Master of Science in Mathematical Finance and Risk Analysis
<p>The Master of Science in Mathematical Finance and Risk Analysis is a postgraduate programme that prepares professionals for advanced practice in quantitative finance, risk management, and financial analytics. The programme combines finance theory with applied mathematics, statistics, computing, and computational methods.
The short answer
- What it costs
- Up to Ksh 295,800 a year at a public institution, Ksh 591,600 for the whole course. Up to Ksh 325,000 a year at a private one.The detail
- Who can get in
- A KCSE mean grade of N/A (Postgraduate) or above. There is another way in without it.The detail
- How long, and when
- Takes 2 years, full-time, part-time, with 0 months of attachment. Next intake January 2027, at 1 institution.The detail
- Where it leads
- 8 careers in the catalogue. The least exposed to AI is Early Childhood Strategist, at 10 out of 100.The detail
The programme
What you study, how long it takes, and how it is delivered.
Overview
Core areas include financial markets, discrete time finance, stochastic calculus, asset pricing, portfolio optimisation, statistical models, computational methods, machine learning in finance, risk management, and dissertation. Students engage with both theoretical and practical work through coursework, computational projects, and research.
The programme is offered by Strathmore University through the Institute of Mathematical Sciences. Strathmore is a private university offering the programme over four academic semesters plus a dissertation project.
Students develop competencies in quantitative finance, risk analytics, computational modelling, asset pricing, and machine learning applications in finance. The programme includes 16 course units, examinations, computational projects, and a supervised dissertation, preparing graduates for analytical roles in the finance industry and academic research.
Strathmore charges approximately KES 834,600 total for the programme, paid over six semesters (~KES 417,300/year). Entry requires a Bachelor's degree with at least Second Class Honours in mathematics, statistics, finance, economics, or related fields from a recognised institution.
Graduates pursue careers as quantitative analysts, risk managers, investment analysts, financial modellers, data scientists, and lecturers in mathematical finance across investment banks, insurance companies, regulatory bodies, government agencies, and universities.
Skills Required
- Quantitative Finance and Modelling
- Risk Analytics and Management
- Stochastic Calculus and Continuous Time Finance
- Asset Pricing and Portfolio Optimisation
- Statistical Methods in Financial Markets
- Computational Methods in Finance
- Machine Learning Applications in Finance
- Financial Markets and Instruments
- Programming in MATLAB, Python, R, and C++
- Academic Writing and Dissertation Research
Key Subjects
- Financial Markets and Instruments
- Discrete Time Finance Methods
- Stochastic Calculus and Continuous Time Finance
- Asset Pricing and Portfolio Optimisation
- Statistical Models in Financial Markets
- Computational Methods in Finance
- Machine Learning with Applications in Finance
- Risk Management and Analytics
- Seminar Series in Finance
- Dissertation Research
Certifications
- GARP FRM Certification
- CFA Charter
Specializations
Quantitative Finance
Focuses on quantitative finance, covering modelling, pricing, derivatives, analytics, and managing quantitative finance.
Risk Analytics
Examines risk analytics, covering assessment, modelling, management, compliance, and managing risk analytics.
Computational Finance
Covers computational finance, covering methods, programming, modelling, simulation, and managing computational finance.
Asset Pricing
Focuses on asset pricing, covering models, valuation, markets, portfolios, and managing asset pricing.
Machine Learning in Finance
Examines machine learning in finance, covering algorithms, models, applications, analytics, and managing ML in finance.
Investment Banking Analytics
Covers investment banking analytics, covering analysis, modelling, valuation, risk, and managing investment analytics.
What you study
10 subjects- Financial Markets and Instruments
- Discrete Time Finance Methods
- Stochastic Calculus and Continuous Time Finance
- Asset Pricing and Portfolio Optimisation
- Statistical Models in Financial Markets
- Computational Methods in Finance
- Machine Learning with Applications in Finance
- Risk Management and Analytics
- Seminar Series in Finance
- Dissertation Research
Modules
12 in the programmeFinancial Markets and Instruments
Year 1Semester 13 creditsCore
Examines instruments, markets, trading, valuation, and understanding financial markets and instruments.
Discrete Time Finance Methods
Year 1Semester 13 creditsCore
Covers models, pricing, derivatives, arbitrage, and managing discrete time finance.
Statistical Models and Methods in Financial Markets
Year 1Semester 13 creditsCore
Examines statistics, models, markets, analysis, and managing statistical models in financial markets.
Computational Methods in Finance
Year 1Semester 13 creditsCore
Covers programming, modelling, simulation, algorithms, and managing computational finance using MATLAB, Python, R, and C++.
Stochastic Calculus and Continuous Time Finance
Year 1Semester 23 creditsCore
Examines calculus, processes, models, derivatives, and managing continuous time finance.
Asset Pricing and Portfolio Optimisation
Year 1Semester 23 creditsCore
Covers models, valuation, portfolios, optimisation, and managing asset pricing and portfolios.
Machine Learning with Applications in Finance
Year 1Semester 23 creditsCore
Examines algorithms, models, applications, analytics, and managing machine learning in finance.
Risk Management and Analytics
Year 1Semester 23 creditsCore
Covers assessment, modelling, management, compliance, and managing risk analytics.
Seminar Series in Finance
Year 2Semester 13 creditsCore
Examines current issues, research, practice, trends, and engaging with finance industry experts.
Financial Derivatives and Pricing
Year 2Semester 13 creditsCore
Covers derivatives, options, futures, swaps, pricing, and managing financial derivatives.
Quantitative Risk Management
Year 2Semester 13 creditsCore
Examines quantitative methods, risk models, VaR, stress testing, and managing quantitative risk.
Dissertation Research Project
Year 2Semester 29 creditsCore
Supervised dissertation on a mathematical finance and risk analysis topic, demonstrating mastery of quantitative methods and finance knowledge, assessed through written submission and oral defence.
Specialisations
Quantitative Finance
Focuses on quantitative finance, covering modelling, pricing, derivatives, analytics, and managing quantitative finance.
Risk Analytics
Examines risk analytics, covering assessment, modelling, management, compliance, and managing risk analytics.
Computational Finance
Covers computational finance, covering methods, programming, modelling, simulation, and managing computational finance.
Asset Pricing
Focuses on asset pricing, covering models, valuation, markets, portfolios, and managing asset pricing.
Machine Learning in Finance
Examines machine learning in finance, covering algorithms, models, applications, analytics, and managing ML in finance.
Investment Banking Analytics
Covers investment banking analytics, covering analysis, modelling, valuation, risk, and managing investment analytics.
A day as a student
A typical day during the MSc in Mathematical Finance and Risk Analysis programme combines lectures, computational labs, project work, seminars, and independent study. Sessions cover financial markets, stochastic calculus, asset pricing, computational methods, and machine learning. Financial markets sessions examine instruments, markets, trading, valuation, and understanding financial markets. Discrete time finance sessions cover models, pricing, derivatives, arbitrage, and managing discrete time finance. Stochastic calculus sessions cover calculus, processes, models, derivatives, and managing continuous time finance. Asset pricing sessions cover models, valuation, portfolios, optimisation, and managing asset pricing. Statistical models sessions cover statistics, models, markets, analysis, and managing statistical models. Computational methods sessions cover programming, modelling, simulation, algorithms, and managing computational finance. Machine learning sessions cover algorithms, models, applications, analytics, and managing ML in finance. Risk management sessions cover assessment, modelling, management, compliance, and managing risk. Seminar series provide opportunities for engaging with industry experts and debating current issues in finance. Computational labs provide hands-on experience with MATLAB, Python, R, and C++ for financial modelling. Project work provides hands-on experience with quantitative finance, risk analytics, and computational modelling. Guest lectures from experienced quantitative analysts, risk managers, and investment bankers provide practical insights. The programme culminates in a supervised dissertation on a mathematical finance and risk analysis topic.
The trade-offs
| In its favour | Against it |
|---|---|
| Unique programme combining mathematics, finance, and computing, filling a niche in Kenya's financial sector. | Strathmore fees are high at approximately KES 417,300/year. |
| Programme uses industry-standard tools: MATLAB, Python, R, and C++. | Only one university (Strathmore, private) confirmed offering this specific programme. |
| Graduates are eligible for GARP FRM certification and CFA charter. | No public university confirmed offering this specific programme. |
| High demand for quantitative finance professionals in Kenya's growing financial sector. | Programme requires strong mathematics background, limiting access for non-quantitative graduates. |
What it costs
Tuition at both ends of the market, and how to pay for it.
What it costs, and where
Against 243 science coursesAnnual tuition in Kenyan shillings, rounded. The upright tick is the median for this field, so a bar sitting entirely to its right is an expensive programme by the standards of its own subject.
The fine print
KU sci MSc ~296K/yr (joweri.com). UoN unverified. Strathmore 325K/yr (sims.strathmore.edu).
HELB postgraduate loans are available for Kenyan students. Strathmore University may offer scholarships for eligible students. GARP may offer bursaries for risk management education. Some financial institutions, banks, and regulatory bodies may sponsor staff for postgraduate study.
Funding options
HELB Postgraduate Loan
Strathmore Scholarship
GARP Bursary
Scholarships
3 recordedHELB Postgraduate Loan
LoanKsh 200,000Kenyan
Kenyan students pursuing postgraduate studies at recognised universities.
Strathmore Scholarship
ScholarshipKsh 417,300Kenyan
Strathmore University offers scholarships for eligible postgraduate students.
GARP Bursary
ScholarshipKsh 150,000
GARP may offer bursaries for risk management education and professional development.
Scholarships you could apply for
2 usable where it is taught- Chevening Scholarships Kenya
Closes 30 November 2026FullUK Government
Full masters in UK
- AfDB Japan Scholarship Program
Closes 28 February 2027FullAfrican Development Bank
Full graduate study abroad
Getting in
The grades, the alternatives, and who accredits the award.
What you need
Strathmore University requires a Bachelor's degree with at least Second Class Honours in a relevant quantitative field. An interview may be required. Contact Strathmore for specific admission requirements.
When it next starts
Taught at 1 institutionIncluding Strathmore University.
Including Strathmore University.
Including Strathmore University.
How you are assessed
4 componentsCoursework and Continuous Assessment
Coursework30% of the mark
Continuous assessment through coursework assignments, computational projects, seminar presentations, and class participation.
Written Examinations
Examination70% of the mark
Written examinations covering financial markets, stochastic calculus, asset pricing, and computational methods.
Computational Project Assessment
Project40% of the mark
Practical assessment through computational projects, financial modelling, programming, and demonstrating quantitative finance skills.
Dissertation
Research100% of the mark
Supervised dissertation on a mathematical finance and risk analysis topic, demonstrating mastery of quantitative methods and finance knowledge, assessed through written submission and oral defence.
Accreditation
Commission for University Education (CUE)
Academic accreditationRequired
Programme accredited by CUE. Strathmore University (private) offers MSc in Mathematical Finance and Risk Analytics through the Institute of Mathematical Sciences. The programme meets CUE standards for postgraduate training in mathematical finance and risk analytics. Graduates are eligible for GARP FRM certification and CFA charter.
The programme is accredited by the Commission for University Education (CUE). Strathmore University (private) offers MSc in Mathematical Finance and Risk Analytics through the Institute of Mathematical Sciences. The programme meets CUE standards for postgraduate training in mathematical finance and risk analytics. Graduates are eligible for GARP FRM certification and CFA charter.
Where it leads
The roles it opens, and what you leave with.
Where graduates go
6 rolesQuantitative Analyst
High demandKsh 150,000 to Ksh 600,000
Develops quantitative models, overseeing modelling, pricing, analytics, and managing quantitative finance.
Risk Manager
High demandKsh 120,000 to Ksh 500,000
Manages risk operations, overseeing assessment, modelling, management, and managing risk.
Investment Analyst
High demandKsh 100,000 to Ksh 450,000
Conducts investment analysis, overseeing analysis, valuation, modelling, and managing investments.
Financial Modeller
Moderate demandKsh 120,000 to Ksh 500,000
Develops financial models, overseeing modelling, simulation, valuation, and managing financial models.
Data Scientist (Finance)
High demandKsh 150,000 to Ksh 600,000
Applies data science in finance, overseeing analytics, ML, modelling, and managing financial data.
Mathematical Finance Lecturer
Moderate demandKsh 100,000 to Ksh 450,000
Teaches mathematical finance at university or college level, overseeing instruction, research, and academic supervision.
Graduate outcomes
Graduates pursue careers as quantitative analysts, risk managers, investment analysts, financial modellers, data scientists, and lecturers in mathematical finance across investment banks, insurance companies, regulatory bodies, government agencies, and universities.
Where these fields lead
8 careers- Early Childhood StrategistEducation10Low exposure
- Special Needs EducatorEducation10Low exposure
- Career Guidance CounselorEducation11Low exposure
- Cloud Security EngineerTechnology14Low exposure
- Early Literacy SpecialistEducation14Low exposure
- Industrial Robotics ProgrammerTechnology14Low exposure
- STEM Laboratory EducatorEducation14Low exposure
- TVET Competency AssessorEducation14Low exposure
Tools you will learn
Python
SoftwarePrimary
Python for financial modelling and analytics, covering data, modelling, analysis, and managing financial data.
MATLAB
Software
MATLAB for computational finance, covering modelling, simulation, analysis, and managing computational finance.
R
Software
R for statistical analysis and modelling, covering data, statistics, analysis, and managing financial data.
C++
Software
C++ for high-performance financial computing, covering algorithms, modelling, simulation, and managing computational finance.
Industry links
Common misconceptions
Mathematical finance is just about accounting.
Mathematical finance covers comprehensive quantitative modelling, stochastic calculus, risk analytics, and computational methods beyond just accounting.
Risk analysis is just about insurance.
Risk analysis covers comprehensive assessment, modelling, management, compliance, and analytics beyond just insurance.
This programme is only for mathematicians.
Mathematical finance skills are valuable for economists, statisticians, computer scientists, and finance professionals beyond just mathematicians.
Computational finance is just about spreadsheets.
Computational finance covers comprehensive programming, modelling, simulation, and algorithms in MATLAB, Python, R, and C++ beyond just spreadsheets.
Machine learning in finance is just about chatbots.
Machine learning in finance covers comprehensive algorithms, models, analytics, prediction, and risk management beyond just chatbots.
Quantitative finance careers are limited in Kenya.
Kenya's growing financial sector, fintech ecosystem, and regulatory bodies create high demand for quantitative finance professionals.