DEEP LEARNING MODEL FOR DETECTING TERROR FINANCING PATTERNS IN FINANCIAL TRANSACTIONS

Abstract

Terror financing remains a critical threat to global security, with illicit actors continually adapting their methods to evade detection. Traditional financial monitoring systems often struggle to identify the complex and covert patterns associated with terror-related transactions due to their reliance on predefined rules and statistical thresholds. This study introduces advanced deep learning models designed to detect terror financing patterns within vast datasets of financial transactions. We developed and evaluated several neural network architectures, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory networks (LSTMs), to capture both spatial and temporal transaction features. The models were trained and tested on a dataset comprising anonymized financial transactions labeled for suspicious activities related to terror financing. Our deep learning models demonstrated superior performance over conventional machine learning approaches, achieving higher accuracy, precision, and recall in identifying suspicious transactions. Notably, the LSTM-based model excelled in detecting sequential transaction patterns indicative of layering and integration stages commonly used in terror financing. The results underscore the potential of deep learning techniques in enhancing the capabilities of financial institutions and regulatory bodies to combat terror financing. Implementing such models can lead to more proactive and effective monitoring systems that adapt to evolving illicit financing strategies.