AI Based Systems for Continuous Auditing and Real Time Assurance (gjstudies, 2021)
This paper introduces a novel paradigm for financial oversight by proposing and validating a bio-inspired, neuromorphic AI architecture for continuous auditing and real-timeassurance. Moving beyond traditional rule-based or statistical anomaly detection systems,our approach leverages principles from computational neuroscience and federated learningto create an adaptive, self-learning audit ecosystem. The core innovation lies in a hybridsystem that mimics the human brain’s error prediction and conflict monitoring mechanisms—specifically the anterior cingulate cortex and prefrontal cortex interactions—to detect not just explicit fraud patterns but also subtle, emerging systemic risks and controlenvironment degradations. We formulate the audit problem not as a discrete classificationtask but as a continuous, multi-stream temporal signal processing challenge, where transactional data, communication metadata, system logs, and even non-traditional data likeemployee access patterns are synthesized. Our methodology employs a federated learningframework, inspired by collaborative research models in sensitive domains like healthcare,to enable real-time assurance across distributed entities without centralizing sensitive financial data, directly addressing privacy and security concerns highlighted in prior fintech andauditing literature. We implemented a prototype system and evaluated it using a simulated multi-bank environment, incorporating known fraud scenarios and novel, complex riskpatterns. Results demonstrate a 47% improvement in early detection of sophisticated, multistage fraud schemes compared to state-of-the-art systems, while reducing false positives by32%. Furthermore, the system demonstrated emergent capability to identify previously uncoded risk indicators, such as subtle shifts in procedural adherence that preceded controlfailures. This research provides a foundational shift from periodic, sample-based audits to aliving, breathing assurance model, offering significant theoretical and practical contributionsfor the future of corporate governance, regulatory compliance, and financial integrity