Leveraging Artificial Intelligence for the Detection and Prevention of Financial Crimes in Digital Payment Ecosystems

Abstract

The rise of digital payment systems has revolutionized the financial services industry by offering rapid, convenient, and accessible means of transaction. However, this transformation has also led to a significant surge in financial crimes such as fraud, identity theft, and money laundering. This paper investigates the application of Artificial Intelligence (AI) for detecting and preventing financial crimes within digital payment ecosystems. It discusses the underlying mechanisms of AI models such as supervised learning, unsupervised learning, and deep learning, and explores how these can be used for anomaly detection, transaction pattern analysis, and fraud prevention. The paper presents a detailed experimental study using real-world financial datasets to evaluate the performance of AI-driven approaches compared to traditional rule-based systems. Results reveal a substantial improvement in detection accuracy, speed, and scalability. Finally, the paper offers insights into implementation challenges, ethical considerations, and future research directions, making a compelling case for integrating AI more deeply into financial crime detection frameworks.