Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to cr...Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.展开更多
美国公共医保欺诈问题严重,为此美国开展了一系列反欺诈活动。其中医疗保险欺诈和滥用控制(Health Care Fraud and Abuse Control,简称HCFAC)项目是美国政府反欺诈工作的一大亮点。文章以HCFAC项目为研究对象,简要介绍了项目成立背景、...美国公共医保欺诈问题严重,为此美国开展了一系列反欺诈活动。其中医疗保险欺诈和滥用控制(Health Care Fraud and Abuse Control,简称HCFAC)项目是美国政府反欺诈工作的一大亮点。文章以HCFAC项目为研究对象,简要介绍了项目成立背景、机构设置、反欺诈措施与法律依据等基本情况,通过对美国医保欺诈案例的变化形势与HCFAC反欺诈工作发展的分析,总结了HCFAC项目的特点。提出我国可以适当借鉴HCFAC的成功经验,完善反欺诈法律制度、建立专门的医保反欺诈机构、引入社会力量参与和建立全国性医保大数据共享反欺诈平台,进一步打击欺诈骗保行为,保障医保基金安全等建议。展开更多
基金supported by the National Key R&D Program of China(Nos.2022YFB3104103,and 2019QY1406)the National Natural Science Foundation of China(Nos.61732022,61732004,61672020,and 62072131).
文摘Credit Card Fraud Detection(CCFD)is an essential technology for banking institutions to control fraud risks and safeguard their reputation.Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD,which significantly impact classification models’performance.To address these issues,this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks(MFGAN).The MFGAN model consists of two modules:a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature space,and a balance module based on the generative adversarial network to decrease the class imbalance ratio.The effectiveness of theMFGAN model is validated on two actual credit card datasets.The impacts of different class balance ratios on the performance of the four resamplingmodels are analyzed,and the contribution of the two different modules to the performance of the MFGAN model is investigated via ablation experiments.Experimental results demonstrate that the proposed model does better than state-of-the-art models in terms of recall,F1,and Area Under the Curve(AUC)metrics,which means that the MFGAN model can help banks find more fraudulent transactions and reduce fraud losses.
文摘美国公共医保欺诈问题严重,为此美国开展了一系列反欺诈活动。其中医疗保险欺诈和滥用控制(Health Care Fraud and Abuse Control,简称HCFAC)项目是美国政府反欺诈工作的一大亮点。文章以HCFAC项目为研究对象,简要介绍了项目成立背景、机构设置、反欺诈措施与法律依据等基本情况,通过对美国医保欺诈案例的变化形势与HCFAC反欺诈工作发展的分析,总结了HCFAC项目的特点。提出我国可以适当借鉴HCFAC的成功经验,完善反欺诈法律制度、建立专门的医保反欺诈机构、引入社会力量参与和建立全国性医保大数据共享反欺诈平台,进一步打击欺诈骗保行为,保障医保基金安全等建议。
