BSc (hons) financial mathematics & statistics

3 Years On Campus Bachelors Program

London School of Economics and Political Science

Program Overview

The BSc (Hons) Financial Mathematics & Statistics gives you a rigorous grounding in mathematics, probability, statistics, and financial modelling — ideal if you enjoy math and want to apply it directly to finance, economics, actuarial science or data‑driven business contexts. The programme blends theoretical mathematical tools with statistical methods and financial applications, preparing you for careers in finance, banking, risk management, actuarial work, data analytics or further study.

Curriculum Structure

(Note: Publicly available details for this programme at LSE are limited. Based on typical structure for financial‑mathematics/statistics degrees, this sketch reflects what you might expect. For exact module names and year‑by‑year breakdown, refer to official LSE course documentation.)

Year 1

In the first year, you’ll build a strong foundation in mathematics and statistics — covering core topics such as calculus, linear algebra, probability and statistical theory. Simultaneously, you may also start introductory modules in financial concepts and data analysis, giving you early exposure to the financial contexts in which mathematical and statistical tools are applied. This combination ensures you gain quantitative fluency while understanding how statistics supports finance.

Year 2

The second year likely deepens both mathematical and statistical training: you may study advanced probability and distribution theory, statistical inference, regression and econometrics, plus modules in financial mathematics or quantitative finance. This strengthens your ability to model uncertainty, analyse financial data, and understand risk — skills critical for careers in banking, trading, insurance, or risk management.

Year 3 (Honours Year)

In the final year, you’ll specialise and apply your knowledge: you might choose advanced electives in financial mathematics (e.g. interest theory, stochastic processes, risk modelling), statistics, econometrics or data analysis. You’ll also undertake a capstone project or dissertation — applying mathematical and statistical methods to real‑world financial or economic problems. This final phase prepares you for a quantitative, finance‑oriented career or for further study (master’s or professional qualifications).

Focus areas

"Mathematics, probability and statistics, financial mathematics, risk modelling, econometrics, data analysis, quantitative finance, statistical inference."

Learning Outcomes

"You will graduate with strong quantitative, statistical and mathematical reasoning skills; the ability to model and analyse financial and economic data; competence in probabilistic and statistical methods; and capacity to apply mathematical models to problems in finance, insurance, banking, risk analysis or data analytics."

Professional Alignment (Accreditation / Career Relevance)

This degree aligns well with careers in finance, banking, insurance, risk management, actuarial science, quantitative trading, data analytics, and economic research. It also provides a solid foundation for postgraduate study in mathematics, statistics, financial mathematics, economics, or data science — and supports preparation for professional qualifications in finance or actuarial fields.

Reputation (Employability & Outcomes)

LSE is globally recognized for strength in mathematics, statistics, economics and finance. Graduates with strong quantitative and statistical training from LSE tend to be highly valued by top financial institutions, consulting firms, analytics companies and actuarial or risk‑management employers. A Financial Mathematics & Statistics degree from LSE signals rigorous training and strong employability in competitive quantitative fields.

Experiential Learning (Research, Projects, Internships etc.)

The BSc Financial Mathematics & Statistics at LSE is built for students who want to combine rigorous mathematics with practical statistical and financial applications. You will develop strong quantitative skills, statistical reasoning, and financial modelling expertise that are directly applicable to finance, risk management, investment, consulting, data analytics, and research.

The programme is jointly delivered by the Mathematics and Statistics Departments, giving you access to research-led teaching in financial mathematics, optimisation, probability, and data analysis. By the time you graduate, you will be able to apply mathematical and statistical methods to real-world financial problems, interpret data, and develop models used in industry.

🎯 Experiential Learning — What You’ll Do and Learn

Solid foundation in Year 1: Core courses include Mathematical Methods, Abstract Mathematics, Elementary Statistical Theory, and introductory economics, ensuring a robust base in mathematics, statistics, and economic context.
Advanced mathematical and statistical training: Later years cover probability theory, stochastic processes, statistical inference, optimisation, and discrete mathematics, preparing you for sophisticated quantitative applications.
Financial mathematics modules: You will study derivatives, pricing, hedging, investment theory, and risk measurement, gaining practical knowledge of models used in finance and insurance.
Computational methods and programming: The programme includes computational and numerical techniques for financial modelling, enabling you to implement models, handle datasets, and perform simulations.
Integration of maths, statistics, and finance: You learn how mathematical and statistical tools are applied in finance, economics, risk management, and data-driven decision-making.
Research-led teaching environment: Faculty are active in financial mathematics, statistics, and optimisation, ensuring teaching reflects modern methods and industry-relevant practices.
Career-focused outcomes: The degree equips you for roles in banking, investment, consulting, risk management, insurance, data analytics, or further postgraduate study in finance, mathematics, or statistics.

💡 Who This Degree Is Ideal For

Students with strong aptitude and interest in mathematics and statistics who want to apply them in finance, economics, or data analysis.
Those aiming for careers in quantitative finance, investment banking, risk management, insurance, or financial data analytics.
Students who want a rigorous, research-informed programme that balances theory with practical, industry-relevant skills.
Those seeking interdisciplinary training that integrates mathematics, statistics, finance, and computational methods.
Students considering postgraduate study in finance, financial mathematics, or statistics.

🌟 What Makes LSE’s Financial Mathematics & Statistics Stand Out

Quantitatively rigorous, blending mathematics, statistics, and finance for real-world applications.
Jointly delivered by Mathematics and Statistics Departments, providing depth in both areas.
Curriculum reflects modern methods in financial mathematics, statistical analysis, and computational modelling.
Graduates are highly employable in finance, insurance, data analytics, risk management, and research.
Versatile degree suitable for industry roles or further academic study.

Progression & Future Opportunities

The BSc Financial Mathematics & Statistics programme equips students with advanced mathematical, statistical, and analytical skills tailored for the financial sector. Graduates are trained in quantitative methods, risk modelling, and data-driven decision-making, preparing them for careers in finance, banking, insurance, investment, and actuarial roles. This combination ensures they are well-prepared for complex analytical challenges in global financial markets.

Typical career roles include:

Quantitative Analyst / Risk Analyst / Financial Modeller
Actuarial Analyst / Insurance Analyst
Data Analyst / Investment Analyst
Financial Consultant / Business Intelligence Analyst

University support & employability features:

Careers & Employability Service: one-to-one guidance, CV preparation, interview coaching, and industry networking tailored for finance and quantitative roles.
Industry-relevant projects: students apply mathematical and statistical methods to real-world financial data, enhancing practical skills and employability.
Professional accreditation and partnerships: training aligned with actuarial and financial mathematics standards supports future professional qualifications.
Placement opportunities: optional internships or industry projects provide real-world experience in finance, banking, insurance, or consultancy.

Employment statistics & salary outcomes:

Graduates in financial mathematics and statistics are highly employable, with a large proportion securing professional roles or further study within 6–15 months.
Starting salaries typically range from £28,000–£35,000, with rapid growth potential in quantitative finance, actuarial roles, and risk management.
Alumni work in investment banking, insurance, financial modelling, data analytics, and consultancy roles globally.

Industry relevance & long-term value:

The programme develops highly transferable quantitative, analytical, and modelling skills essential for financial and actuarial careers.
Graduates gain skills in probability, statistics, data analysis, financial mathematics, and risk modelling — core areas for modern financial sectors.
Strong training in practical applications ensures long-term relevance across finance, risk management, investment, and analytics industries.

Graduation outcomes:
Graduates leave with advanced mathematical, statistical, and financial modelling abilities, ready for professional roles in finance, actuarial work, risk analytics, or quantitative research, or for further postgraduate study.

Further Academic Progression:

After completing this programme, students may pursue:

Master’s degrees in Financial Mathematics, Quantitative Finance, Data Science, Actuarial Science, Risk Management, or Statistics.
Research degrees (MSc/PhD) in Quantitative Finance, Financial Mathematics, Actuarial Science, or Statistical Modelling.
Professional careers in finance, investment banking, insurance, consultancy, and risk management, often supported by strong technical and analytical training gained during the degree.