Huan Peng Tang
About Huan Peng Tang
Huan Peng Tang is an Actuarial Technician with extensive experience in data management and financial reporting. He holds an MBA with a Financial Resources Emphasis and a BSBA in Actuarial Science from Drake University.
Work at State Auto Insurance
Huan Peng Tang has been employed at State Auto Insurance as an Actuarial Technician since 2010. In this role, he develops and maintains data sources for competitive modeling processes, ensuring accuracy in data management. His responsibilities include preparing, analyzing, and presenting quarterly financial reports to supervisors, which highlights his analytical and presentation skills. Over the course of his tenure, he has contributed to various projects that enhance the company's actuarial processes.
Education and Expertise
Huan Peng Tang studied at Drake University, where he earned a Master of Business Administration (MBA) with a focus on Financial Resources Emphasis from 2008 to 2009. Prior to this, he completed a Bachelor of Science in Business Administration (BSBA) with a specialization in Actuarial Science at the same institution from 2006 to 2007. His educational background equips him with a solid foundation in financial analysis and actuarial practices.
Background
Before joining State Auto Insurance, Huan Peng Tang gained experience as an Actuarial Intern at EMC National Life Company in 2007 for six months. He later worked at the same company as an Actuarial Technician from 2008 to 2009. This early experience provided him with practical skills in actuarial work and data analysis, contributing to his professional development in the field.
Technical Skills and Adaptability
Huan Peng Tang has demonstrated adaptability to new technologies by converting and enhancing indication spreadsheets from the Lotus platform to Excel. This transition showcases his technical skills and ability to manage data effectively, which is essential in the actuarial profession. His focus on precision in data management further supports his role in competitive modeling processes.