A SUSPICIOUS FINANCIAL TRANSACTION DETECTION MODEL USING AUTOENCODER AND RISK-BASED APPROACH

Abstract

The detection of suspicious financial transactions has been a critical focus in the financial industry for decades. Traditionally, financial institutions employed rule-based systems for identifying potentially fraudulent activities. These systems rely on predefined thresholds and patterns, such as large transactions or frequent deposits, to flag suspicious activities. While effective to some extent, traditional systems face significant limitations. They often generate a high rate of false positives, requiring manual intervention to review flagged transactions. Additionally, these systems struggle to adapt to evolving fraud patterns, making them less effective in detecting sophisticated financial crimes.