2nd Semester AY 2023-24

• Math 162 (Theory of Interest). This course provides an in-depth knowledge of the different measures of interest, how they are derived from a cashflow or series of cashflows, and how these interest concepts may be applied in amortization, sinking funds, and pricing investment securities such as bonds and stocks.
• Math 260 (Actuarial Theory and Practice). This is the second course in actuarial mathematics covering life contingencies. This covers analysis of reserves, policy values, cash values, nonforfeiture options, asset shares and profit analysis; and multiple life, multiple decrement and multiple state models.

Previous Courses

• Math 261 (Survival and Loss Models). This course is an overview of various survival and loss models as applied on risk theory and short-term insurances. This covers frequency, severity and aggregate models; insurance, exposure and policy modifications; tail weight of distributions and risk measures.
• Math 262.1 (Actuarial Science I: Product Management Aspects in Actuarial Science). This course tackles about product development and valuation of liabilities for life insurance. In addition, it introduces the concept of the actuarial control cycle framework as a problem solving algorithm in dealing with actuarial tasks.
• Math 262.2 (Actuarial Science II: Financial Management Aspects in Actuarial Science). This course discusses the continuation of the actuarial control cycle with emphasis on professionalism and ethics. In addition, it introduces basic actuarial accounting concepts and financial reporting framework for life insurance.
• Math 297 (Actuarial Models). This course walks through the actuarial modeling process as applied on formulating premium rates for short-term insurances. It covers empirical and parametric models, and credibility theory.
• Math 297 (Advanced Statistical Modeling and Predictive Analytics). This course covers statistical modeling with focus on data-centric models that can carry out predictive analytics. In particular, this is an overview of various supervised and unsupervised statistical learning methods that are prominently used in insurance and finance industry practice.
• Math 297 (Group Insurance). This course surveys various aspects of group insurance. Topics include benefit provisions, pricing, underwriting, experience rating, funding methods and reserving principles.
• Math 297 (Mathematics of Investment and Finance). This course presents the quantitative methods and analysis applicable to fundamental concepts, theory and practice in investments and finance. It covers financial reporting and analysis, project valuation, portfolio theory, asset pricing models, and capital structure and financing.
• Math 297 (Statistical Modeling and Data Analysis). This course introduces statistical modeling as applied on actuarial science and finance with the aid of R software. Topics include an introduction to data analysis, basic and multiple linear regression, and classical methods in time series analysis and forecasting.
• Math 297 (Survey on Areas of Actuarial Practice). This course presents introductory actuarial methods for various areas of actuarial practice. Topics include pension and retirement benefit valuation; profit measures and asset share pricing for traditional life insurance; introduction to participating and universal life insurances; short-term insurance ratemaking and loss reserving for health, group life and non-life insurance.
• Math 2 (Mathematics in Everyday Life). This general education course covers basic mathematical concepts and skills encountered in everyday life and gain appreciation to its practical and disciplinary values.
• Math 295 (Special Project). Supervised facilitation and directed research course for actuarial science graduate students finishing their project and orally presenting a defensible result of their research work.