Continuous Auditing Systems Supported by Advanced Data Analytics Tools

Abstract

This research introduces a novel, cross-disciplinary framework for continuous auditing
systems that integrates principles from computational ecology and swarm intelligence into
advanced data analytics. Traditional continuous auditing approaches have largely relied
on rule-based anomaly detection and periodic sampling, which often fail to capture the
complex, emergent patterns of fraud in modern, high-volume transactional environments.
Our methodology diverges fundamentally by conceptualizing the financial data ecosystem
as a dynamic, adaptive habitat. We employ a bio-inspired analytics engine, termed the
Swarm Anomaly Detection and Pattern Recognition (SADPR) system, which utilizes algorithms modeled after the foraging and communication behaviors of social insects to identify
anomalous transactional clusters and evolving fraud vectors. The system operates on a
continuous, real-time basis, analyzing 100