Enhancing Financial Audit Operations through AI Anomaly Detection

Authors

• Nadya Cousin, St. Mary's University
• Rebekah Garza, St. Mary's University
• Talisa Gomez, St. Mary's University

Files

Contributor

Swain, Ajaya (Faculty Mentor)

Digital Publisher

Digital Commons at St. Mary's University

Publication Date

Spring 2026

Keywords

Artificial Intelligence (AI), Financial auditing, Data sciences

Description

❖ Financial auditing plays a critical role in ensuring accuracy, regulatory compliance, and fraud detection in financial reporting

❖ Traditional audit approaches rely heavily on sampling and manual review processes, limiting their ability to scale with increasing data complexity

❖ The rapid growth of high-volume, high-velocity financial data (big data) has exposed significant limitations in traditional auditing, including:

• Incomplete data coverage
• Delayed anomaly detection
• Increased risk of material misstatements

❖ These limitations create a need for scalable, automated, and data-driven audit solutions

❖ Artificial Intelligence (AI), particularly anomaly detection models, enables:

•  Full-population testing
•  Real-time pattern recognition
•  Proactive risk identification

Format

pdf

Size

1 poster

City

San Antonio, Texas

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.