Advanced techniques for detecting and preventing internal fraud within banking organizations

Posted: Oct 28, 2025

• Dr. Prof. Elijah Clark
• Dr. Prof. Elijah Costa
• Dr. Prof. Emma Kowalski

Abstract

The financial services industry faces an escalating challenge from internal fraud, with recent estimates suggesting that insider threats account for approximately 45% of fraud incidents. Internal fraud detection presents unique challenges that distinguish it from external fraud monitoring. Insiders possess legitimate access to systems, understand organizational controls, and can gradually escalate their activities to avoid detection. Furthermore, the psychological and behavioral dimensions of internal fraud require sophisticated analysis beyond simple transaction monitoring. Our research builds upon recent advances in behavioral analytics, quantum-inspired computing, and deep learning to develop a multi-layered detection system that addresses both technical and human factors in fraud prevention. This paper makes several key contributions to the field of financial security. First, we introduce a novel hybrid architecture that combines quantum-inspired optimization with deep learning for behavioral pattern analysis. Second, we adapt neuroimaging-inspired transformer architectures for financial behavior modeling, creating a new paradigm for insider threat detection. Third, we develop a privacy-preserving implementation framework that balances security needs with employee rights. Finally, we provide empirical validation through extensive testing in real banking environments, demonstrating significant improvements over existing systems.