FRAUD TRANSACTION DETECTION FOR ANTI-MONEY LAUNDERING SYSTEMS BASED ON DEEP LEARNING

Abstract

This study addresses the escalating problem of financial fraud, with a particular focus on credit card fraud, a phenomenon that has skyrocketed due to the increasing prevalence of online transactions. The research aims to strengthen anti-money laundering (AML) systems, thereby improving the detection and prevention of fraudulent transactions. For this study, a Dense Neural Network (DNN) has been developed to predict fraudulent transactions with high accuracy. The model is based on deep learning, and given the highly unbalanced nature of the dataset, balancing techniques were employed to mitigate the bias towards the minority class and improve performance. The DNN model demonstrated robust performance, generalizability, and reliability, achieving over 99% accuracy across training, validation, and test sets. This indicates the model's potential as a powerful tool in the ongoing fight against financial fraud. The results of this study could have significant implications for the financial sector, corporations, and governments, contributing to safer and more secure financial transactions.