University course Β· real outcomes from HESA / Discover Uni Β· part of Careermash
University degree

Mathematics for Finance

Swansea University
Qualification
Degree
Length
3 yrs
UK fees / yr
Β£9,535
Study
full-time
Robin Β· your guide
Here's the honest picture on this course - what you'd study, whether you'd likely get in, what it pays, and where it leads. Everything's real data.
About this course

Mathematics for finance is a programme that addresses the mathematical foundations of modern financial theory and practice. Contemporary finance is built on quantitative models: options pricing, portfolio optimisation, risk measurement, and the valuation of complex financial instruments all require a level of mathematical sophistication that standard finance or business programmes rarely develop. A dedicated mathematics for finance degree gives you the mathematical depth to understand these models, evaluate their assumptions, and apply them in professional contexts where quantitative rigour matters.

At Swansea University, this three-year full-time programme reflects the close connection between mathematics and industry, developing both the pure mathematical foundations and the specific applications of mathematics to finance and business. You will study calculus, linear algebra, probability theory, and statistics alongside financial mathematics topics including stochastic processes, derivatives pricing, and risk theory, building a rigorous quantitative toolkit that prepares you for the analytical demands of financial professional life. Swansea has a strong mathematics department with good connections to business and finance, and the programme benefits from teaching staff who combine mathematical expertise with an understanding of how quantitative methods are used in practice.

A typical entry tariff of 104 points reflects an accessible entry standard within a programme that builds serious mathematical and financial competence progressively. The programme is particularly suitable for students who are strong mathematically and want to develop that strength in a finance-oriented direction without sacrificing mathematical rigour.

Graduates work in banking, investment, insurance, risk management, and financial technology, as well as in any sector where quantitative analytical skills are valued. Many pursue professional qualifications in actuarial science, quantitative finance, or data science, and postgraduate study in financial mathematics or statistics is a natural further option.

Could you get in?
The grades students arrived with
<48 pts2%
48-63 pts4%
64-79 pts9%
80-95 pts22%
96-111 pts22%
112-127 pts12%
128-143 pts8%
144-159 pts4%
160-175 pts5%
176-191 pts5%
192-207 pts2%
208-223 pts2%
240+ pts2%
How they qualified
95% got in with A-levels. The rest came in a mix of ways:
A-levels95%
another degree3%
an Access course2%
Could I get in? Try your grades
120 UCAS pts
Pay & prospects
88%
Continue past first year
91%
Student satisfaction
What graduates earn over time
Β£25,000
3 years on
Β£32,000
5 years on
What students say National Student Survey
91%
of students are satisfied with the course overall
Teaching86%
Assessment & feedback82%
Academic support85%
Well organised92%
Learning resources89%
Student community96%
In students' own words
β˜…β˜…β˜…β˜…β˜…
A great decision
Honestly, one of the best decisions I've made. The best part: the cohort are bright and motivated, which makes the learning environment great. If I'm honest, would like to see more industry guest speakers in later years. On the city β€” the c…
Class of 2023 Β· Part-time
β˜…β˜…β˜…β˜…β˜…
Highly recommend
The course has exceeded my expectations. The best part: the careers office is relentless about getting you internships. If I'm honest, would like to see more industry guest speakers in later years. On the city β€” local cost of living is mana…
Class of 2024 Β· Full-time
More courses like this
Where this degree can lead
Like the look of it?
When you're ready, the full entry requirements and application are on Swansea University's own site.
Apply on uni site
Careermash Β· real course data from HESA / Discover Uni, in plain English.

Career data: role, pay and progression profiles built for Careermash's careers engine; AI-impact estimates from Anthropic's observed AI-usage telemetry and OpenAI's AI Jobs Transition Framework. Course data: HESA / Discover Uni, including Graduate Outcomes, LEO and the National Student Survey. Apprenticeships: IfATE-published standards, approved only.

Β© 2026 Careermash. A concept for secondary schools.