险管理研究.学位论文.湖南大学.20071030.
[49].Balazs Balasko, Janos Abonyi and Balazs Feil.Fuzzy Clustering and Data Analysis Toolbox—For Use with Matlab. www.codesoso.com
[50].Antoinette Leung,Zhuang Yan,Simon Fong.On Designing a Flexible E-Payment System with Fraud Detection Capability.IEEE International Conference on E-Commerce Technology,2004.
[51].Pallapa Venkataram, B.Sathish Babu, Naveen M.K., and Samyama Gungal G.H.A Method of Fraud & Intrusion Detection for E-payment Systems in Mobile e-Commerce.IEEE,2007.
[52].何强,何英MATLAB扩展编程.清华大学出版社.2002年6月第1版.
[53].李强,赵伟MATLAB数据处理与应用----工程师工具软件应用系列.国防工业出版社.2001年1月第1版.