- Reading time: 3 minutes
- Price: Free download
- Published: 16th September 2026
- Word count: 901 words
- File format: Text
Why do you want to study this course or subject?
What pulled me towards financial mathematics was realising that uncertainty can be handled rather than simply worried about. In my statistics lessons I liked the moment when a messy situation, such as how often a machine fails, became a distribution with parameters we could argue about. Later, reading about how insurers set premiums, I saw the same idea applied to money: nobody knows which house will flood, but with enough data and sensible assumptions you can price the risk and still stay solvent. That combination of careful probability and real consequences interests me more than pure theory alone.
My Economics course gave me the other half of the picture. We studied how interest rates influence borrowing and saving, and I became curious about how a bank decides what a thirty-year commitment is worth today. Discounting seemed almost too simple until I thought about what happens when the rate itself moves unpredictably. I began following financial news properly, and the debate about how much of a market fall reflects new information and how much reflects everyone reacting to everyone else struck me as a genuinely mathematical question.
I want a degree where analysis, probability and programming sit alongside finance rather than being decoration. I would like to study stochastic processes and numerical methods and understand why a model works before trusting its output, since a formula applied without its assumptions is worse than no formula at all. In the longer term I am drawn to risk or actuarial work, where the mathematics has to be explained to people who will not read the derivation.
How have your qualifications and studies helped you to prepare?
Mathematics and Further Mathematics have given me the tools I expect to use most. The statistics modules taught me hypothesis testing and the normal and binomial distributions, while Further Maths introduced complex numbers, matrices and differential equations. I found first-order differential equations particularly satisfying because modelling population growth showed how quickly a small change in assumptions alters a prediction. Proof by induction was harder; I worked through past paper questions with a friend until I could set out the inductive step clearly rather than gesturing at it.
Economics has trained me to write arguments that survive scrutiny. Essays on monetary policy forced me to be explicit about what I was assuming and to admit where evidence was mixed, which is a habit I think transfers to modelling.
For my Extended Project I modelled the finances of a school raffle. I built a spreadsheet simulating ticket sales and prize costs, then used a simple random sampling approach to estimate the range of possible profits instead of quoting one figure. Comparing my estimate with the eventual result taught me that my sales assumption, taken from one previous year, was the weakest part of the model. Writing the evaluation section honestly was more useful than the arithmetic.
Alongside lessons I have taught myself basic Python, mostly loops, functions and using pandas to summarise a dataset of daily share prices I downloaded. I worked out the standard deviation of daily returns and was surprised how much it varied between periods. I have also read The Signal and the Noise by Nate Silver, which sharpened my thinking about the difference between a confident forecast and a well-calibrated one.
What else have you done to prepare outside of education, and why are these experiences useful?
On Saturdays I work in a family-run hardware shop, mainly on the till and the returns desk. Customers arrive with part-used tins of paint, missing receipts and firm opinions, and I have learned to stay calm, explain the policy plainly and find a solution the owner will accept. I also help with stock counts, which showed me how easily small recording errors accumulate across a month and why reconciliation matters. Handling cash at closing time made me careful about checking figures twice rather than trusting a total that looks about right.
For three years I have helped run the junior sessions at my badminton club. I set up courts, keep the register and run warm-ups for around fifteen children aged eight to twelve. Last year I took over the subscription spreadsheet, moving it from scattered notes into one sheet that tracks who has paid and flags anyone two sessions behind. It saved the treasurer an evening of chasing each term, and he asked me to keep doing it. Explaining a serve to a nine-year-old has also improved how I explain things generally, which I noticed when tutoring two Year 9 pupils in maths through a school scheme.
Outside this I play in the school chess club and reached the semi-final of our internal tournament, which taught me to think several moves ahead under time pressure. I run three times a week and keep a simple log of times and distances, partly out of interest in whether the training is actually working.
These commitments have made me organised and comfortable with responsibility for other people's money and time, however modest the sums. I expect a demanding degree, but juggling shifts, coaching and Further Maths coursework has shown me I work steadily rather than in panicked bursts, and I am ready for the step up.
This example has 5,113 characters across the three answers. Use it for ideas and structure. Your own UCAS answers must fit within 4,000 characters in total, including spaces.
Why this example works — strengths and ways to improve
This is a thoughtful, well-grounded financial mathematics statement with genuine reflection, especially on model assumptions and their limits. Its main problem is length: at about 5,113 characters, it exceeds the 4,000-character UCAS limit. Some outside activities also feel loosely connected. Cutting the weaker material and sharpening the links to the subject would produce a strong, credible statement.
Subject motivation
nobody knows which house will flood, but with enough data
You show motivation through a precise idea, pricing uncertainty, rather than through generic enthusiasm. The insurance example links probability to real consequences convincingly. The market-fall paragraph is interesting, but it adds breadth more than depth, so it is a candidate for trimming when you cut for length.
Academic preparation
modelling population growth showed how quickly a small change in assumptions alters
Your coursework evidence is specific and relevant, covering statistics, differential equations and induction. You go beyond listing topics: the induction struggle shows how you actually learn. The Economics paragraph is brief but useful, because it connects explicit assumptions to modelling without overclaiming.
Evidence and reflection
my sales assumption, taken from one previous year, was the weakest part
The raffle project is excellent evidence because it is modest, real and honestly evaluated. Identifying the weakest assumption shows genuine modelling judgement. The Python returns analysis works similarly. However, "surprised how much it varied" could say what you concluded about volatility or about choosing the time period.
Relevant experience
how easily small recording errors accumulate across a month and why reconciliation
Your shop and club work are well chosen. The subscription spreadsheet shows initiative with a concrete outcome. The chess and running sentences are weaker, especially the generic line about thinking several moves ahead under time pressure. These are the clearest material to cut if you need space.
Credibility and voice
however modest the sums
Your tone is appropriately humble for a school applicant. You avoid professional overclaiming and show self-awareness about scale. The voice is personal and economical throughout. The closing claim about being "ready for the step up" is the only generic note, and the evidence earlier in your statement already makes that point.
Structure and format
Each answer is clearly over 350 characters and distinct, with little duplicated evidence. However, the total of roughly 5,113 characters is over 1,100 above the 4,000-character limit. Substantial cuts are required while staying above the library's 3,500 minimum. The first answer and the outside-education answer have the most room to cut.
What you’ve done well
- Your raffle Extended Project shows real modelling reflection, because you name the weak assumption and value an honest evaluation over the arithmetic.
- Ordinary experiences, such as stock counts and the subscription spreadsheet, are linked concretely to accuracy and responsibility rather than to vague skill claims.
- Your recurring theme of understanding assumptions before trusting outputs gives the three answers coherence without forced links.
How this draft could improve
- Cut about 1,100 characters to meet the 4,000 limit. Start with the chess and running paragraph, the market-fall discussion and the closing summary sentence.
- After cutting, aim for a total somewhere between 3,500 and 4,000 characters, keeping each answer above 350 characters.
- Turn your share-price observation into a brief conclusion about what the varying volatility taught you, rather than ending at surprise.