Maksym Lazirko

Maksym Lazirko

Recent advancements in quantum technology threaten the cryptographic foundations of Accounting Information Systems (AIS), necessitating a transition to quantum-safe standards. This paper investigates why quantum standards fall within the purview of accounting by framing them as essential institutional governance mechanisms that ensure the integrity, auditability, and legitimacy of data. Utilizing neo-institutional theory, the study analyzes how coercive, normative, and mimetic pressures drive the adoption of these standards across jurisdictions. Through a structured documentary analysis of major standard-setting bodies, the research identifies significant divergence between U.S. and EU/European approaches: U.S. standards emphasize market-driven innovation and pragmatic legitimacy, while EU and Pan-European standards prioritize regulatory harmonization and societal privacy objectives. The findings suggest that while these standards are currently voluntary, their inconsistent implementation creates risks of decoupling and fragmented assurance practices, challenging the global comparability of AIS security controls.

Buhe Li, Berkay Kaplan, Maksym Lazirko et al.

This study investigates the effectiveness of unsupervised outlier detection methods in audit analytics, utilizing USA spending data from the U.S. Department of Health and Human Services (DHHS) as a case example. We employ and compare multiple outlier detection algorithms, including Histogram-based Outlier Score (HBOS), Robust Principal Component Analysis (PCA), Minimum Covariance Determinant (MCD), and K-Nearest Neighbors (KNN) to identify anomalies in federal spending patterns. The research addresses the growing need for efficient and accurate anomaly detection in large-scale governmental datasets, where traditional auditing methods may fall short. Our methodology involves data preparation, algorithm implementation, and performance evaluation using precision, recall, and F1 scores. Results indicate that a hybrid approach, combining multiple detection strategies, enhances the robustness and accuracy of outlier identification in complex financial data. This study contributes to the field of audit analytics by providing insights into the comparative effectiveness of various outlier detection models and demonstrating the potential of unsupervised learning techniques in improving audit quality and efficiency. The findings have implications for auditors, policymakers, and researchers seeking to leverage advanced analytics in governmental financial oversight and risk management.