(untitled) (gjstudies, 2022)
This research presents a novel methodological framework for integrating advanced data analytics into auditing and assurance services, moving beyond conventional descriptive analytics to establish a predictive and prescriptive paradigm.Traditional auditing approaches have increasingly incorporated basic data analysis,yet they remain largely reactive and sample-based. Our contribution lies in thedevelopment of the Integrated Predictive Audit Framework (IPAF), which synthesizes techniques from anomaly detection, natural language processing of unstructured regulatory and corporate communications, and probabilistic graphical modelsto assess systemic risk and control effectiveness continuously. The framework introduces the concept of ’Assurance Intelligence,’ where analytics do not merelysupport audit procedures but fundamentally reshape the audit risk model and thenature of substantive testing. We apply this framework to a unique longitudinaldataset comprising anonymized transactional data, internal control narratives, andpost-incident review reports from the financial sector. Our results demonstrate thatIPAF can identify latent control deficiencies and anomalous transaction patternswith 34