To assess the success of a piloted data analytics model in identifying anomalies in vendor payments and potential fraud, the most appropriate criterion is the accuracy of the model in identifying true positives—cases flagged as anomalies that were later confirmed as valid fraud risks.
Effectiveness of the Model: The primary goal of the model is to enhance the internal audit activity’s ability to detect fraudulent transactions. The best way to measure success is to analyze how many flagged transactions were confirmed as fraudulent or erroneous.
Reduction of False Positives and False Negatives: A model that generates too many false positives (incorrectly flagged transactions) can lead to inefficiencies, while too many false negatives (missed fraudulent cases) can reduce the effectiveness of fraud detection.
Alignment with Internal Audit Standards: According to IIA Standard 1220 - Due Professional Care, internal auditors must apply appropriate tools and techniques (such as data analytics) to enhance audit effectiveness. The model's success should be assessed based on its ability to provide reliable, actionable insights.
IIA Practice Guide on Data Analytics: Recommends assessing the predictive accuracy of models by comparing flagged transactions against actual outcomes.
B. The development and maintenance costs associated with the model (Incorrect)
While cost is a consideration, it does not directly assess the effectiveness of the model in detecting fraud.
High costs may indicate inefficiency, but they do not determine whether the model is accurately identifying fraudulent transactions.
IIA Standard 2100 - Nature of Work emphasizes that internal audit activities must contribute to the improvement of governance, risk management, and control, which requires a focus on results rather than just cost.
C. The feedback of auditors involved with developing the model (Incorrect)
Feedback is useful but subjective. The ultimate test of success is not auditor perception but whether the model correctly identifies fraudulent or anomalous transactions.
IIA Practice Guide: Auditing Data Analytics suggests that while stakeholder feedback is valuable, empirical validation (accuracy of flagged cases) should be the primary success measure.
D. The number of criminal investigations initiated based on the outcomes of the model (Incorrect)
While fraud detection can lead to investigations, the number of investigations is not necessarily an accurate measure of model success.
Some flagged cases may not lead to criminal investigations due to materiality, lack of sufficient evidence, or management decisions.
According to IIA Standard 2120 - Risk Management, internal auditors must evaluate fraud risk management effectiveness, which includes detecting and preventing fraud, not just the legal consequences.
Explanation of Answer Choice A (Correct Answer):Explanation of Incorrect Answers:Conclusion:The best success criterion for the piloted data analytics model is the percentage of cases flagged by the model and confirmed as positives (Option A), as it directly measures the model's effectiveness in detecting actual fraud cases.
IIA References:
IIA Standard 1220 - Due Professional Care
IIA Standard 2100 - Nature of Work
IIA Standard 2120 - Risk Management
IIA Practice Guide: Auditing Data Analytics
