PA Study Manual
Begin preparing for the Predictive Analytics SOA exam with a first-class manual written by a predictive analytics expert. Our Study Manual offers personalized learning on a schedule that works for you. This manual is designed to help you develop a conceptual understanding of and hands-on experience with the Exam PA as effectively and efficiently as possible so you pass on the first try.
The Exam PA study guide contains:
Retain more information with our helpful videos that cover key topics in the syllabus Designed to help you understand the most complex topics and add substantial value to your learning.
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Graded Mock Exam
Rehearse before the real exam! Practice with the ACTEX PA Graded Mock Exam - a great way to predict your exam outcome! Before you take the official PA exam - take the ACTEX PA Graded Mock Exam and get feedback from PA expert, Ambrose Lo.
Ambrose Lo, PA professor and author of the ACTEX PA study manual, has designed the graded mock exam to mirror the SOA Exam PA. This Mock exam has all the typical elements your SOA exam will have plus the questions and format are set up just like the SOA PA exam. This allows you to gain insights into the content, format and your strengths and weaknesses prior to taking the exam.
He will lead a team of experts, including Prof. David Lee, PhD and Boxiang Wang, Ph.D. to grade the submitted mock exams.
How does it work?
1:1 Live Feedback Session on Graded Mock Exam
Want more? Add 1:1 Live Feedback Session to your graded exam. Connect with one of the expert graders and go over your Graded mock exam results. You will:
The instructor that grades your paper is the instructor that you will meet- so they will already have a good grasp of your strengths and more importantly weaknesses! See bios of the teaching team below.
About the Authors
Ambrose Lo Ph.D., FSA, CERA
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, The University of Hong Kong, public recognition in the Daily Iowan as a faculty member "making a positive difference in students' lives during their time at The University of Iowa" for eight years in a row (2016 to 2023), and, most recently, the 2019-2020 Collegiate Teaching Award from the College of Liberal Arts and Sciences, The University of Iowa.
Professor Lo is currently Associate Professor of Actuarial Science with tenure at the Department of Statistics and Actuarial Science, The University of Iowa. He earned his B.S. in Actuarial Science (first class honors) and Ph.D. in Actuarial Science from The University of Hong Kong in 2010 and 2014, respectively. He joined The University of Iowa as Assistant Professor of Actuarial Science in August 2014 and was tenured and promoted to Associate Professor 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.
In addition to coauthoring the ACTEX Study Manual for SOA Exam SRM, he is also the sole author of the ACTEX Study Manual for CAS Exam MAS-I, ACTEX Study Manual for SOA Exam PA, and the textbook Derivative Pricing: A Problem-Based Primer (2018) published by Chapman & Hall/CRC Press.
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 Assistant Professor 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.