This source, "Chap_06 - Data-Driven Fraud Detection.pdf", serves as a chapter from a forensic accounting text, likely titled "Forensic Accounting" with Dr. Neale O’Connor. It focuses specifically on data-driven fraud detection, contrasting errors with intentional fraud and highlighting the limitations of audit sampling for fraud. The document outlines a six-step data analysis process for proactive fraud detection, beginning with understanding the business and culminating in symptom investigation. It also introduces various data analysis software (like ACL Audit Analytics and CaseWare's IDEA), methods for data access (such as Open Database Connectivity), and common data analysis techniques including digital analysis (Benford's Law), outlier investigation, stratification, fuzzy matching, and real-time analysis. Finally, it explains how to detect fraud by analyzing financial statements through comparisons, ratio analysis, and vertical/horizontal analysis.