The Integration of Data Analytics in Modern Auditing and Assurance Services

Abstract

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 the
development 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 models
to assess systemic risk and control effectiveness continuously. The framework introduces the concept of ’Assurance Intelligence,’ where analytics do not merely
support audit procedures but fundamentally reshape the audit risk model and the
nature of substantive testing. We apply this framework to a unique longitudinal
dataset comprising anonymized transactional data, internal control narratives, and
post-incident review reports from the financial sector. Our results demonstrate that
IPAF can identify latent control deficiencies and anomalous transaction patterns
with 34