Leveraging Statistical Sampling Techniques to Enhance Audit Accuracy and Detect Financial Anomalies in Complex Systems

Abstract

This study examines how leveraging statistical sampling techniques can enhance audit accuracy and detect financial anomalies in complex systems. The research objective is to evaluate the effectiveness of various sampling methods, identify challenges in their application, and propose optimization strategies to improve audit reliability. A mixed-methods approach was employed, incorporating secondary data from financial audits conducted between 2020 and 2024, regression analysis to assess the correlation between sample sizes and anomaly detection rates, and chi-square tests to evaluate the distribution of anomalies across financial departments. Findings indicate a strong positive correlation (0.90) between increased sample sizes and higher anomaly detection rates, with an R-squared value of 0.98 confirming that 98% of the variation in anomaly detection is explained by sample size increments. Moreover, audit accuracy improved consistently by 5% annually, reaching 94% in 2024. The study concludes that expanding sample sizes and integrating advanced technologies such as artificial intelligence and predictive analytics significantly enhance anomaly detection and audit precision. However, challenges such as sampling bias, regulatory constraints, and methodological limitations persist. Recommendations include the adoption of dynamic sampling strategies, integration of AI-driven anomaly detection, enhanced auditor training in statistical methods, regulatory flexibility in sampling frameworks, and real-time data analytics for continuous audit improvement.