Financial Mathematics

The MSc in Financial Mathematics is designed for students with an undergraduate degree in Mathematics or a related field, who wish to gain a competitive advantage in the financial sector by acquiring the strong mathematical and statistical background demanded by high-level quantitative roles. The proposed programme will equip students with the relevant contemporary knowledge and skills, including new digital innovations such as machine learning with financial applications, computational finance and statistical and data analysis. In the Summer Trimester students will explore their theoretical and applied knowledge in greater depth by completing a dissertation or they will be able to apply their theoretical knowledge to real-world situations via a work placement with a financial firm.



- In the Autumn and Spring Trimesters, you will take a mixture of face-to-face and online modules.



- In the Summer Trimester, you will have the opportunity to apply for a summer work placement with a Dublin-based financial firm, or a dissertation supervised by a member of faculty.



- Upon completion of the programme, you will be able to understand, critique and judge the suitability of a number of advanced financial mathematical models, manipulate, analyse and discern the reliability of financial data sets, and be acquainted with industry practice.



What Will I Learn?

Upon completion of the programme the students will be able to:

- demonstrate a deep knowledge of quantitative methodologies needed for jobs in investment banks and financial institutions.



- apply financial mathematical theory and quantitative methodologies to real-world situations.



- critique and understand the limitations of financial mathematical models, judging the suitability of financial mathematical models and understand industry practice.



- write and run computer programmes that analyse complicated financial systems and data sets.



- analyse the reliability of a financial data set.



- generate new knowledge through research.



- access library and online resources to develop and understand financial mathematical theory and models.



- continue to study in a manner that may be largely autonomous.



- train others in the use of financial mathematical models.



Student Internships

Internship opportunities* are available and the following are industry partners where students were previously placed for a summer work placement: AIB, FINCAD (Zafin Capital Markets Group) and Grant Thornton Advisory, Quantitative Risk.



*Placements are secured through a competitive process and are not guaranteed.

Stage 1 Core Modules

MATH40690 Stochastic Calculus Autumn 10

MATH40720 Fin.Risk Measurement & Mngmt Autumn 10

MATH40730 Counterparty Credit Risk Spring 10

MATH40740 Advanced Financial Models Spring 10



Stage 1 Options - A) 4 of:

Students should choose options in consultation with the programme director. In particular, it is strongly recommended that students take MATH40430 Measure Theory and Integration and ACM30080 PDEs in Financial Maths in the Autumn Trimester, and ACM30070 Computational Finance in the Spring Trimester, if they have not taken equivalent courses before.

ACM30080 PDEs in Financial Maths Autumn 5

ACM40660 Scientific Programming Concepts (ICHEC) Autumn 5

ECON42560 Behavioural Economics Autumn 5

MATH40430 Measure Theory & Integration Autumn 5

STAT40730 Data Programming with R (Online) Autumn 5

STAT40800 Data Prog with Python (online) Autumn 5

COMP47470 Big Data Programming Autumn and Spring (separate) 5

ACM30070 Computational Finance Spring 5

ACM40990 Optimisation in ML Spring 5

COMP40725 Introduction to Relational Databases and SQL Programming Spring 10

ECON42360 Energy Economics and Policy Spring 5

ECON42710 Advanced Econometrics: Time Series Spring 5

STAT40750 Statistical Machine Learning (online) Spring 5

STAT40850 Bayesian Analysis (online) Spring 5



Stage 1 Options - B) 1 of:

Students take exactly 1 module from this list. Availability of Financial Work placements subject to approval of Programme Director and host firm (sufficiently high Autumn Trimester GPA and interview process in Spring Trimester). Co-supervised by the host firm and a member of faculty.

MATH40750 Financial Work Placement Summer 30

MATH40760 Financial Dissertation Summer 30

- The minimum entry requirement will be a 2:1 (or equivalent grade) BSc in Financial Mathematics, Mathematics,

- Applied and Computational Mathematics, or Statistics.

Applicants whose first language is not English must also demonstrate English language proficiency of IELTS 6.5 (no band less than 6.0 in each element), or equivalent.



- Students meeting the programme’s academic entry requirements but not the English language requirements, may enter the programme upon successful completion of UCD’s Pre-Sessional or International Pre-Master’s Pathway programmes. Please see the following link for further information http://www.ucd.ie/alc/programmes/pathways/



You may be eligible for Recognition of Prior Learning (RPL), as UCD recognises formal, informal, and/or experiential learning. RPL may be awarded to gain Admission and/or credit exemptions on a programme. Please visit the UCD Registry RPL web page for further information. Any exceptions are also listed on this webpage. https://tinyurl.com/2ae2ffax

1 year full-time (T341), 2 years part-time (T349).

Next Intake: September 2025.

Career & Graduate Study Opportunities

Graduates with training in Financial Mathematics can cover upper-level quantitative roles in several sub-sectors such as:

- Quantitative analysis in financial firms and hedge funds

- Risk modelling in banking and insurance

- Computational modelling in fintech

- Research and academia