you will learn about:
Using Fuzzy Matching to Solve Messy Audit Data Problems
What will be covered in this session
Audit analytics often breaks down not because data is unavailable but because it’s inconsistent—vendor names don’t match, descriptions vary, and exact joins fail. This session teaches auditors how to use fuzzy matching techniques to resolve common data quality challenges and move analytics work forward when perfect data doesn’t exist.
Participants will learn when fuzzy matching is an appropriate solution, how to prepare data for matching, how to tune and interpret match confidence scores, and how to validate results from an audit perspective. Through a realistic example—with support from Copilot—the session demonstrates how fuzzy matching can be applied to audit scenarios such as vendor analysis, expense review, and population completeness testing.
The focus is on practical application and audit judgment, helping attendees build confidence in using fuzzy matching to complete analytics assignments more efficiently and effectively.
About Speaker
Valerie began her career in internal audit, working for CME Group in Chicago. She discovered her passion for data analytics and visualizations and switched roles to become an Analytics Auditor while pursuing her MBA in Business Analytics at DePaul University. She then advanced to become the AVP of Data Analytics at Northern Trust and now is the Manager, Internal Audit Analytics at Victoria’s Secret and Company. Valerie also holds a Finance and Economics degree from Wake Forest University and a Master of Accountancy degree from the University of Pittsburgh. She is a licensed CPA in Pennsylvania where she resides.
