The Master of Financial Insurance (MFI) program is designed as a 12-month (3 term) program comprised of 9 required core courses, 1 elective course, and a 16-week summer internship placement.

Students proceed through the program as a single cohort, following a common course of study which is fully integrated and computer-laboratory intensive. Course projects and assignments are designed to integrate the material from multiple courses and their application in a real-world practical context. Excellence in communication and presentation skills is emphasized in both the oral and written components of the projects.

All core courses (except for STA 2546 H and the elective STA45** H) in this program are 0.5 FCE (Full Course Equivalent) which run for twelve (12) weeks and meet for three (3) hours per week (excluding tutorials / computer labs).


Core Courses:

This course features studies in derivative pricing theory and focuses on building basic financial theory and their applications to various derivative products. A working knowledge of probability theory, stochastic calculus, knowledge of ordinary and partial differential equations and familiarity with the basic financial instruments is assumed. The topics covered in this course include but are not limited to binomial pricing models; continuous time limits; the Black-Scholes model; the Greeks and hedging; European, American, Asian, barrier and other path-dependent options; short rate models and interest rate derivatives; convertible bonds; stochastic volatility and volatility derivatives; currency and commodity derivative.

An overview of methods and problems in the analysis of time series data related to finance and insurance. The course will focus on both theory and application with real datasets using R and Python and will require writing reports. Topics include stationary processes, linear processes; elements of inference in time and frequency domains with applications; ARMA, ARIMA, SARIMA, ARCH, GARCH; filtering and smoothing time-series; and State-space models.

This graduate course develops the theory and application of life insurance products. Beginning with basic life insurance and annuity valuation, the course introduces the concepts of premium reserving, multiple decrements, multiple life insurance, and expense loading. As well, topics in pension mathematics will be covered. The course and projects emphasize numerical implementation and practical relevance.

This course focuses on data science techniques for risk modelling stemming from finance and insurance, including maximum likelihood estimation, expectation maximization, generalized linear and additive models, mixture models, hidden Markov models, artificial neural networks, and reinforcement learning.

This course extends over the fall/winter semesters and will feature invited guest speakers delivering both academic and practical seminars on current aspects of finance and insurance modeling, pensions, valuation risk management, regulation, and accounting.

This course features studies in the risks, and how to quantify and manage those risk, in financial and mortality linked insurance products. Topics include hedging of guarantees embedded in equity-linked insurance and annuity products, asset-liability management, determination of regulatory and economic capitals, insurance securitization (life & P/C), longevity bonds and derivatives, reinsurance, catastrophe bonds and derivatives.

This course takes cases from a variety of problems in the financial and insurance worlds and students will work in groups to develop both the theory and implementation of cases, draft reports, and deliver presentations on their findings. The course will be led by industry practitioners. Sample topics include Solvency II, Pension Benefits Act, valuing and managing complex annuity riders.

This course explores what are the various issues that arise when machine and statistical learning methods are used in practice on big data to inform business intelligence (in finance and insurance). In practice, data is not clean, number of features is large, feature engineering must be carried out, and data is often multi-modal consisting not only of structured data, but also of images, text, and social network data. In this course, students will be exposed to various techniques and practical expertise to deal with these cases and learn how to present results to practitioners who are not domain experts.

This course explores the practical application of various numerical methods to finance and insurance modeling. It covers topics including: the generation of random variables, simulating solutions of stochastic differential equations, variance reduction methods, multi-level sampling, least square Monte Carlo, Markov chain Monte Carlo, and solving partial difference equations stemming from derivative valuation, optimal control, and optimal stopping.

Elective Courses

Any one of Statistical Sciences’ 0.25 FCE graduate offerings, level 4000 with significant financial, insurance, or data science components, and approval of the MFI Program Director.


Students will complete an industrial internship or research project in the financial insurance area and draft a report, present, and defend it.

Check out what some of our students have to say about their internship experience.

Examples of Internship Reports submitted:

  • Credit Valuation Adjustment Calculation and its Implementation
  • Validation of the Premium Credit Card Portfolio Probability of Default Model
  • Reinsurance and Data Management
  • Market Risk Management: Basel 2.5 and V@R
  • Hedging Foreign Exchange Risk
  • Optimal Asset Allocation
  • The Municipal Fiscal Circumstances Index for Predictive Modeling (MCFI)
  • Foreign Exchange Risks in Options
  • Reinsurance and Risk Management Strategies
  • Model Risk Management