Continuous Auditing: AI systems that monitor transactions in real time

Abstract The rapid advancement of Artificial Intelligence (AI) has transformed the auditing profession by enabling continuous auditing systems capable of monitoring financial transactions in real time. Unlike traditional auditing methods, which rely on periodic reviews, AI-driven continuous auditing facilitates automated transaction analysis, anomaly detection, fraud prevention, and timely risk assessment. This study examines the role of AI in enhancing the effectiveness of continuous auditing by evaluating its impact on audit efficiency, fraud detection, internal controls, and organizational decision-making. A descriptive research design was adopted, and primary data were collected from 100 respondents, including auditors, accountants, finance professionals, and postgraduate students through a structured questionnaire. Secondary data were gathered from scholarly journals, books, industry reports, and professional publications. The collected data were analyzed using descriptive statistics, Chi-square analysis, Pearson correlation, and regression analysis. The findings reveal a significant association between AI awareness and perceptions of audit efficiency (χ² = 23.67, p < 0.05). Further, a strong positive relationship exists between AI adoption and continuous auditing effectiveness (r = 0.784), with AI adoption explaining 61.5% of the variation in auditing effectiveness (R² = 0.615). The study concludes that AI-powered continuous auditing significantly improves the accuracy, speed, and reliability of financial transaction monitoring while strengthening fraud detection and internal controls. Despite challenges relating to implementation costs, data security, and technical expertise, AI offers substantial opportunities to enhance audit quality and support sound financial governance in the digital era.