Real-time suspicious detection framework for financial data streams

Abstract Money laundering hides illegal money’s origin by making it seem legal. Detecting suspicious activity quickly in financial data is key to stopping fraud and money laundering. Real-time detection is popular approach for its speed and efficiently detecting illegal activities in financial institutes system. However, handling massive and distributed data streams have challenges in achieving real-time efficiency and effectiveness. Therefore proposing and developing data stream framework is needed to handle these challenges efficiently. The main goal of this study is to propose real-time suspicious detection framework for financial institutions to effectively combat money laundering. The proposed model comprises two approaches: a distributed computing architecture based on Docker container to enhance flexibility, migration capabilities, and customization, and a suspicious detection module employing the autoencoder method. To determine whether there is any suspicious activity in the system, the proposed model uses the reconstruction error. The reconstruction error is the difference between the original input data and the data reconstructed by the proposed model. To evaluate the proposed model, we used real-world data from a financial institution and synthetic data generated from the real-world data. The study demonstrates the better performance of the proposed real-time detection framework compared to traditional methods in identifying anomalous transactions. It also explores the importance and limitations of using both real-world and generated data. Our code is publicly available: https://github.com/Ermiyas21/Real-Time-Suspicious-Detection-Framework-for-financial-data .