Kings' College London - MSc Financial Mathematics

The course begins with a one-week, zero-credit module, 7CCMFM00 Mathematical Analysis for Financial Mathematics. This intensive refresher covers the key undergraduate mathematical tools needed for the rest of the programme. It is especially valuable for students, like myself, who did not complete an undergraduate degree in mathematics.

There is a wide range of optional modules, with only two compulsory ones: 7CCMFM01 Probability Theory and 7CCMFM02 Risk Neutral Valuation: Pricing and Hedging Derivatives. These form the core of the programme, and the concepts taught in them transfer naturally to many of the other modules.

Among the electives, my favourites were 7CCMFM12 Incomplete Markets and 7CCMFM20 Stochastic Control and Applications to Algorithmic Trading. Both were highly focused and tightly aligned with the research interests of the professors teaching them.

Once all modules are completed, work on the Master’s project begins in May. Students choose three preferred project topics, and supervisors are allocated accordingly. Across the May–August project period, you are given only three supervision sessions in which you may ask questions.

Overall, the degree is very well structured, with lectures delivered to a high standard. However, in terms of career support, there is relatively little guidance, and students are largely expected to seek out help independently.

I completed the MSc Financial Mathematics at KCL, having previously done my undergraduate BSc in Mathematics with Management and Finance at the same university. My decision to pursue this program was heavily influenced by Dr. Ryan Donnelly (who became the programme director later), whose overview of the course content convinced me it was the right fit. The central London location and relatively affordable home fees (~£12,500) compared to competitors like LSE were additional deciding factors. While I ultimately didn't land in quant finance due to the competitive job market and my gaps in algorithmic knowledge, the programme provided solid preparation for those committed to the field.

Application Process and Prerequisites:
Coming from a mathematics background at KCL made the transition easy, but I'd strongly advise future students without prior exposure to financial derivatives to review "Options, Futures, and Other Derivatives" by J. Hull during the summer before starting. This groundwork will be helpful during the academic year, particularly for the compulsory modules.

Modules and Course Content:
The programme structure includes two compulsory modules:
- Probability Theory [7CCMFM01]
- Risk Neutral Valuation [7CCMFM02]
which form the foundation for everything else. While the lectures for these are solid, the assessments are notably more challenging than some students expect.

The quality of modules varies significantly.Personally, I would highlight these modules:
- Numerical and Computational Methods in Finance [7CCMFM06] - exceptionally well-organised and exceptional lecturer
- Stochastic Control [7CCMFM20] - clear structure, interesting content and excellent lecturer
- Machine Learning [7CCMFM18] - relevant and well-taught
- C++ for Financial Mathematics [7CCMFM13] - valuable as a starting point
However, some modules like Statistics in Finance [7CCMFM05] required piecing together content from lectures and video recordings, demanding extra time to organise notes.

Teaching and Learning Experience:
The programme is heavily self-directed, with lectures being more directional than explanatory. Professors provide the framework and direction, but students must deeply understand topics through independent study before attending small-group tutorials. This approach initially felt challenging but eventually developed the self-sufficiency needed in industry.
My advice would be to balance hardcore mathematical modules with programming-focused ones. Given that even standard quant roles (not just hedge funds) require OOP language proficiency, modules like Numerical and Computational Methods in Finance and C++ for Financial Mathematics are invaluable.

Research Project/Dissertation:
The dissertation demands full-time commitment and consists of three parts:
1. Literature Overview - reviewing existing methods and relevant theory
2. Numerical Experiment - demonstrating skills acquired during the programme (fixed for all students)
3. Personal Contribution - extending the topic through different models or practical applications
The supervision is intentionally limited (only 3 meetings allowed in our year), forcing independent research skills. Initially frustrating, I later appreciated this as it provides real-world experience. The topics are generally applicable across quant roles and valuable for CVs.

Career Services and Outcomes:
Career services are limited and most suited for undergraduate students new to internship applications. While career fairs invited companies like Barclays, G-Research and Millennium, these were general networking opportunities. None of my friends heavily engaged with career services. Success still requires grinding through hundreds of math and LeetCode problems independently.
The job market reality is harsh. Despite the programme's quality, the quant finance field is extremely competitive with market surplus. Some peers successfully landed roles at J.P. Morgan or pursued PhDs, but many (myself included) found the combination of competition and gaps in algorithmic optimisation knowledge challenging to overcome.

Overall, this is a solid programme for those certain about pursuing quant finance and willing to dedicate a full year to learning challenging material. The flexibility to combine mathematics with programming across multiple languages is one of the programme's greatest strengths. However, success requires supplementing course work with external preparation (Quant Green Book, LeetCode) to be competitive in the job market. If you're committed to becoming a quant and can handle self-directed learning, this programme offers good value for money and comprehensive preparation.

Summary:
I was originally attracted to apply to this course due to its relatively low level of tuition fees (~£12,000 for home students), and the option to pursue this course part-time. I completed this course while employed full-time in a Data Science graduate scheme across two years, as I wished to move towards a role in quantitative finance. I managed to graduate with Distinction, and a high-ranked dissertation, leading to a quant role within a systematic hedge fund.



Application Process:


Simple process with just a personal statement, however I would make sure that your undergraduate programme has the right level of mathematics taught for this course, you should have some familiarity with Multivariable Calculus and know how to solve differential equations at A-Level Further Maths or 1st year of undergraduate level.

Modules and Course Content:


Throughout the course, you are expected to complete 8 taught modules during the main semesters, and a research project + dissertation running from May-August.

The course begins with a non-credit week on Mathematical Analysis to get students up to speed in the style of teaching expected from a degree in the Mathematics department. I have spoken to plenty of students who did not come from a direct Mathematics/Statistics background that found later modules difficult as they were not familiar with the Definition/Theorem/Proof-style teaching of rigorous maths courses.

Afterwards, the first term consists of 2 compulsory modules (Probability Theory and Risk-Neutral Valuation) which forms the basis of much of the content taught in other modules offered by the department.

The content taught here was well-timed as they form the basis of what’s expected in quant interviews, I recommend students use this material directly for their interview preparation as soon as they start the programme.

The rest of the 6 modules you need to pick are entirely decided upon by you and are held across the different semesters. In particular, I had picked Computational Finance (Python), Stochastic Analysis, Machine Learning, Stochastic Control and Algorithmic Trading, C++ in Finance, and Interest Rates & Credit Risk.

Other options in the department focus on areas such as Statistics in Finance, Financial Markets, and even modules offered by the CS department such as High-Frequency Trading.



The teaching in general can be quite good to sometimes not so great. My favourite modules in the programme were Computational Finance (John Armstrong), Stochastic Analysis (Ryan Donnelly), and Algorithmic Trading (Ryan Donnelly). These lecturers here were very passionate about their work and it made a difference in my own learning of the material.

I would say the enjoyment and satisfaction you receive from the modules and teaching really depends on your work ethic as a student. I personally took time off from my full-time role to make an effort to attend the small group teaching classes for each module, which I found to significantly improve my understanding of the content (and in turn help my exam grades too). 



However these classes were often attended by less than a quarter of the students taking the respective module and many of these missing students often complained about the high difficulty of the material taught - beware, it’s not an easy or free degree in any sense.


Research Project and Dissertation:

With the research project and dissertation, you get to pick from three overarching themes each year, which are quite broad and cover a variety of aspects taught across the different modules.

During my year, the themes covered deep learning for option pricing, algorithmic trading + market making, and fractional brownian motion/rough volatility. Each of these projects do correspond to a couple of different modules taught in the programme, so pretty much anything you learn will be of use somewhere.



How you approach the specific contents of the dissertation is open-ended, but each theme has a specific range of tasks set out for you to get familiar with the theme and research topic, covering about 30% of the available marks. The rest of the marks come from an independent extension of the theme. The project also heavily emphasises on the use of programming to validate your work and to showcase your results, which I find reminiscent of how research in industry is truly done.



Throughout the dissertation, you have the opportunity to discuss your ongoing work with an academic supervisor at a few key stages. It’s really crucial that these meetings are attended with a substantial amount of progress in-between as to get proper feedback on the direction of your work. I made the mistake of not making much progress on my first meeting, but then really pushed far ahead for my second, which provided me with useful feedback for the final sections of my work.

This dissertation served as a major project for me when applying for roles in quantitative finance, and I was certainly able to learn a variety of applicable skills that resonated with different roles and teams in the industry, especially the ability to translate mathematical theory into code.

Top ranking students in the two compulsory modules can get offered to be paired up with an industry partner (Santander) for their projects.

Finally, high ranking students in the research project/dissertation can be offered PhD positions within the university.

Careers Services and Destinations:


KCL is in a good location being in central London which opens up accessibility to many careers events not just at KCL but also at LSE, UCL, and Imperial. If you’re keen on networking then you won’t find a shortage of it in London.

I didn’t particularly interact with the university’s own careers services much and neither did my friends. I would say this area could be something KCL is lacking in if you’re expecting careers support of the level of LSE for example.



As for destinations, many of my closest peers are indeed in strong careers at financial institutions including large banks, hedge funds, and exchanges, across a variety of different roles from quant research to financial risk analysis. 

KCL’s reputation doesn’t quite fall into the radar of the most elusive quant destinations, but one can certainly jump into a successful career from here. 



Though ultimately, I found this to be an appropriate course for what I was looking for and found the career that I desired and it was incredible value for money comparing other similar courses which can run over £25k for even home students. But if offered, I would definitely have picked Imperial, LSE, UCL due to their relevance and reputation in quantitative finance hiring.

The course is good, you have plenty of options to choose from, the professors are highly educated, very knowledgeable have written books, and respected in quant field. But there is little to no help for career services. You are left alone to find the job. You have to self study a lot. The professors in Class just give you like a overview of the topic. It's mostly self study. The paper pattern in the for the Exam is not good. You have for questions and you have to solve any three so each carry like 33 marks. You don't get to choose your own dissertation topic. You are presented with three topics, and you have to choose one so your classmates will be working on same thing, with nearly same results. There are no internal marks. Most of the exams are hundred percent written paper.
As it is a master course, everyone is very busy. No one is networking. Everyone just comes to the unique studies and leaves. I have seen my course mates get good roles in companies and I've also seen people not being able to get a job. It depends on your skill set. There large emphasis on python code, but you also get to code on R and C++.
Overall, it's a good course, but if you get the same subject at LSE or Imperial or UCL, choose that.

The content of this course is high-quality and has a standard similar to that of other big-name universities in the UK, which charge much higher tuition fees. Many of the lecturers are excellent, however there are a small number whose optional courses are avoided with good reason. The application process is simple and rarely requires an interview. The quality of students is varied, with many performing exceptionally well throughout the course and going on to study PhDs or acquire excellent graduate jobs, and others who struggle in many modules. There are career services provided, and the University has an industry partner in Santander UK, with whom a small number of students may be selected to complete their dissertation project in the summer. Overall I would recommend this course especially due to its value for money for home students and the content covered, but it may lack the industry prestige of the top UK universities.