Design of an Intelligent Asset Recovery System Based on Deep Learning

The increasing complexity of financial crimes poses significant challenges for traditional asset recovery methods, which often fail to effectively trace covert fund transfers and complex money laundering schemes. To address these limitations, this paper proposes and implements an intelligent asset recovery system based on deep learning, incorporating advanced computational techniques to enhance the detection and analysis of complex financial transactions. The system utilizes a hybrid CNN-LSTM model to extract both spatial and temporal features from transaction data, while a self-attention mechanism dynamically highlights critical transaction patterns, improving the model's interpretability and recognition capabilities. A multimodal data fusion approach integrates diverse sources, including bank transaction records, social network data, and public financial statements, enabling comprehensive analysis of heterogeneous data. Experimental results demonstrate that the proposed system significantly outperforms traditional methods in key metrics, achieving higher accuracy, precision, recall, and Fl scores. Moreover, it exhibits superior performance in identifying intricate fund transfer chains and adapting to diverse financial crime scenarios. The study not only provides a robust, automated tool for asset recovery but also establishes a foundation for the development of intelligent systems aimed at preventing and combating financial crimes. Future research will focus on enhancing system scalability, optimizing computational efficiency, and integrating additional data sources to further expand its application scope.