Benford's Law and Beyond: A framework for auditors

We propose a systematic framework that integrates Benford's Law – a mathematical principle predicting the frequency distribution of leading digits – with advanced statistical and machine learning techniques for enhanced anomaly detection in financial auditing, especially highlighting actionable next steps for implementation. This framework combines Benford's Law with K-means clustering and multi-digit analysis (First-two and First-three digits) to effectively distinguish between errors, benign anomalies, and fraudulent activities. Empirical validation on financial transaction data demonstrates significant improvements in fraud detection accuracy and reliability, offering practical insights and guidance for auditors on adopting a more robust approach to anomaly detection in modern auditing practices.