NEXT-GENERATION BANKING FRAUD DETECTION THROUGH QUANTUM-BASED DATA CORRELATION MODELS

This article explores the transformative potential of quantum computing in financial fraud detection, addressing the limitations of classical systems in combating sophisticated fraud schemes in digital banking environments. The article shows theoretical foundations of quantum machine learning, highlighting how quantum principles like superposition and entanglement enable multi-dimensional pattern recognition in transaction networks. Implementation architectures for hybrid quantum-classical systems are detailed, emphasizing real-time detection capabilities and secure processing workflows that maintain banking confidentiality. Performance analysis demonstrates significant improvements in detection accuracy and processing speed compared to traditional methods, with case studies from major financial institutions validating these advantages in production environments. The article concludes with an examination of regulatory compliance frameworks across jurisdictions and identifies research gaps that must be addressed as the technology matures, providing a comprehensive overview of quantum entanglement applications in visualizing fraud patterns within banking transactions.