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. The growing complexity and volume of financial transactions in the digital era have heightened the need for advanced detection mechanisms. Traditional systems fail to address the dynamic nature of financial fraud, leading to inefficiencies in preventing financial losses. This creates a pressing need for a more adaptable, accurate, and scalable approach to detecting suspicious transactions. The lack of adaptability in traditional methods, combined with the significant financial and reputational risks posed by undetected fraud, underscores the necessity of a more robust detection framework. The goal is to enhance the ability to detect anomalous patterns in financial data with minimal false positives while maintaining efficiency and scalability. The proposed system introduces an innovative solution that leverages an autoencoder-based model combined with a risk-based assessment strategy. This approach aims to capture subtle anomalies in transaction data that deviate from normal patterns, enabling the identification of suspicious activities. The integration of a risk-based framework ensures that the model considers contextual factors, reducing false alarms and prioritizing high-risk transactions for further analysis. This system addresses the limitations of traditional methods, providing a sophisticated, adaptive, and reliable tool for combating financial fraud.

Description:

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