INTRO TO LIFE INSURANCE
- M 6:10 p.m.-8:55 p.m.
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Instructor:
Regina Dolgoarshinnykh
CULPA: 2 Info - 3 points
PROPERTY/CASUALTY
PROPERTY & CASUALTY
- W 6:10 p.m.-8:55 p.m.
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Instructor:
Mark Hoffmann
Info - 3 points
ACTUARIAL METHODS
The Actuarial Methods course explores models for evaluating and managing risks of life contingent contracts, their theoretical basis and applications. Topics include survival models, life insurance and annuity benefits, premium and reserve calculations related to policies on a single life, as well as option pricing. This course also covers materials relevant to the long-term section of the Fundamentals of Actuarial Mathematics (FAM) exam of the Society of Actuaries. This is a core course of the M.S. in Actuarial Science program.
ADVANCED ACTUARIAL METHODS
The purpose this class is to develop the student’s knowledge of the theoretical basis of certain actuarial models and the application of those models to insurance and other financial risks. A thorough knowledge of calculus, probability, and interest theory is assumed. Knowledge of risk management at the level of Exam P is also assumed.
The combination of these two classes covers the material for the FAM-L and ALTAM examinations of the Society of Actuaries. This is a core class of the Actuarial Science program. Students who have already taken and passed the MLC or LTAM exam for SOA are exempted from this class and can substitute an elective.
- MW 10:10 a.m.-11:25 a.m.
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Instructor:
Abraham Weishaus
Info - 3 points
ACTUARIAL MODELS
This course provides an introduction to the tools for pricing and reserving for short term insurance. We will discuss methods for calculating IBNR reserves, ratemaking, frequency and severity models used for modeling coverage modifications, statistical methods for fitting, evaluating, and selecting parametric models for frequency and severity, and three credibility methods.
This class covers the short-term material of Exam FAM and also covers the material of Exam ASTAM of the Society of Actuaries, and some of the material on Exams MAS I, MAS II, and 5 of the Casualty Actuarial Society. This is a core class of the Actuarial Science program. Students who have already taken and passed the FAM exam (or its short term portion) and the ASTAM exam administered by the SOA are exempted from this class and can substitute an elective.
- MW 1:10 p.m.-2:25 p.m.
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Instructor:
Regina Dolgoarshinnykh
CULPA: 2 Info - 3 points
PREDICTIVE MODELING IN FINANCE & INSURAN
PREDICT MODLNG IN FIN & INSRNC
This course introduces to the students, generalized linear models (GLM), time series models, and some popular statistical learning models such as decision trees models as well as random forests and boosting trees. The aim for GLM is to provide a flexible framework for the analysis and model building using the likelihood techniques for almost any data type. The aim for the statistical learning models is to build and predict or understand data structure (if unsupervised) using statistical learning methods such as tree-based for supervised learning and the Principle Component Analysis and Clustering for unsupervised learning. It develops a student’s knowledge of the theoretical basis in predictive modeling, computational implementation of the models and their application in finance and insurance. Tools such as cross-validation and techniques such as regularization and dimension reduction for fitting and selecting models are explored. We also implement these models using a combination of Excel and R.
The class covers the material of Exams, Statistics for Risk Modeling (SRM) and Predictive Analytics (PA) of Society of Actuaries, and some material of Exams, Modern Actuarial Statistics I (MAS-I) and MAS II by the Casualty Actuarial Society. This is a core course for the Actuarial Science students. Students who have already taken and passed the SRM and PA exams administered by the SOA are exempted from this class and can substitute an elective.
DATA SCIENCE IN FINANCE AND INSURANCE
DATA SCIENCE IN FIN & INSURANC
The Data Science in Finance and Insurance course explores machine learning models, their theoretical basis, computing implementation and applications in finance and insurance. Topics include machine learning models for regression, classification and unsupervised learning; tools such as cross validation and techniques such as regularization, dimension reduction and ensemble learning; and select algorithms for fitting machine learning models.
This course offers students an intensive hands-on experience where they combine theoretical understanding, domain knowledge and coding skills to better inform data-driven decision making. Prior exposure to linear algebra, calculus and statistics is helpful. A working knowledge of a spreadsheet program and R is a plus. Students will use spreadsheets and R for validation and prototyping and Python to implement algorithms and apply models to applicable data.
Some topics covered are also relevant to the statistical learning portion of the Society of Actuaries (SOA) and the Casualty Actuarial Society (CAS) curricula, and the quantitative methods section of the Chartered Financial Analyst (CFA) Institute curriculum. This is a core course of the Actuarial Science program.
Advanced Data Science Applications in Fi
Adv Data Sci App in Fin a
The Advanced Data Science Applications in Finance and Insurance course covers topics in object-oriented programming with python, the relational theory and relational database navigation with SQL, deep learning (including traditional machine learning tasks, computer vision, recurrent networks, natural language processing, and large language models), as well as interpretable machine learning.
Some topics covered are relevant to the Advanced Topics in Predictive Analytics (ATPA) exam of the Society of Actuaries. Topics in deep learning are also relevant to the statistical learning portion of the Casualty Actuarial Society (CAS) curriculum, and the quantitative methods section of the Chartered Financial Analyst (CFA) Institute curriculum.
Familiarity with machine learning models covered in the Data Science in Finance and Insurance (ACTU PS5841) course is helpful. Prior exposure to linear algebra, calculus, statistics, and a working knowledge of python and spreadsheets are necessary.
GLOBAL CAPITAL MARKET & INVESTMENT
GLOBAL CAPITAL MRKT & INVSTMNT
This course will introduce students to major issues currently of concern to all investors. It can give you the skills to conduct a sophisticated assessment of current issues and debates covered by the popular media as well as more-specialized finance journals. These skills are essential for people who pursues a financial service career, especially in today’s rapidly evolving environment. The material presented in this course are both practical important and intellectually interesting.
This course is consistent with and relevant to Chartered Financial Analyst (CFA) curriculum. It covers all subjects in CFA test and most of problems are in the same format as the CFA examination questions. This course will also provide a foundation for further study in Financial Risk Management and Financial market related courses.
- W 2:40 p.m.-5:25 p.m.
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Instructor:
Leland Wei
Info - 3 points
PROSEMINAR IN ACTUARIAL SCIENCE
Industry representatives conduct a series of noncredit seminar sessions designed to expose students to the actuarial profession as well as to address a range of topics in actuarial science.
Internship In ACTU
This course requires you to experience firsthand a program-related job in a real working environment. You will engage in personal, environmental and organizational reflection. The ideal Internship will provide you an opportunity to gain tangible and practical knowledge in your chosen field by taking on a position that is closely aligned with your coursework and professional interests. Before registering for this course, you must have completed the Internship Application Form in which you will describe your internship sponsor and provide details about the work that you will be doing. This form must be signed by your internship supervisor and approved by your program director BEFORE you register for this course.
To receive instructor approval, the internship:
● Must provide an opportunity for the student to apply course concepts, either at the organizational or team level
● Must fit into the planned future program-related career path of the student
You must identify your own internship opportunities. The internship must involve a commitment to completing a minimum of 210 hours over the semester.
At the end of your course, you will submit an evaluation form to your internship supervisor. The evaluation form should be returned directly to the instructor
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Instructor:
Robin Wang
Info - 3 points