LSE - MSc Financial Mathematics

[TLDR at the end] I will not go through the aims and objectives of the master’s program here. You can find that on the course website, so please do your own mandatory research to have a basic intuition of the program at LSE that you are paying £40k for - higher if you are reading this in a few years' time.

The program, run by the Department of Mathematics is (was) structured as follows: there will be a 2-week pre-sessional course that aims to introduce some measure theory and stochastic calculus, including, but not limited to, martingale theory, brownian motion, Ito’s Lemma, Girsanov’s Theorem and the Radon-Nikodym Derivative. You will take a non-credit-bearing, informal assessment at the end of the two weeks to consolidate their understanding of the material. I personally did not find the material difficult, as I had exposure to said topics at the undergraduate level, albeit at a shallower level.

Then come the 8 courses that form the master’s program as a whole. For context, I was a student from the 24/25 cohort. The composition of mandatory and optional courses may have changed throughout the years. But during my time, I had to take 5 compulsory courses and only got to choose 3 electives. Now, research the assessment criteria and method yourself, i.e., if it is coursework-oriented or exam-heavy. You can click into each module on the course webpage to find self-contained information, which is all that you need - I want to and will focus on aspects of the program that are not (immediately) obvious.
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The lack of flexibility in the aspect of course selection was quite disappointing, considering that the ‘sister program’, Quantitative Methods for Risk Management (QMRM), run by the Department of Statistics, had way more course choices that comprise the electives portion of the course, at least when I was studying there in 2024/2025. So, QMRM gets 2 points over Financial Mathematics.

I would also like to encourage you to go through the graduate course guides for the program meticulously before selecting your electives based on random decision generators. These graduate course guides are freely available to view on Google, even if you are not a student at LSE and reading this review just for your own pleasure on QuantNet. The 2025/2026 graduate course guides can be found by searching ‘LSE 2025 Graduate Course Guides’.

Now, THIS IS THE MOST IMPORTANT PARAGRAPH IN THE REVIEW. Please get a feel for the available electives for both the Financial Mathematics program and the QMRM program - these two programs have a high degree of overlap, at least when I was a student there. Personally speaking, if you go into this program aiming to acquire the foundational skills for the (quant, AI, ML) industry, then the QMRM program as a whole will suit you much better, as the Financial Mathematics program is fairly theoretical and does not dive into the applications and implementations that are absolutely compulsory for the industry. You just have to compare the elective selections for both programs to verify my proposition. So, important things to do : compare the course structure for Financial Mathematics and QMRM, and hopefully sufficiently validate my proposition.

Note that you do not necessarily have to have chosen a particular course to access the respective course materials. You can audit most courses as an ‘Auditing Student’. At least that was the case when I was studying there in 2024/2025. Obviously audit the courses that you are interested in on top of your 8 modules, not the other way around, as you are still a student, or became a student - and so have courseworks and exams.

Here I list the modules that are in both the Financial Mathematics and QMRM programs that I think are useful, or somehow beneficial in the future : Computational Methods in Finance, Statistical Methods for Risk Management, Stochastic Simulation Training and Calibration, Topics in Financial Mathematics, Quantitative Methods for Finance and Risk Analysis. Advanced Time Series Analysis.

I want to give a special shoutout to the course MA420 Topics in Financial Mathematics and Johannes Ruf, who taught this course during 2024/2025. This module brought me from Level 0 to Level 5 (or any arbitrary numerical figure that you deem appropriate) in working with financial data. For context, before taking this course, I had literally zero exposure to the pandas library, and hence, hadn’t read a single CSV file and hadn’t extracted a column in a dataframe. This course has been my lifesaver.

SECOND MOST IMPORTANT PARAGRAH IN THE REVIEW. I want to justify my choice of headline. Most people tend to expect Financial Mathematics to be a ‘mix of finance and mathematics’. Fact is, the discipline itself is a rigorous exploration of (very) advanced topics like stochastic processes and partial differential equations, and probability theory. You can have a look at the MSc Mathematical Finance program offered by Warwick Business School. On the other hand, the LSE Financial Mathematics program is pieced together by courses from the Mathematics, Finance, and Statistics departments, and each department teaches its segment in relative isolation, resulting in a curriculum that mirrors the misconception (that I pointed out) of a ‘mix’, rather than an axiomatic whole. Hence, I prefer to name this program Mathematics and Finance, albeit seeing some improvements on the holisticism of the program when they incorporated a new module from the Statistics department, ‘Mathematics of Market Microstructure’.

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Other key facts or opinions
Applied AND received my offer in March 2024
Undergraduate Degree : Kings College London Mathematics. Close to First Class Honours when I applied. Performed fairly well in key modules. Transcript available upon private message through my LinkedΙn - we can set up a meaningful discussion.
Other university offers received for the same subject : Kings College London (KCL), Warwick (WBS), UCL, rejected by Imperial.
Conditional Offer : 2.1 on the UK scale (60%)
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TLDR : Choose MSc QMRM (Quantitative Methods for Risk Management) over MSc Financial Mathematics to put yourself in a better position before going into the industry. On the other hand, choose MSc Financial Mathematics if you want to stay in academia and pursue a PhD.