A financial services firm is integrating AI to enhance fraud detection.

In AI initiatives for financial fraud detection, PMI-style AI data governance emphasizes that theintegrity, provenance, and reliability of input datamust be established before modeling. Transaction histories and customer profiles are high-risk, regulated data, so the project manager is expected to applystructured, repeatable verification methodsrather than ad hoc checks. Afact checklist to systematically verify data sourcesdirectly supports this requirement. Such a checklist typically includes validation of data origin (systems of record), timeliness, completeness, consistency across systems, documentation of transformations, and confirmation that data has not been tampered with in transit or storage.

Within an AI governance framework, these checklists form part ofdata control evidence, supporting auditability and regulatory compliance. They also help uncover misalignments such as missing transaction fields, inconsistent customer IDs, or unexplained gaps in history—all of which can materially degrade model accuracy and fairness. In contrast, prompt patterns (option A) address LLM behavior rather than data integrity; alternative processing approaches (option C) do not ensure correctness of the underlying data; and visualization of data flows (option D) helps understanding architecture but does not validate the truthfulness or accuracy of the data itself. Therefore,using a fact checklist to systematically verify data sourcesis the method that best addresses the need to ensure data integrity and accuracy.