IMPROVING LEGAL MECHANISMS FOR DETECTING CYBERCRIMES BASED ON STATISTICAL ANALYSIS OF FINANCIAL TRANSACTIONS IN THE BANKING SYSTEM

Abstrak

The rapid expansion of digital banking has significantly increased the scale and complexity of cyber-enabled financial crimes. Traditional legal mechanisms often struggle to keep pace with evolving fraud patterns, particularly in real-time transaction environments. This paper proposes an integrated approach that combines statistical analysis of financial transactions with compliance-aware artificial intelligence to enhance cybercrime detection and legal accountability. By leveraging short-term behavioral patterns, anomaly detection, and regulatory-aligned decision frameworks, the study demonstrates how financial institutions can improve both detection accuracy and legal traceability. The findings highlight the importance of embedding explainability, auditability, and statistical evidence into cybercrime detection systems to support effective law enforcement and regulatory compliance.