Transfer Learning-Based Applications for Cross-Domain Fraud Analysis in National Security Procurement Chains

Abstract

Fraud detection within national security procurement chains presents a significant challenge due to the complex and heterogeneous nature of procurement transactions. Traditional fraud detection methods often struggle with the dynamic and adversarial strategies employed by fraudulent entities. Transfer learning, a paradigm in machine learning, enables the adaptation of pre-trained models across different but related domains, offering a promising approach for cross-domain fraud detection. This paper explores the application of transfer learning in detecting fraudulent activities within national security procurement chains by leveraging insights from disparate domains such as financial fraud, cybercrime, and supply chain anomalies. A framework integrating domain adaptation, feature transferability, and adversarial learning is proposed to enhance detection capabilities. The study evaluates different transfer learning techniques, including instance-based, feature-representation-based, and adversarial-based approaches, and assesses their effectiveness in handling imbalanced and evolving datasets. Experimental analysis demonstrates the superiority of transfer learning models in identifying complex fraud patterns with improved generalization. The findings underscore the potential of transfer learning in mitigating fraud risks, improving procurement integrity, and strengthening national security resilience.