Credit Card Fraud Detection Using Machine Learning Algorithms: A Comparative Study of Six Models
Keywords:
AUPRC-score, Adaboost, Combined Resampling Technique, K- fold Cross Validation, K-NN algorithm, SMOTE, Xgboost.Abstract
Credit Card Fraud (CCF) is a significant financial threat where individuals impersonate others to conduct unauthorized financial activities. This study implements a Combined Resampling Technique (CRT) as a specialized new approach for addressing class imbalances and enhancing model performance in credit card fraud (CCF) detection. Additionally, it evaluates the effectiveness of various machine learning models in accurately identifying fraudulent transactions. Performance metrics included cross-validation K-fold, AUPRC score, precision, recall, averages, and F1 scores. The models assessed encompass traditional algorithms like Logistic Regression, Decision Trees, and K-Nearest Neighbors (KNN), along with ensemble methods such as Random Forest, Adaboost, and Xgboost. The dataset utilised was a simulated data set containing credit card transactions spanning January 2019 to December 2020, involving 1000 customers and 800 merchants. The study addresses data imbalance using techniques like SMOTE and employs feature engineering for improved results. Notably, the K-NN algorithm demonstrated superior performance in detecting fraudulent transactions, making it a valuable tool in combating the CCF crisis.
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