AI-Driven Adaptive Fraud Detection System for Financial Networks

Abstract

The rise in digital transactions, the volume of transactions has increased leading to rapid growth in the fraud detection rate in financial networks. Challenges in fraud detection include imbalanced data, high transaction feature dimensionality, evolving fraudulent patterns and the need for real-time decision making. However, traditional fraud detection approaches fall short in areas of imbalanced datasets handling, feature selection, detection accuracy and high false negative rate. In this study, an AI-driven adaptive fraud detection system is proposed for financial networks using European Cardholder Dataset. The proposed framework applies data preprocessing techniques including data cleaning, data normalization and Synthetic Minority Over-sampling Technique (SMOTE) to address the dataset imbalance issue. We apply Principal Component Analysis (PCA) for feature extraction to convert transaction data to meaningful features. Filter-based feature selection methods such as Correlation and Chi-square are applied to identify fraudulent transaction data features with the highest dependency and correlation. Random forest and SVM are implemented as classifiers for fraudulent transactions. Random forest utilizes ensemble learning to strengthen the robustness of the detection system. SVM increases classification accuracy for identifying samples with complicated distribution patterns. Accuracy, precision, recall, F1-score and receiver operating characteristic area under the curve (ROC-AUC) are used to evaluate the system performance. The system provides a high accuracy with a very low false negative rate, which makes it viable for real time fraud detection in financial networks.