Continuous Auditing Systems Supported by Advanced Data Analytics Tools (gjstudies, 2016)
This research introduces a novel, cross-disciplinary framework for continuous auditingsystems that integrates principles from computational ecology and swarm intelligence intoadvanced data analytics. Traditional continuous auditing approaches have largely reliedon rule-based anomaly detection and periodic sampling, which often fail to capture thecomplex, emergent patterns of fraud in modern, high-volume transactional environments.Our methodology diverges fundamentally by conceptualizing the financial data ecosystemas a dynamic, adaptive habitat. We employ a bio-inspired analytics engine, termed theSwarm Anomaly Detection and Pattern Recognition (SADPR) system, which utilizes algorithms modeled after the foraging and communication behaviors of social insects to identifyanomalous transactional clusters and evolving fraud vectors. The system operates on acontinuous, real-time basis, analyzing 100