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Who Should Apply?

Admission to the professional Master of Financial Insurance (MFI) Program is on a competitive basis and candidates for this degree program are accepted under the general regulations outlined in the calendar of the School of Graduate Studies.

The MFI program is a mathematically sophisticated program, and students graduating in a quantitative field such as: actuarial science, statistics, economics, mathematics, or engineering and who are keen to pursue a career in the finance, insurance, fintech, or consulting sectors are encouraged to apply. Students should already have undergraduate training in multivariable calculus, linear algebra, probability, statistics, and computation to be a competitive applicant. Students with business undergraduate degrees do not have enough quantitative training to be successful in the program.

Below you will find a collection of suggested courses (using the University of Toronto course codes), as well as brief course descriptions, that we recommend students complete before applying to the program:

Interest, discount, and present values, as applied to determine prices and values of annuities, mortgages, bonds, equities; loan repayment schedules and consumer finance payments in general; yield rates on investments given the costs on investments.

Term structure of interest rates, cashflow duration, convexity, and immunization, forward and futures contracts, interest rate swaps, introduction to investment derivatives and hedging strategies.

Mathematical theory of financial derivatives, discrete and continuous option pricing models, hedging strategies, and exotic option valuation.

Applications of the lognormal distribution, Brownian motion, geometric Brownian motion, martingales, Ito’s lemma, stochastic differential equations, interest rate models, the Black-Scholes model, volatility, value at risk, conditional tail expectation.

A conceptual approach for students with a serious interest in mathematics. Attention is given to computational aspects as well as theoretical foundations and problem-solving techniques. Review of Trigonometry. Limits and continuity, mean value theorem, inverse function theorem, differentiation, integration, fundamental theorem of calculus, elementary transcendental functions, Taylor’s theorem, sequence and series, power series.

Sequences and series. Uniform convergence. Convergence of integrals. Elements of topology in R^2 and R^3. Differential and integral calculus of vector valued functions of a vector variable, with emphasis on vectors in two and three dimensional euclidean space. Extremal problems, Lagrange multipliers, line and surface integrals, vector analysis, Stokes’ theorem, Fourier series, calculus of variations.

Systems of linear equations, matrix algebra, real vector spaces, subspaces, span, linear dependence and independence, bases, rank, inner products, orthogonality, orthogonal complements, Gram-Schmidt, linear transformations, determinants, Cramer’s rule, eigenvalues, eigenvectors, eigenspaces, diagonalization.

Abstract probability and expectation, discrete and continuous random variables, and vectors, with the special mathematics of distribution and density functions, all realized in the special examples of ordinary statistical practice: the binomial, poisson and geometric group, and the gaussian (normal), gamma, chi-squared complex.

Statistical models, parameters, samples, and estimates; the general concept of statistical confidence with applications to the discrete case and the construction of confidence intervals and more general regions in both the univariate and vector-valued cases; hypothesis testing; the likelihood function and its applications; time permitting: the basics of data analysis, unbiasedness, sufficiency, line+D5ar models and regression.

Introduction to data analysis with a focus on regression. Initial Examination of data. Correlation. Simple and multiple regression models using least squares. Inference for regression parameters, confidence, and prediction intervals. Diagnostics and remedial measures. Interactions and dummy variables. Variable selection. Least squares estimation and inference for non-linear regression.

Probability from a non-measure theoretic point of view. Random variables/vectors; independence, conditional expectation/probability, and consequences. Various types of convergence leading to proofs of the major theorems in basic probability. An introduction to simple stochastic processes such as Poisson and branching processes.

Practical techniques for the analysis of multivariate data; fundamental methods of data reduction with an introduction to underlying distribution theory; basic estimation and hypothesis testing for multivariate means and variances; regression coefficients; principal components and partial, multiple, and canonical correlations; multivariate analysis of variance; profile analysis and curve fitting for repeated measurements; classification and the linear discriminant function.

It is not necessary to have all the above courses, rather we seek students with a solid breadth and depth in their training to be successful in the program.

The MFI Program has only one start date each year in the fall term (September) and is only offered on a full-time basis, and students are expected to be available for a scheduled series of orientation events and informational workshops and bootcamps from the last week of August.

The MFI Program only accepts applications through the UofT online application system GradApp. Online applications open on October 1 and close mid-January (no late applications will be considered). Your application will only be considered after you have included all the required application materials and paid the School of Graduate Studies’ application fee currently set at $125 (subject to change).

The admission committee takes a comprehensive approach to admissions and considers each applicant individually with the goal of admitting the most exceptional candidates well suited to the program. Candidates are evaluated on a complete portfolio of academic performance, personal characteristics, and life experiences, as well as academic and professional (if applicable) references, and a personal statement or letter of intent. International students with an undergraduate degree from non-English teaching institutions must submit an Official IELTS (International English Language Testing System) or TOEFL (Test of English as a Foreign Language) score report before the application deadline that meets SGS (School of Graduate Studies) minimum standards in all sections.

Once a completed application is reviewed, shortlisted applicants will receive an invitation to an in-person interview where possible, or virtually if not in the Toronto area, with one or more members of the MFI Admissions Committee. Please note that not all applicants will receive an invitation to interview. Here are some useful tips when preparing for a Virtual interview or meeting.


Admission to the MFI Program is highly competitive and we will only extend offers to the most qualified applicants. We admit around 30 students into the cohort.
If we extend an offer of admission, applicants must accept (or decline) our offer in a timely fashion (within 2 weeks) and submit a non-refundable deposit.

We do not grant deferrals. Interested students are expected to apply in the year they are available and ready to start the program.


Admission Requirements

All applicants must have:

  • Completed or be the process of completing an appropriate undergraduate bachelor’s degree, from a recognized university in a related field such as Statistics, Actuarial Science, Economics, Mathematics, or any discipline with a significant quantitative component.
  • A standing equivalent to at least B+ (U of T = 77-79% or 3.3/4.0 GPA) in their final year of study (the equivalent of 5 senior level full credits, or 10 half credits).
  • Achieved English Language Proficiency (if applicable) according to the requirements set by the School of Graduate Studies.


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