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The MSc Computational Finance at King’s delivers training in quantitative finance, scientific computing, and modern computational tools used in financial markets. It suits students with strong mathematical or computing backgrounds aiming for careers in quantitative roles, financial risk, or FinTech.
Curriculum Structure
Term 1:
Students learn core units like Scientific Computing for Finance, Quantitative Methods in Finance, Agent-Based Modelling in Finance, and High-Frequency Finance, which build skills in numerical methods, simulation, time-series, and modelling real-time market behaviour.
Term 2:
They deepen their expertise with optional modules (selected from topics such as big-data analytics, distributed ledgers, systematic risk) alongside required modules, combining modeling, programming, and risk analysis techniques.
Project / Final Component:
A substantial Individual Project lets students apply what they have learned—scientific computing, quantitative finance, and high-frequency modelling—to a research or applied problem in computational finance.
Focus areas: “Quantitative finance, scientific computing, high-frequency modelling, risk & agent-based simulation, FinTech tools”
Learning outcomes: “Apply numerical and computational methods to model financial markets; deepen understanding of systematic risk & modern finance theory; implement algorithms & analytics for financial instruments & data; complete a rigorous individual project”
Professional alignment (accreditation): “Aligned with industry practice in quantitative finance and FinTech; strong links with financial institutions; uses real-world tools & data analytics methods”
Reputation (employability rankings): “King’s ranked 6th in the UK for Computer Science (QS 2025); past graduates working as analysts, risk quant, fintech engineers”
The MSc Computational Finance at King’s College London offers a dynamic blend of theoretical knowledge and practical application, ensuring students are well-prepared for the evolving tech landscape. Through hands-on projects, industry collaborations, and access to state-of-the-art facilities, students gain invaluable experience that bridges the gap between academia and industry.
Key experiential learning opportunities include:
Knowledge Exchange Projects (KEPs): Collaborative initiatives with public, private, and third-sector organizations, allowing students to tackle real-world challenges in areas like AI, cybersecurity, and data science.
Industry Collaborations: Partnerships with tech giants such as Amazon Web Services, where students develop innovative digital solutions to address public sector challenges.
Research-Led Teaching: Modules delivered by leading experts in fields like algorithms, data analysis, and human-centered computing, ensuring students are at the forefront of technological advancements.
State-of-the-Art Facilities: Access to advanced computing labs, high-performance computing resources, and specialized software tools that support both learning and research activities.
Central London Location: Proximity to major tech hubs and industry events, providing students with networking opportunities and exposure to the latest industry trends.
Graduates of the MSc Computational Finance at the University of Wolverhampton develop strong quantitative, programming, and data analytics skills for finance, preparing for careers as Quantitative Analyst, Risk Manager, Financial Data Scientist, and Investment Analyst. The course’s blend of theoretical finance, mathematical modelling, and computational skills supports employability in banking, asset management, and fintech.
Student Futures offers career coaching, CV workshops, placements, and job fairs, supporting professional development and employability for finance graduates.
While exact stats vary by cohort, STEM postgraduate employment outcomes are strong in the UK, with starting salaries typically ranging from £25,000 to £45,000 depending on specialization and experience.
Collaboration with banks, asset managers, and analytics consultancies provides industry exposure, live projects, and placement opportunities.
The course aligns with high-demand professional skills in finance and modelling, supporting long-term career growth and accreditation value for financial roles.
Graduates demonstrate expertise in computational approaches to finance, financial engineering, and risk modelling.
Further Academic Progression:
Graduates may pursue PhD research or advanced certifications in quantitative finance, financial engineering, or related areas, or progress into academic and technical leadership roles.
A minimum high 2:1 undergraduate Bachelor’s (honours) degree with a final mark of at least 65% or above India - Degree from a Prestigious Institution: Bachelor degree (Honours/Special) with a score of: 67% or 8.3 out of 10. India - Degree from a Recognised Institution: Bachelor degree (Honours/Special) with a score of: 70% or 8.5 out of 10. India - Degree from a Highly Prestigious Institution: Bachelor degree (Honours/Special) with a score of: 65% or 8 out of 10United States of America: Bachelor degree with a GPA of 3.5/4.0IELTS (Academic) 7.0 overall with a minimum of 6.5 in each skillTOEFL iBT: 100 overall with a minimum of 25 in writing and 23 in each of the other skillsOne academic reference is requiredApplicants may wish to include a CV (Resume) or evidence of professional registration as part of their application.Quantitative Analyst (Quant)Financial Data ScientistRisk AnalystAlgorithmic Trading DeveloperFinancial Engineer (Quant Developer)Computational Finance Specialist
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Kings College London
