TY - GEN
T1 - Analyzing Factors Affecting Credit Card Fraud
T2 - 5th International Conference on Big Data Engineering (BDE 2023)
AU - Xu, Xuefei
AU - Chen, Kailin
PY - 2023/11
Y1 - 2023/11
N2 - With the proliferation of the internet, credit card fraud has become a pressing issue, leading to substantial financial losses and undermining trust among consumers. This research aims to elucidate the determinants associated with credit card fraud. By importing and cleansing two databases from Kaggle, we constructed two heatmaps for comparison, subsequently selecting the most suitable database for further analysis. We then established four models: Linear Regression model, Random Forest classifier, Logistic Regression model, and Decision Tree model. By comparing the confusion matrices and ROC curves of each model, the Linear Regression model emerged as the most proficient. Ultimately, three highly correlative factors were identified in relation to credit card fraud: High-risk country, Total number of declines per day, and 6-month chargeback frequency. The findings from this research pave the way for bolstering financial security, enhancing the efficacy of fraud detection, and thereby mitigating potential losses for consumers. © 2023 ACM.
AB - With the proliferation of the internet, credit card fraud has become a pressing issue, leading to substantial financial losses and undermining trust among consumers. This research aims to elucidate the determinants associated with credit card fraud. By importing and cleansing two databases from Kaggle, we constructed two heatmaps for comparison, subsequently selecting the most suitable database for further analysis. We then established four models: Linear Regression model, Random Forest classifier, Logistic Regression model, and Decision Tree model. By comparing the confusion matrices and ROC curves of each model, the Linear Regression model emerged as the most proficient. Ultimately, three highly correlative factors were identified in relation to credit card fraud: High-risk country, Total number of declines per day, and 6-month chargeback frequency. The findings from this research pave the way for bolstering financial security, enhancing the efficacy of fraud detection, and thereby mitigating potential losses for consumers. © 2023 ACM.
KW - Decision Tree Model
KW - Linear Regression Model
KW - Logistic Regression Model
KW - Machine Learning in Fraud Detection
KW - Random Forest Model
UR - https://www.scopus.com/pages/publications/85186524795
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85186524795&origin=recordpage
U2 - 10.1145/3640872.3640877
DO - 10.1145/3640872.3640877
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9798400708695
T3 - ACM International Conference Proceeding Series
SP - 28
EP - 34
BT - BDE '23: Proceedings of the 2023 5th International Conference on Big Data Engineering
PB - Association for Computing Machinery
Y2 - 17 November 2023 through 20 November 2023
ER -