Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Advancements in Credit Card Fraud Detection: A Comprehensive Evaluation of Current Approaches
The credit card, as a prevalent electronic payment system, faces heightened vulnerability to fraud owing to the proliferation of electronic transactions. Incidents of credit card fraud have led to substantial financial losses for cardholders. In response, credit card issuing companies are actively seeking optimal methods and technologies to minimize and identify instances of credit card fraud. This study meticulously examines, highlights, and draws correlations among various approaches aimed at pinpointing credit card fraud, evaluating the merits and demerits of each method. The research delves into the utilization of diverse algorithms and methodologies for forecasting and detecting credit card fraud, offering insightful solutions to mitigate the impact of fraudulent activities. Through a comprehensive exploration of these methods, the study aims to contribute valuable insights to the ongoing efforts in the development of robust credit card fraud detection systems. The study employed various machine learning algorithms, including Decision Tree, K-Nearest Neighbor, Logistic Regression, SVM, Random Forest, and XGBoost, for the detection of fraudulent activities within a comprehensive dataset. Subsequently, the outcomes are compared and analyzed based on evaluation metrics such as accuracy, precision, recall, F1-score, and Cohen's Kappa. This research is positioned to make a significant contribution to the advancement of technologies and strategies employed by credit card companies to enhance the security of electronic transactions.
Advancements in Credit Card Fraud Detection: A Comprehensive Evaluation of Current Approaches
The credit card, as a prevalent electronic payment system, faces heightened vulnerability to fraud owing to the proliferation of electronic transactions. Incidents of credit card fraud have led to substantial financial losses for cardholders. In response, credit card issuing companies are actively seeking optimal methods and technologies to minimize and identify instances of credit card fraud. This study meticulously examines, highlights, and draws correlations among various approaches aimed at pinpointing credit card fraud, evaluating the merits and demerits of each method. The research delves into the utilization of diverse algorithms and methodologies for forecasting and detecting credit card fraud, offering insightful solutions to mitigate the impact of fraudulent activities. Through a comprehensive exploration of these methods, the study aims to contribute valuable insights to the ongoing efforts in the development of robust credit card fraud detection systems. The study employed various machine learning algorithms, including Decision Tree, K-Nearest Neighbor, Logistic Regression, SVM, Random Forest, and XGBoost, for the detection of fraudulent activities within a comprehensive dataset. Subsequently, the outcomes are compared and analyzed based on evaluation metrics such as accuracy, precision, recall, F1-score, and Cohen's Kappa. This research is positioned to make a significant contribution to the advancement of technologies and strategies employed by credit card companies to enhance the security of electronic transactions.
Advancements in Credit Card Fraud Detection: A Comprehensive Evaluation of Current Approaches
Panthakkan, Alavikunhu (Autor:in) / Prajapati, Dharmistha (Autor:in) / Chaubey, Nirbhay (Autor:in) / Joshi, Ritesh (Autor:in) / Mansoor, Wathiq (Autor:in) / Al-Ahmad, Hussain (Autor:in)
03.06.2024
780325 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch