Juncheng An

Juncheng An

Actuarial Analyst @ Milliman

About Juncheng An

Juncheng An is an Actuarial Analyst at Milliman in Chicago, Illinois, where he has worked since 2020. He has a background in actuarial mathematics and has completed various internships and research roles at the University of Michigan and other organizations.

Work at Milliman

Juncheng An has been employed at Milliman as an Actuarial Analyst since 2020. In this role, he is based in Chicago, Illinois, and has accumulated four years of experience. His responsibilities include involvement in financial risk management, financial reporting, hedges, and mergers and acquisitions (M&A) projects. This position allows him to apply his actuarial knowledge in a practical setting, contributing to the firm's analytical capabilities.

Education and Expertise

Juncheng An holds a Bachelor's degree in Actuarial Mathematics and Computer Science from the University of Michigan, where he studied from 2017 to 2020. He also completed a Bachelor's degree in Actuarial Math at the University of Pittsburgh from 2015 to 2017. He has passed all ASA actuarial exams and is currently working on completing the remaining modules for the ASA designation, indicating a strong foundation in actuarial science.

Background

Prior to his current role at Milliman, Juncheng An gained diverse experience through various positions at the University of Michigan. He worked as a Grader, Instructor Aid, and Undergraduate Researcher, focusing on areas such as deep learning and market prediction. He also completed internships in actuarial roles at China Taiping Insurance (Singapore) Pte. Ltd. and Global Atlantic Financial Group, further enhancing his practical skills in the field.

Research Experience

During his time at the University of Michigan, Juncheng An engaged in multiple research projects. He was involved in Prediction Market Research and worked at the IoT/CPS Security Research Lab. His research contributions spanned several months and focused on applying advanced analytical techniques, including deep learning, to real-world problems.

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