We’re opening the playbook on the SC.CPA Cup. These aren’t just sessions — they’re game plans. Each one is built to sharpen your strategy, strengthen your positioning, and help you move the ball forward in a profession that’s changing fast.
This week, we’re going Inside the Playbook with a sneak peek at “Evaluating the Reliability of Audit Evidence – Including Electronic Evidence & Use of AI,” led by Jennifer Louis, President of Emergent Solutions Group. The session examines how auditors evaluate the reliability of audit evidence, particularly system‑generated and AI‑enabled evidence.
Evaluating the reliability of financial statement audit evidence is fundamental to forming a sound audit opinion. Audit evidence varies in quality depending on its source, nature, and the circumstances under which it is obtained. Evidence obtained directly by the auditor, such as physical inspection or recalculation, is generally more reliable than information provided indirectly. Likewise, external evidence from independent third parties is typically considered more credible than internally generated data. Without careful evaluation of reliability, auditors risk basing conclusions on incomplete, biased, or inaccurate information.
The growing use of automation has significantly reshaped how audit evidence is generated, collected, and assessed. Automated systems can process vast volumes of transactions quickly and consistently, which can enhance audit efficiency and enable broader testing coverage. However, automation also introduces new risks. For instance, if underlying algorithms or data inputs are flawed, the outputs may appear reliable while actually containing systematic errors. Auditors must therefore understand and test the controls around automated systems, including data integrity, access controls, and change management processes.
Auditors may need to rely more heavily on system-generated reports, making it essential to verify the accuracy and completeness of those reports. Additionally, professional skepticism becomes even more critical, as automated outputs can create a false sense of assurance. Ultimately, evaluating the reliability of audit evidence—especially in the context of automation—ensures that audit conclusions are well-founded, supports audit quality, and maintains stakeholder trust in financial reporting.
Jennifer F. Louis, CPA has over 20 years of experience in designing and presenting high-quality training programs in a wide variety of technical and “soft skills” topics needed for professional and organizational success. In 2003, she founded Emergent Solutions Group, LLC, a company dedicated to developing and delivering accounting and auditing training services. She was most recently executive vice president/director of training services at AuditWatch, Inc., a highly respected training and consulting firm serving the audit profession. During her six years at AuditWatch, Ms. Louis served in many other capacities, including vice president of product development, and a consultant for the firm’s Audit Productivity Improvement Program TM. She also coauthored a manual on value-added auditing. Before joining AuditWatch, Ms. Louis was the financial/operational audit manager at AARP. While at AARP, she served as one of their specially trained facilitators available as a resource throughout the organization for designing and leading strategic meetings and training sessions. Ms. Louis was also an audit manager for Deloitte and Touche, LLP. During her years in that firm’s Washington, D.C. office, she served as an instructor for the firm’s national “Train the Trainers” program. Ms. Louis graduated summa cum laude from Marymount University with a BBA in accounting.
Evaluating the Reliability of Audit Evidence – Including Electronic Evidence & Use of AI
Thursday, May 21
11:10 a.m.–12:30 p.m.
