Next-Gen Financial Fraud and Money Laundering Detection Using Real and Simulated Datasets: A Comparative Study of Machine Learning, Deep Learning, and Graph Neural Networks (GNNs)

This study undertakes a comparative evaluation of ML and DL models across four different datasets for the detection of financial fraud: PaySim, IEEE-CIS, BankSim, and the 2023 Kaggle Credit Card Fraud dataset. Unlike earlier works with limitations of singular datasets and/or singular families of algorithms, this research focuses on hybrid model architectures, cross-dataset generalizability, and real-world trade-offs of implementation. We test different models empirically, including Random Forest, XGBoost, SVM, CNN, LSTM, and hybrid CNN-LSTM architectures, regarding several metrics such as accuracy, recall, F1-score, AUC-ROC, and false positive rates. The result shows that LSTM-and CNN-LSTM-are best suited for temporal datasets (PaySim, IEEE-CIS), while XGBoost and Random Forest are best suited for static datasets (Kaggle, BankSim). This paper, therefore, presents a solid non-overlapping framework that financial institutions could use to adopt AI systems concerning data type, latency requirements, and accuracy needs. Furthermore, considering the escalating danger of money laundering, the paper integrates the approach developed from recent advances in graph-based and temporal deep learning models such as MAGIC and Amatriciana. These models demonstrated powerful performance in modeling financial transaction networks and detecting illicit behavior.