Exploring the Effectiveness of Data Mining Classification Algorithms in Credit Card Fraud Detection - Agustino, Rano; Asniati Djaali, Nur; Restu Ayuningtias, Mutia (Siber Nusantara Research & Yayasan Sinergi Inovasi Bersama (SIBER), 2024)
Credit card fraud is a widespread problem that impacts both individuals and companies. Data mining provides a powerful solution to not only detect but also prevent this type of fraud. This research explores this approach by utilizing data mining classification techniques to determine the potential for fraudulent credit card transactions. Data from various sources is collected and processed to extract relevant features. This research will compare 8 classification algorithms, namely Naive Bayes, Decision Trees, Artificial Neural Network, SVM, Linear Regression, Logistic Regression, LDA and Random Forest, in classifying transactions as legitimate or fake. These findings suggest that the combined use of these data mining classification methods offers a powerful tool in combating credit card fraud. To combat the problem of credit card fraud and maintain financial security for both individuals and institutions, this researcher explores the power of data mining. By using potential classification techniques, this research aims to predict fraudulent transactions on credit cards.