Article
FINSHIELD : AI-DRIVEN DETECTION OF MONEY LAUNDERING SOCIAL NETWORK TRANSATIONS
Money laundering is a critical financial crime that poses serious threats to economic stability and global financial systems. With the rapid growth of digital banking, online transactions, and financial technologies, detecting suspicious financial activities has become increasingly complex. Traditional Anti-Money Laundering (AML) systems primarily rely on rule-based approaches, which often fail to detect advanced laundering techniques and generate a high number of false positives. To address these limitations, this project proposes FinShield, an AI-driven system designed to detect money laundering activities within social transaction networks. The system integrates Machine Learning (ML), behavioural analysis, and network-based graph analysis to identify suspicious transaction patterns and hidden relationships among users. Instead of analysing transactions individually, FinShield evaluates the overall transaction ecosystem by modelling users as nodes and transactions as edges, enabling detection of circular transactions, layering patterns, and suspicious clusters. Multiple ML models such as Random Forest, Logistic Regression, and Support Vector Machines are used to classify transactions based on risk scores. The system also incorporates real-time monitoring and a web-based dashboard for visualization and alerts. By combining AI techniques with network intelligence, FinShield significantly improves detection accuracy, reduces false positives, and enhances decision-making for financial institutions. The proposed system provides a scalable, adaptive, and efficient solution for modern AML challenges, ensuring better financial security and fraud prevention in digital ecosystems.
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