Credit Card Fraud Detection Using Tree-based Algorithms For Highly Imbalanced Data

The escalation of cybercrime in recent years, moves on by the proliferation of electronic devices, poses a significant threat as users engage in diverse online activities, including financial transactions. Cybercriminals exploit phishing information and manipulate cardholders' account balances to illicitly appropriate funds, presenting challenges in traceability. This study addresses detecting fraudulent banking transactions within customers' account data, characterized by highly imbalanced fraud records. Employing diverse machine learning algorithms, particularly focusing on tree-based approaches, this research reveals that LightGBM and XGBoost exhibit superior performance, with LightGBM demonstrating notable supremacy. The resultant evaluation metric, the Area Under the Curve (AUC), attains a commendable value of 0.94649, underscoring the efficacy of the proposed machine learning models in cybersecurity measures against fraudulent financial activities.