Corinne Elizabeth Larsen

Title: Corinne Elizabeth Larsen: Innovator in Machine Learning-Based Auditing Solutions

Introduction

Corinne Elizabeth Larsen is a notable inventor based in Robbinsdale, MN (US). She has made significant contributions to the field of machine learning, particularly in the area of auditing processes. Her innovative approach aims to enhance the efficiency and accuracy of internal audits within organizations.

Latest Patents

Corinne holds a patent for a "Machine learning-based clustering model to create auditable entities." This invention describes techniques for the automatic creation of optimal auditable entities (AEs) using a machine learning (ML)-based clustering model. The model is designed to cluster a company's business processes based on similarity analyses of their attributes. By ingesting business processes and their corresponding attributes from a database, the clustering model automatically groups similar processes, resulting in model AEs that can be utilized for internal audits. This innovation not only improves audit efficiency but also allows for more accurate assignment of audits based on auditor experience and technical skills.

Career Highlights

Corinne is currently employed at Wells Fargo Bank, N.A., where she applies her expertise in machine learning to enhance auditing practices. Her work focuses on integrating advanced technologies into traditional auditing methods, thereby streamlining processes and improving outcomes.

Collaborations

Throughout her career, Corinne has collaborated with talented individuals such as Tylor Christopher Mondloch and Richard Penfil, II. These partnerships have fostered a creative environment that encourages innovation and the development of cutting-edge solutions.

Conclusion

Corinne Elizabeth Larsen is a pioneering inventor whose work in machine learning is transforming the auditing landscape. Her contributions are paving the way for more efficient and accurate auditing processes in the financial sector.