Algorithmic Audit: A Methodology of Continuous Assurance in Corporate Ecosystems

Citation: Primzharova Liza, "Algorithmic Audit: A Methodology of Continuous Assurance in Corporate Ecosystems", Universal Library of Business and Economics, Volume 03, Issue 03.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This monograph develops a theoretical and methodological justification of algorithmic audit as a new model of continuous assurance in corporate ecosystems, where the generation, processing, and verification of financial information are increasingly delegated to AI systems, RPA solutions, smart contracts, and API architectures. The aim of the study is to design a comprehensive framework that closes the gap between the speed of algorithmic decision-making and the lagging nature of traditional retrospective audit. The relevance of the work follows from the crisis of the sampling-based control paradigm in the digital economy, the growing opacity of black boxes, the increasing complexity of the regulatory environment, and the need for high-frequency, deterministic verification of data. The scholarly novelty lies in the conceptual shift from probabilistic audit to an Audit as Code model, and in the design of the AIP protocol, together with metrics for explainability, traceability, and integrated assurance readiness. The central conclusion holds that the reliability of financial reporting in algorithmized ecosystems can be secured only through embedded, continuous, and machine-readable control mechanisms integrated into the very architecture of corporate systems. The monograph will be useful to researchers, auditors, risk managers, IT architects, and regulators.

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https://doi.org/10.70315/uloap.ulbec.2026.0303004