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The MSci (Hons) in Financial Mathematics is a four-year programme that provides advanced mathematical and financial training without a professional placement. It is designed for students who want to develop strong quantitative, analytical, and computational skills to pursue careers in finance, banking, risk management, or actuarial science.
Curriculum structure:
Year 1
The first year builds a strong foundation in mathematics, statistics, and introductory finance. Students study Applied Calculus, Applied Probability & Statistics, Numbers, Sets and Functions, Introduction to Probability, and Computing and Data Analysis with Excel, equipping them with essential analytical, statistical, and computational skills.
Year 2
The second year develops advanced mathematical and statistical knowledge, while introducing specialized finance concepts. Core modules include Statistical Modelling I, advanced probability and statistics, and electives such as Actuarial Mathematics I, Complex Variables, Introduction to Algebra, or programming/data-analysis electives, allowing students to begin focusing on finance or actuarial science.
Year 3
The third year focuses on more advanced mathematical finance and stochastic modelling. Modules include Financial Mathematics I, Stochastic Processes, Random Processes, and options in derivative pricing, numerical methods, and computational finance. This year prepares students for their final-year research and specialization.
Year 4 (Final Academic Year)
The final year emphasizes advanced applications and independent research. Students undertake the MSci Project and study modules such as Financial Mathematics II, Advanced Stochastic Modelling, Numerical Methods in Finance, and electives like Machine Learning for Finance or Computational Finance. This year allows students to specialize in quantitative finance, risk analysis, actuarial science, or computational finance.
Focus areas:
Quantitative mathematics, probability and statistics, stochastic processes, mathematical finance, statistical modelling, computational finance and data analysis, financial instruments and markets, risk modelling, actuarial mathematics.
Learning outcomes:
Graduates develop strong analytical and quantitative skills, proficiency in mathematical finance and statistical modelling, and the ability to apply computational methods to financial problems. They are well-prepared for careers in banking, risk management, finance, insurance, and actuarial or quantitative analysis roles.
Professional alignment (accreditation):
The programme is offered by the School of Mathematical Sciences and meets high academic and professional standards in mathematics and financial applications.
Reputation (employability & career prospects):
Graduates are highly sought after by global financial institutions, consulting firms, insurance and actuarial companies, and other data-driven industries. The programme equips students with the skills required to excel in high-demand sectors of finance and quantitative analysis.
The MSci Financial Mathematics programme at Queen Mary offers an advanced, four-year course that combines rigorous mathematics with deep training in financial theory, numerical methods, and computational finance. It is designed for students who want both a strong theoretical foundation and the quantitative, technical skills needed for careers in finance, risk management, investment, and quantitative analysis.
Experiential Learning & Practical Exposure
Students in this MSci programme benefit from structured coursework, computational training, and real-world relevance as they build mathematical and financial expertise:
Specialist modules in financial mathematics, numerical methods, stochastic processes, and computer programming enable students to model financial markets, price derivatives, and perform quantitative risk analysis.
Emphasis on computational competence, including numerical computing and programming (e.g., C/C++), equips students to implement mathematical models and computational solutions.
Advanced financial mathematics modules in final years include a substantial MSci project, allowing students to engage in research-style work or real-world modelling problems.
Balanced course structure: strong pure and applied mathematics foundation in early years, followed by finance and computational specialisation in later years.
Access to high-quality teaching spaces, group and private study areas, and a supportive academic environment through the School of Mathematical Sciences.
Programme Structure & What Students Learn
Years 1 and 2: Robust foundation in pure and applied mathematics, developing problem-solving, analytical, and mathematical reasoning skills.
Year 3: Specialist financial mathematics training, including Financial Mathematics I & II, Mathematical Tools for Asset Management, Numerical Computing, Partial Differential Equations, and Random Processes.
Final Year (Year 4): Master-level finance-oriented modules such as Financial Instruments and Markets, Foundations of Mathematical Modelling in Finance, and the MSci Financial Mathematics Project. Optional electives allow focus on advanced derivatives pricing, risk management, continuous-time models, programming for finance, stochastic processes, or asset analytics.
Why This Degree Gives a Competitive Edge
Graduates gain strong mathematical depth combined with specialised financial-mathematical and computational skills, making them highly competitive for careers in quantitative finance, risk, banking, asset management, derivatives, or financial engineering.
Numerical methods, stochastic processes, and programming training equip graduates with practical tools for modern finance, enhancing employability for quantitative roles.
The final-year MSci project provides experience in research and complex problem-solving, valuable for finance industry roles or further academic study.
The programme’s structure ensures versatility: a strong mathematical foundation with applied finance specialisation allows graduates to pursue a wide range of careers while maintaining advanced mathematical skills.
Graduates of the Financial Mathematics MSci (Hons) at Queen Mary University of London (QMUL) are prepared for careers such as quantitative analyst, risk analyst, financial engineer, investment analyst, or actuarial consultant. The programme equips students with advanced mathematical and computational skills, making them highly competitive in finance, banking, insurance, investment, and quantitative consultancy sectors.
Why This Degree Opens Doors:
University‑supported employability services: The QMUL School of Mathematical Sciences provides careers support including CV and application coaching, interview preparation, networking events, and guidance on internships.
Strong employment record & starting salary: A high proportion of graduates secure employment or further study within six months of graduation, with typical starting salaries around £32,000–£35,000.
Access to industry-leading employers: Graduates have opportunities with leading financial and consultancy firms such as J.P. Morgan, Goldman Sachs, Morgan Stanley, HSBC, Deloitte, and KPMG.
Accredited, high‑quality academic training with long-term value: The programme combines advanced mathematics with financial theory, stochastic modelling, computational finance, and risk management, providing a solid foundation for analytical and quantitative roles.
Diverse graduate outcomes: Graduates are equipped for careers in quantitative finance, investment banking, risk management, financial modelling, and actuarial work, both nationally and internationally.
Further Academic Progression:
After completing the MSci, graduates may pursue Master’s or doctoral programmes in quantitative finance, financial mathematics, computational finance, risk management, or applied mathematics. They may also undertake professional qualifications such as CFA, FRM, or actuarial certifications to further enhance career prospects.
GCSE: A minimum of five GCSE passes, including English at grade C/4, is required. This ensures essential academic and communication skills for university-level study.IELTS (Academic): A score of 6.0 overall with 5.5 in each component is required. This level confirms balanced English proficiency suitable for academic work.TOEFL (iBT): Applicants must achieve 79 overall, with minimum section scores of Reading 18, Listening 17, Speaking 20, and Writing 17. These ensure competency across all communication areas.UCAS: All qualifications correspond to UCAS standards for AAA-equivalent entry, ensuring consistent evaluation across global education systems.India (CBSE/ISC, West Bengal, Other Boards): Indian applicants must meet AAA-equivalent thresholds: 85% (CBSE/ISC), 80% (West Bengal Board), and 90% (other state boards).A-Level: A-Level students must achieve AAA, including Mathematics, indicating exceptional quantitative ability. This ensures applicants are fully prepared for advanced mathematical study.International Baccalaureate (IB): IB candidates require 36 points overall with 6,6,6 at Higher Level, including HL Mathematics (AA or AI). This confirms high academic performance and strong mathematical grounding.American Curriculum (GPA/AP): U.S. applicants must present a GPA of 3.3 plus three AP scores of 5,5,5 or four AP scores of 5,5,4,4. These requirements ensure depth and excellence in advanced academic subjects.ActuaryData AnalystStatisticianQuantitative AnalystOperations Research AnalystFinancial AnalystRisk AnalystEconomistCryptographerMathematicianData ScientistMarket Research AnalystBiostatisticianMachine Learning EngineerAlgorithm DeveloperResearch ScientistInvestment AnalystStatistician ConsultantSoftware Engineer (Mathematical Modeling)Computational Scientist
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