How does it work?

• Only a limited number of Graded Mock exams will be accepted for the April ‘25 exam
• You will receive the ACTEX PA Graded Mock Exam by late February
• The Mock Exam will come in a Word Document with spaces for you to input your responses
• Take the exam in the allotted time so you can simulate the 3.5 hours that will be given at the actual exam
• Submit the exam directly to the PA grading team via email. Last submission date: TBD
• Prof. Lo and his team will review, grade and provide written feedback within 2 weeks

About the Authors

Ambrose Lo Ph.D., FSA, CERA

Ambrose Lo, PhD, FSA, CERA, is the author of several study manuals for professional actuarial examinations and an Adjunct Associate Professor at the Department of Statistics and Actuarial Science, the University of Hong Kong (HKU). He earned his BS in Actuarial Science (first class honors) and PhD in Actuarial Science from HKU in 2010 and 2014, respectively, and attained his Fellowship of the Society of Actuaries (FSA) in 2013. He joined the Department of Statistics and Actuarial Science, the University of Iowa (UI) as Assistant Professor of Actuarial Science in August 2014, and was promoted to Associate Professor with tenure in July 2019. His research interests lie in dependence structures, quantitative risk management as well as optimal (re)insurance. His research papers have been published in top-tier actuarial journals, such as ASTIN Bulletin: The Journal of the International Actuarial Association, Insurance: Mathematics and Economics, and Scandinavian Actuarial Journal. He left the UI and returned to Hong Kong in July 2023.

Besides dedicating himself to actuarial research, Ambrose attaches equal (if not more!) importance to teaching and education, through which he nurtures the next generation of actuaries and serves the actuarial profession. He has taught courses on a wide range of actuarial science topics, such as financial derivatives, mathematics of finance, life contingencies, and statistics for risk modeling. He is the (co)author of the ACTEX Study Manuals for Exams ATPA, MAS-I, MAS-II, PA, and SRM, a Study Manual for Exam FAM, and the textbook Derivative Pricing: A Problem-Based Primer (2018) published by Chapman & Hall/CRC Press. Although helping students pass actuarial exams is an important goal of his teaching, inculcating students with a thorough understanding of the subject and logical reasoning is always his top priority. In recognition of his outstanding teaching, Ambrose has received a number of awards and honors ever since he was a graduate student, including the 2012 Excellent Teaching Assistant Award from the Faculty of Science, HKU, public recognition in the Daily Iowan as a faculty member "making a positive difference in students' lives during their time at UI" for nine years in a row (2016 to 2024), and the 2019-2020 Collegiate Teaching Award from the UI College of Liberal Arts and Sciences.

David Lee, Ph.D., ASA

Dr. David Lee is a Lecturer at the Department of Statistics and Actuarial Science, the University of Hong Kong since 2017 and has taught many actuarial science and statistics courses, such as life contingencies, corporate finance, multivariate statistics, statistical inference and time series. He earned his B.S. in actuarial science (first class honors) and M.Phil. in statistics at the University of Hong Kong, and Ph.D. in statistics at the University of British Columbia. He is a Fellow of the Advance HE, a British professional membership scheme promoting excellence in higher education. He obtained the ASA credential after SOA's curriculum change in 2018, and had first-hand experience in some of the newer SOA exams such as PA and LTAM (getting grade 10 in both).

Boxiang Wang, Ph.D.

Dr. Boxiang Wang is an Associate Professor with tenure in the Department of Statistics and Actuarial Science at The University of Iowa. He received his Ph.D. in Statistics from the University of Minnesota in 2018 under the supervision of Professor Hui Zou. His primary research interests lie at the intersection of statistics, machine learning, actuarial science, and optimization. His areas of expertise within machine learning are wide-ranging and include random forests, support vector machines, cluster analysis, lasso-penalized regression and classification, and model assessment including cross-validation, among others. He has published numerous papers in top journals in statistics and machine learning, including the Journal of the American Statistical Association, the Journal of the Royal Statistical Society Series B, and the Journal of Machine Learning Research, Neural Information Processing Systems. Dr. Wang is also engaged in leveraging machine learning methodologies for solving real-world insurance problems. In addition, as a valuable contributor to the R community, he has developed and released over ten R packages, which have collectively amassed over 100,000 downloads on The Comprehensive R Archive Network.

Besides his achievements in scholarly research, Dr. Wang has also distinguished himself as an exceptional educator, teaching a wide range of courses from probability and statistical theory to computing and advanced machine learning topics. His students range from undergraduate scholars to doctoral candidates, with a notable proportion majoring in actuarial science. Dr. Wang is especially good at simplifying complex technical concepts, making them accessible to students with varying backgrounds. His dedication to pedagogy and his outstanding teaching performance have been recognized with the Bernard W Lindgren Teaching Award at the University of Minnesota and the inaugural Departmental Faculty Award for Teaching at the University of Iowa.