Research on credit card transaction security supervision based on PU learning
The complex and ever-evolving nature of credit card cash out methods and the emergence of various forms of fake transactions present challenges in obtaining accurate transaction information during customer interactions.In order to develop an accurate supervision method for detecting fake credit card...
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| Format: | Article |
| Language: | English |
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POSTS&TELECOM PRESS Co., LTD
2023-06-01
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| Series: | 网络与信息安全学报 |
| Subjects: | |
| Online Access: | https://www.infocomm-journal.com/cjnis/CN/10.11959/j.issn.2096-109x.2023039 |
| _version_ | 1797262538884251648 |
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| author | Renfeng CHEN, Hongbin ZHU |
| author_facet | Renfeng CHEN, Hongbin ZHU |
| author_sort | Renfeng CHEN, Hongbin ZHU |
| collection | DOAJ |
| description | The complex and ever-evolving nature of credit card cash out methods and the emergence of various forms of fake transactions present challenges in obtaining accurate transaction information during customer interactions.In order to develop an accurate supervision method for detecting fake credit card transactions, a PU (positive-unlabeled learning) based security identification model for single credit card transactions was established.It was based on long-term transaction label data from cashed-up accounts in commercial banks’ credit card systems.A Spy mechanism was introduced into sample data annotation by selecting million positive samples of highly reliable cash-out transactions and 1.3 million samples of transactions to be labeled, and using a learner to predict the result distribution and label negative samples of non-cash-out transactions that were difficult to identify, resulting in 1.2 million relatively reliable negative sample labels.Based on these samples, 120 candidate variables were constructed, including credit card customer attributes, quota usage, and transaction preference characteristics.After importance screening of variables, nearly 50 candidate variables were selected.The XGBoost binary classification algorithm was used for model development and prediction.The results show that the proposed model achieve an identification accuracy of 94.20%, with a group stability index (PSI) of 0.10%, indicating that the single credit card transaction security identification model based on PU learning can effectively monitor fake transactions.This study improves the model discrimination performance of machine learning binary classification algorithm in scenarios where high-precision sample label data is difficult to obtain, providing a new method for transaction security monitoring in commercial bank credit card systems. |
| first_indexed | 2024-04-24T23:58:43Z |
| format | Article |
| id | doaj.art-c8451a66f89d48308bc4aa77ed34d318 |
| institution | Directory Open Access Journal |
| issn | 2096-109X |
| language | English |
| last_indexed | 2024-04-24T23:58:43Z |
| publishDate | 2023-06-01 |
| publisher | POSTS&TELECOM PRESS Co., LTD |
| record_format | Article |
| series | 网络与信息安全学报 |
| spelling | doaj.art-c8451a66f89d48308bc4aa77ed34d3182024-03-14T10:25:36ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2023-06-0193737810.11959/j.issn.2096-109x.2023039Research on credit card transaction security supervision based on PU learningRenfeng CHEN, Hongbin ZHUThe complex and ever-evolving nature of credit card cash out methods and the emergence of various forms of fake transactions present challenges in obtaining accurate transaction information during customer interactions.In order to develop an accurate supervision method for detecting fake credit card transactions, a PU (positive-unlabeled learning) based security identification model for single credit card transactions was established.It was based on long-term transaction label data from cashed-up accounts in commercial banks’ credit card systems.A Spy mechanism was introduced into sample data annotation by selecting million positive samples of highly reliable cash-out transactions and 1.3 million samples of transactions to be labeled, and using a learner to predict the result distribution and label negative samples of non-cash-out transactions that were difficult to identify, resulting in 1.2 million relatively reliable negative sample labels.Based on these samples, 120 candidate variables were constructed, including credit card customer attributes, quota usage, and transaction preference characteristics.After importance screening of variables, nearly 50 candidate variables were selected.The XGBoost binary classification algorithm was used for model development and prediction.The results show that the proposed model achieve an identification accuracy of 94.20%, with a group stability index (PSI) of 0.10%, indicating that the single credit card transaction security identification model based on PU learning can effectively monitor fake transactions.This study improves the model discrimination performance of machine learning binary classification algorithm in scenarios where high-precision sample label data is difficult to obtain, providing a new method for transaction security monitoring in commercial bank credit card systems.https://www.infocomm-journal.com/cjnis/CN/10.11959/j.issn.2096-109x.2023039cash transaction data monitoringcredit card system security supervisionsemi-supervised learningpu learning |
| spellingShingle | Renfeng CHEN, Hongbin ZHU Research on credit card transaction security supervision based on PU learning 网络与信息安全学报 cash transaction data monitoring credit card system security supervision semi-supervised learning pu learning |
| title | Research on credit card transaction security supervision based on PU learning |
| title_full | Research on credit card transaction security supervision based on PU learning |
| title_fullStr | Research on credit card transaction security supervision based on PU learning |
| title_full_unstemmed | Research on credit card transaction security supervision based on PU learning |
| title_short | Research on credit card transaction security supervision based on PU learning |
| title_sort | research on credit card transaction security supervision based on pu learning |
| topic | cash transaction data monitoring credit card system security supervision semi-supervised learning pu learning |
| url | https://www.infocomm-journal.com/cjnis/CN/10.11959/j.issn.2096-109x.2023039 |
| work_keys_str_mv | AT renfengchenhongbinzhu researchoncreditcardtransactionsecuritysupervisionbasedonpulearning |