Which method of fraud detection utilizes statistical techniques to analyze data?

The correct answer is predictive modeling because it specifically employs statistical techniques to analyze data and identify patterns or anomalies that may indicate fraudulent activity. This method leverages historical data to develop models that predict future behavior, allowing for the detection of deviations that could suggest fraud.

Predictive modeling often uses algorithms and statistical methods to assess probabilities and support decision-making regarding potential fraud. By examining various data sets, it can uncover hidden insights that are not easily noticeable through manual analysis. This proactive approach allows organizations to detect fraud before it can escalate, enhancing their overall risk management and fraud prevention strategies.

In contrast, qualitative analysis relies more on non-numerical data and subjective judgment, which may not effectively capture the statistical insights necessary for fraud detection. Forensic accounting primarily focuses on examining financial records and transactions for fraud but does not emphasize the statistical analysis of large data sets. Risk assessment involves evaluating potential risks and vulnerabilities but is broader in scope and not specifically linked to statistical techniques aimed at detecting fraud. Thus, predictive modeling stands out as the most appropriate method in this context.