Do you have an interest in risk management and financial regulation? Do you want a career in areas such as corporate risk, compliance, consultancy, or academia? Do you want exemptions from the Professional Risk Manager (PRM) exams?
Studying Financial Risk Management looks at how organisations and investors should understand, evaluate and address relevant risks to maximise the chances of their objectives being achieved.
The programme equips students with the cutting-edge risk management tools and strategies used by leading financial firms and regulatory bodies. Academics who teach on this programme are at the cutting edge of their fields, many also have relevant industry experience.
Queen’s University is ranked third in the UK for Graduate Prospects in Accounting and Finance
(Times and Sunday Times Good University Guide 2024).
Financial Risk Management highlights
Student Experience
Students will use and have access to software such as R, Excel, Matlab, and databases such as Thomson one banker and Bloomberg.
World Class Facilities
Queen’s Business School (QBS) has recently undergone an innovative expansion that establishes a benchmark of global excellence for one of the top business schools in the UK and Ireland. A stunning new 6,000 square metre building, adjacent to the listed red-brick Riddel Hall has been designed with the latest digital infrastructure for media lecture capture, TED Talk provision and collaborative breakout sessions.
Professional Accreditations
This course has an academic partnership with GARP (Global Association of Risk Professionals), helping prepare students for the Financial Risk Manager (FRM) examinations. This course is part of the PRMIA (The Professional Risk Managers' International Association) Risk Accreditation Programme, giving students exemptions from Professional Risk Manager (PRM) exams.
World Class Facilities
Fostering an enhanced social and educational experience the new state-of-the-art QBS venue boasts a 250-seat tiered educational space; 120-seat Harvard style lecture theatre; 150-seat computer laboratory; breakout study spaces; FinTrU Trading Room; a café, and a Business Engagement and Employability Hub.
Industry Links
Students have the opportunity to hear from industry professionals and academics who regularly deliver guest lectures/talks in the Business School.
World Class Facilities
Students may wish to join the Student Managed Fund, which seeks to achieve positive returns through superior stock selection using quantitative and qualitative fundamental analysis. This is a real money student managed investment fund. The goal is to achieve consistent long term positive returns by optimally managing downside risk. The Fund seeks to mitigate risk through sufficient diversification and through a series of strict rules and procedures.
Certain classes are held in the FinTrU Trading Room. Students have access to Bloomberg software, a market leader in financial news, data and analytics, which is used by many financial institutions. The Trading Room allows for an interactive and exciting learning environment which brings textbook theory to life.
The modules taught on the programme provide varied and comprehensive insights into both investment and risk management. The degree also offers professional accreditation and prepares us extremely well for a career in finance.
MSc Financial Risk Management is a year-long full-time programme delivered in-person. Students start with an intensive induction week before undertaking eight modules across the first and second semesters. In the final semester, students either study an Applied Research Project or Dissertation.
You’ll look at how organisations and investors evaluate and address risks to maximise the chances of achieving their objectives.
You’ll use the cutting-edge risk management tools and strategies used by leading financial firms and regulatory bodies. You have an interest in risk management and financial regulation and want a career in areas such as corporate risk, compliance, consultancy, or academia. You’ll gain exemptions from the Professional Risk Manager (PRM) exams.
Semester 1
Asset Pricing
Corporate Finance
Financial Regulation and Risk Management
Financial Data Analytics
Semester 2
Credit Risk Management
Derivatives
Enterprise Risk Management and Risk Analytics
Advanced Financial Data Analytics
Learning opportunities available with this course are outlined below:
Teaching Times
Morning / Afternoon
Full-time option: modules are taught morning/afternoon/evening.
Assessment
Assessments associated with the course are outlined below:
Assessment is by a variety of methods, including individual coursework, group work, oral presentations, simulations, case studies, class tests, examinations and dissertation or work-based research project.
The information below is intended as an example only, featuring module details for the current year of study (2024/25). Modules are reviewed on an annual basis and may be subject to future changes – revised details will be published through Programme Specifications ahead of each academic year.
Enterprise Risk Management & Risk Analytics examines two distinct and increasingly important areas of risk management. The first part of the course focuses on the management of risk at the enterprise level, examining how companies are addressing all their key risks on a consistent integrated basis. The second half of the course covers the most important principles, techniques and tools in Quantitative Risk Analysis with a focus on Monte Carlo simulation. Students will be introduced to risk analysis and modelling using ModelRisk from Vose Software.
Course Aim:
The aims of part one of this module are to:
(i) Enable students to develop an understanding of how corporate risk management has progressed from a traditional approach to a more holistic integrated approach.
(ii) Provide students with an appreciation of the benefits of an integrated approach to risk management and why it should create value.
(iii) Properly define and categorise risks within an Enterprise Risk Management framework and contrast this with a small to medium enterprise risk framework.
(iv) Provide an understanding of Economic Capital calculation and illustrate how quantitative risk management is utilised in corporate decision making.
The aims of part two of this module are to:
(i) Introduce the most important techniques and tools in Quantitative Risk Analysis.
(ii) Familiarise students with risk analysis modelling environments in Microsoft Excel using ModelRisk software from Vose Software.
Learning Outcomes
Upon successful completion of this module, students will be able to:
• Describe and synthesize the theory and rationale behind a corporation’s motivation to implement an Enterprise Risk Management programme.
• Understand how risks may be categorised and thus develop an appropriate business risk framework.
• Outline the main components of the Enterprise Risk Management framework
• Understand how Economic Capital measurement and management are key components in shaping corporate strategy
• Build a Monte Carlo risk model within Microsoft Excel using ModelRisk from Vose Software.
• Present the results of a quantitative risk model in an appropriate format which can be effectively interpreted by the end-user.
Skills
This course provides opportunities for the student to acquire or enhance the following skills:-
Subject-specific skills:
• The ability to construct arguments and exercise problem solving skills in the context of theories of risk management.
• The ability to read and evaluate finance and risk-related academic literature.
• The ability to appreciate, construct and analyse mathematical, statistical, financial and economic models of practical risk situations
• The ability to use Microsoft Excel based quantitative risk analysis software (ModelRisk) to analyse and evaluate uncertainty.
Cognitive skills:
• Problem solving
• Logical reasoning
• Independent enquiry
• Critical evaluation and interpretation
• Self assessment and reflection
Transferrable skills:
• The ability to synthesise information/data from a variety of sources including from databases, books, journal articles and the internet.
• The preparation and communication of ideas in finance, information and risk management in both written and presentation forms.
• The ability to work both independently and in groups.
• Organisation and time management.
• Problem solving and critical analysis.
• Work-based skills; use of IT, including word-processing, email, internet and statistical risk management packages.
• The ability to communicate quantitative and qualitative information together with analysis, argument and commentary in a form appropriate to different intended audiences.
The aim of the dissertation is to provide students with the skills needed for the advanced analysis of relevant datasets, to allow them to demonstrate an understanding of the relevant literature and to derive and test hypotheses and to draw appropriate conclusions.
Learning Outcomes
On completion of the dissertation students will have an understanding of:-
• how to conduct a review of the current and relevant literature of the subject area chosen for the research study;
• how to derive hypotheses or formulate research questions;
• how to use data extracted from datasets or interviews to test hypotheses or answer research questions;
• how to draw conclusions and identify the limitations of the study and scope for further research.
Skills
This course provides opportunities for the student to acquire or enhance the following skills:-
• Communication
• Effective and independent learning
• Specific research skills relevant to the chosen research topic
• Data analysis skills relevant to the chosen research topic
• Econometric skills
The aims of this module are to:
Deepen participants' understanding of financial predictions and decision-making by exploring the revolutionary
impact of combining econometrics and machine learning in financial analytics.
Integrate machine learning and classical financial time series econometrics to tackle complex financial problems
characterised by uncertainty and conflicting objectives.
Explore the role of machine learning in processing large datasets and accurately modelling the complexities of
financial markets.
Advocate for adopting a growth mindset for learning advanced financial data analytics, emphasising embracing
challenges, persisting through setbacks, leveraging criticism, and finding lessons in others' success.
Equip participants with the necessary insights and tools to navigate the sophisticated realm of financial analytics,
encouraging a lifelong commitment to learning and development in the field.
Learning Outcomes
Upon successful completion of this module students will be able to:
1. Extract meaning from noisy financial data
2. Critique stylised facts of financial data for economic inference
3. Evaluate the output of statistical tests
Skills
This module provides opportunities for the student to acquire or enhance the following skills:-
1. Problem solving – innovative ability to implement statistical tests
2. Logical reasoning – analysing data
3. Digital Proficiency – ability to write code
4. Abstraction – developing generic re-usable solutions
5. Critical Thinking – applying and interpreting statistics
The aims of this module are to:
(i) provide students with the necessary theoretical and analytical tools which underpin the pricing of assets;
(ii) familiarize students with the environment of a trading room
Areas to be covered include:
Financial markets
Overview of main markets; how firms and governments raise finance; financial instruments; trading securities.
Valuation
Valuing stocks.
Asset returns and portfolio theory
Measuring asset returns; theory of choice under uncertainty; mean-variance portfolio theory.
Asset-pricing models
Assessing the theoretical and empirical validity of various asset pricing models.
Upon successful completion of this module, students will:
1. Be familiar with the various theories on individuals’ investment decision making
2. apply techniques for formally assessing risk.
3. understand the methodologies employed in investigating asset pricing behaviour in the capital market
4. be able to critically evaluate the various asset pricing models in terms of both theory and empirical evidence
5. be able to critically appraise the EMH, anomalies and behavioural finance.
6. be familiar with the trading-room environment and the Bloomberg database.
Skills
This module provides opportunities for the student to acquire or enhance the following skills:-
• Subject-specific skills
o Use of computer-based packages to analyse and evaluate relevant data
o Ability to criticially read and evaluate finance and risk-related academic literature
o Appreciation, construction and analysis of financial and economic models of practical risk situations
• Cognitive Skills
o Problem solving
o Logical reasoning
o Independent enquiry
o Criticial evaluation and interpretation
o Self-assessment and reflection
• Transferable Skills
o The ability to synthesis information/data from a variety of sources
o Preparation and communication of ideas in both written and presentational forms
o Ability to work both independently and in groups
o Organisation and Time Management
o Use of IT.
i. Profile the development of credit culture and its key role in the global economy. Identify the key credit players, their roles and the interaction between key players.
ii. Introduce students to the techniques used to quantify credit risk. The incremental nature of model development is highlighted and the strengths and weaknesses of various techniques are discussed.
iii. Provide students with an understanding of basic credit derivatives and securitised credit products set against a backdrop of how the complexity of these instruments contributed to the recent financial crisis.
Areas to be covered include:
Background:
History & Evolution of Credit Culture
The Key Players:
Banks, Savings Institutions, Insurance Companies, Finance Companies, Special Purpose Entities, Rating Agencies
Classic Credit Risk Modelling
Financial Ratio Analysis, Strategic Industry Analysis
Accounting Based Credit Risk Models
The Altman Z Score, The Zeta Score, RiskCalc by Moody’s, S&P Credit Model, Neural Networks, Mortality Models
Market Based Credit Risk Models
Options theory, The Merton Model, The KMV Model, The ‘Distance to Default’ Derivation, Reduced Form Models
Analysis of Changing Default Rates
Default Recovery Rates, Loss Given Default, Credit Risk Migration Matrices
Portfolio Approaches to Credit Risk
Portfolio Theory, Correlated Defaults, Credit VaR, Copula Modelling
Credit Derivatives & Structured Credit Products
The Credit Default Swap Market, Credit Linked Notes, Asset Backed Securitisation, Credit Derivatives and the Financial Crisis
Learning Outcomes
Upon successful completion of this module, students will:
1. understand how corporate credit culture and the lending decisions have evolved over the past 50 years
2. be able to identify and critically access the roles played by key institutional and market players in credit markets
3. be able to demonstrate knowledge of the structure and application of accounting and market based credit risk models
4. be familiar with portfolio based techniques for credit risk modeling
5. understand the features of credit derivatives and securitized credit products with an appreciation of how these instruments contributed to the recent financial crisis
Skills
This course provides opportunities for the student to acquire or enhance the following skills:
Subject-specific skills:
o The ability to critically read and evaluate finance and risk specific academic literature
o The ability to apply financial data to a series of models in order to make a appropriate estimate of credit risk exposure
o The understanding of the relative strengths and weaknesses of credit risk models
o The ability to analyse various credit derivatives from a risk mitigation perspective
o The ability to identify and debate issues pertaining to credit risk set within a broader context of the current financial climate
Cognitive skills:
o Problem solving
o Logical reasoning
o Independent enquiry
o Critical evaluation and interpretation
o Self assessment and reflection
Transferable Skills:
o The ability to synthesise financial information/data from a variety of sources
o Preparation and communication of ideas in both written and presentational forms
o Ability to work both independently and in groups
o Organisation and Time Management
o Use of IT
This module considers both risk and regulation in financial services. With regard to risk the module introduces students to the risks that institutions must take if they are to survive and prosper including market risk, credit risk, liquidity risk and operational risk. Emphasis is on the quantification of these risks, decisions about what level of such risks are acceptable and the action required to mitigate unacceptable levels of risk.
MODULE AIMS
(i) to offer a rigorous and intellectually demanding course of study of the techniques, principles and underpinning theories behind effective financial regulation
(ii) to offer a rigorous and intellectually demanding course of study of risk assessment and risk mitigation techniques applicable to financial institutions
(iii) to provide students with an understanding of current thoughts on financial market reform.
With regard to regulation the emphasis is on identifying the objectives of the regulatory system. The course stresses that being clear about the objectives of regulation is essential from the point of view of ensuring that the system is operated efficiently, that priorities are correctly assigned and weighted, and that the spirit as well as the letter of regulatory requirements is observed.
Learning Outcomes
LEARNING OUTCOMES
Upon successful completion of this module students will have an understanding of:
1. the economics of information and its importance in financial regulation
2. the calculation of market risk, credit risk, liquidity risk and operational risk for financial institutions
3. the relationship between the capital requirements faced by financial institutions and their levels of credit risk, market risk and operational risk.
4. obstacles to efficient supervision and resolution
5. working in small groups and making tutorial presentations
6. how to use journal articles to build knowledge of the subject material
Skills
Skills
This module provides opportunities for the student to acquire or enhance the following skills:-
• Subject-specific skills
o Ability to critically read and evaluate finance and risk-related academic literature;
o Appreciation, construction and analysis of maths/statistical, financial and economic models of practical risk situations;
o Ability to connect business problems with risk management;
o Ability to marry regulatory structure with the principles of risk sharing and risk mitigation
• Cognitive Skills
o Problem solving
o Logical reasoning
o Independent enquiry
o Criticial evaluation and interpretation
o Self assessment and reflection
• Transferable Skills
o The ability to synthesis information/data from a variety of sources
o Preparation and communication of ideas in both written and presentational forms
o Ability to work both independently and in groups
o Organisation and Time Management
o Use of IT.
The purpose of this course is to provide an introduction to econometric techniques used in finance. It contains a treatment of classical regression and an introduction to time series techniques. There will be an emphasis on applied work using econometric packages.
The course is designed to give students both theoretical and practical experience of statistical and econometric techniques. A wide range of topics is typically covered including the basic regression model, which includes a discussion of the classical violations of this model and methods for their correction. Students will learn a computer statistical software package (R).
Learning Outcomes
Upon successful completion of this course students will have an understanding of:-
• the main issues relating to the appropriate econometric modelling of financial and economic time series;
• and have gained experience in the use of econometric software and be able to demonstrate their software skills in completing assignments;
• and be able to discuss, applied econometric research topics in finance;
• and have improved their data management, programming and research skills.
Skills
Subject-specific Skills
• The ability to construct arguments and exercise problem solving skills in finance
• The ability to use computer-based mathematical/statistical/econometric packages to analyse and evaluate relevant data
• The ability to read and evaluate finance and risk-related academic literature
Cognitive Skills
• Problem solving
• Logical reasoning
• Independent enquiry
• Critical evaluation and interpretation
• Self-assessment and reflection
Transferable Skills
• The ability to synthesise information/data from a variety of sources
• The preparation and communication of ideas in finance, information economics and risk management
• Organisation and time management
• Problem solving and critical analysis
• Work-based skills; use of IT, including word-processing, email, internet and statistical/econometric/risk management packages
• The ability to communicate quantitative and qualitative information together with analysis, argument and commentary
The aim of this course is to develop in students a theoretical and practical knowledge of derivative instruments.
This module provides participants with an exhaustive coverage of widely used derivative products stressing pricing and uses for financial engineering and risk management. The module provides an overview of derivative instruments, markets, participants and uses. It focuses on the pricing and uses of futures, forwards and options. The cost of carry relationship, the binomial approach, the Black-Scholes model and its variants are detailed to equip participants with the basic tools for pricing derivatives. The module examines practical uses of derivative securities as risk management tools for corporations and financial institutions.
Areas to be covered include:
THE MOVEMENT OF FUTURES PRICES: some basic facts. CTAs, managed futures, hedge funds. Financialization of Commodity Markets. Time series momentum.
MEAN VARIANCE APPROACHES TO HEDGE RATIO DETERMINATION, STOCK INDEX FUTURES AND HEDGING EFFECTIVENESS: The mean-variance approach to hedge ratio construction. Hedging with stock index futures. Hedging effectiveness and hedge ratio estimation - OLS, ECM and GARCH procedures. Duration and Expiration effects.
THE STOCHASTIC PROCESS OF ASSET PRICES AND THE DERIVATION OF THE BLACK-SCHOLES MODEL:The Wiener process and rare events in financial markets; Ito processes; Ito's lemma; generalised Ito's lemma; Black-Scholes differential equation; Black-Scholes pricing formula; options on stocks paying known dividends; pseudo-American model; option on stock indices, currency options and options on futures;
VOLATILITY: Estimating volatility: historical; implied - application of Newton-Raphson. Empirical characteristics of volatility: smiles; term structure skew; mean reversion; Forecasting volatility: application of GARCH; empirical evidence of volatility forecasts - implied versus historical; Bisection.
EXOTIC OPTIONS: Types of exotic options - barrier options; lookback options; strike options; binary or digital options; compound options; and chooser options.
INTEREST RATE DERIVATIVES: The standard market models; models of short rate; HJM and LMM models.
RISK AND REGULATION WITH EMPHASIS ON VALUE AT RISK: Regulation of Financial Institutions; value at risk and forecast accuracy; capital adequacy and value at risk; value at risk and the variance covariance approach; value at risk and non-parametric methods such as historical simulation and bootstrapping; value at risk and linear and non-linear positions.
REAL OPTIONS: The option to expand, contract, default, abandon and switch. The valuation of real options in the face of compoundness, interaction between options and ownership. Real options and the valuation of internet companies.
Learning Outcomes
Upon successful completion of this module, students will have an understanding of:-
1. understand the mechanisms of futures and forward market
2. price futures and forward instruments
3. understand the mechanisms of options markets
4. understand concepts of stochastic processes and its application in financial modelling
5. understand and derive binomial tree model
6. understand and derive Black-Scholes-Merton model
7. estimate historical and implied volatility
8. construct hedges using futures and options
Skills
This module provides opportunities for the student to acquire or enhance the following skills:
Subject-specific Skills
• The ability to construct arguments and exercise problem solving skills in the context of theories of finance and risk management
• The ability to use computer-based mathematical / statistical / econometric packages to analyse and evaluate relevant data
• The ability to read and evaluate finance and risk-related academic literature
• The ability to appreciate, construct and analyse mathematical, statistical, financial and economic models of practical risk situations
• The ability to connect business problems with risk management
• The ability to marry regulatory structure with the principles of risk sharing and risk mitigation
Cognitive Skills
• Problem solving
• Logical reasoning
• Independent enquiry
• Critical evaluation and interpretation
• Self assessment and reflection
Transferable Skills
• The ability to synthesise information/data from a variety of sources including from databases, books, journal articles and the internet
• The preparation and communication of ideas in finance, information economics and risk management in both written and presentational forms
• The ability to work both independently and in groups
• Organisation and time management
• Problem solving and critical analysis
• Work-based skills; use of IT, including word-processing, email, internet and statistical/econometric/risk management packages
• The ability to communicate quantitative and qualitative information together with analysis, argument and commentary in a form appropriate to different intended audiences
Course Description:
The purpose of this course is to analyse how corporations make major financial decisions. The theory of corporate behaviour is discussed and the relevance of each theoretical model is examined by an empirical analysis of actual corporate decision making.
Course Aim:
The aims of this module are to:
(i) familiarize students with the issues confronting corporations when making investment and financing decisions;
(ii) develop the ability of students to obtain corporate information from the Bloomberg database.
Course Coverage:
• Corporate Governance
• Investment Appraisal
• Dividend Policy
• Capital Structure
• Initial Public Offerings
• Mergers and Acquisitions
Learning Outcomes
Upon successful completion of this module, students will be able to:
• describe and synthesize academic theories which explain the approaches of corporations to investment and financing decisions;
• analyse how corporations can increase shareholder value;
• evaluate empirical evidence regarding whether corporate decision making is consistent with academic theories;
• apply theoretical principles to hypothetical situations;
• use the Bloomberg database in a trading-room environment.
Skills
This course provides opportunities for the student to acquire or enhance the following skills:
Subject-specific Skills
• The ability to construct arguments and exercise problem solving skills in the context of theories of finance and risk management
• The ability to use computer-based mathematical / statistical / econometric packages to analyse and evaluate relevant data
• The ability to read and evaluate finance and risk-related academic literature
• The ability to appreciate, construct and analyse mathematical, statistical, financial and economic models of practical risk situations
Cognitive Skills
• Problem solving
• Logical reasoning
• Independent enquiry
• Critical evaluation and interpretation
• Self assessment and reflection
Transferable Skills
• The ability to synthesise information/data from a variety of sources including from databases, books, journal articles and the internet
• The preparation and communication of ideas in finance, information economics and risk management in both written and presentational forms
• The ability to work both independently and in groups
• Organisation and time management
• Problem solving and critical analysis
• Work-based skills; use of IT, including word-processing, email, internet and statistical/econometric/risk management packages
• The ability to communicate quantitative and qualitative information together with analysis, argument and commentary in a form appropriate to different intended audiences.
The applied research project provides students with the opportunity to utilise the knowledge and skills acquired over the previous two semesters to plan, develop and produce a substantial piece of original, independent applied research.
Lectures and computer-based workshops will cover the following areas:
1. Research Methodology
2. Fundamental analysis and strategy analysis
3. Data Management, Analysis, Visualisation and Inference
4. Financial analysis [ratios/cash flows], forecasting profit & EPS.
5. Valuation 1: DDM and DCF approach
6. Valuation 2: EVA and Price- multiples
7. Critical assessment of model adequacy
8. Presenting Information and Data
Learning Outcomes
Upon successful completion of this project, students will:
1. Demonstrate an ability to design and manage a piece of individual research.
2. Apply knowledge and skills developed in previous modules to contemporary issues in financial markets.
3. Establish links between financial theory and financial practice.
4. Exhibit intellectual discipline in identifying and critique the appropriate information.
5. Identify appropriate econometric methods for critically analysing a contemporary issue in finance.
6. Critically evaluate the appropriateness of modelling assumptions.
7. Present their thinking in a professional industry-style research paper.
Skills
This applied research project provides opportunities for the student to acquire or enhance the following skills:-
· Subject-specific skills
-Use of computer-based packages to analyse and evaluate relevant data
-Ability to critically read and evaluate finance and risk-related academic literature
-Appreciation, construction and analysis of financial and economic models of practical risk situations
· Cognitive Skills
-Problem solving
-Logical reasoning
-Independent enquiry
-Critical evaluation and interpretation
-Self-assessment and reflection
-Intellectual humility
-Intellectual discipline
· Transferable Skills
-The ability to synthesis information/data from a variety of sources
-Preparation and communication of ideas in both written and presentational forms
-Ability to work both independently
-Organisation and Time Management
-Use of IT
Normally a strong 2.2 Honours degree (with minimum of 55%) or equivalent qualification acceptable to the University in Mathematics, Accountancy, Finance, Economics or other relevant quantitative subject. Science and Engineering disciplines will be considered where there is a significant mathematical component. Performance in relevant modules must be at UK 2:2 Honours standard (minimum 55% or acceptable equivalent).
We welcome applications from a diverse range of applicants so will also consider previous work experience alongside academic qualifications. Prior experiential learning in quantitative analysis, Mathematics or Finance based subjects in lieu of academic qualifications will be considered on an individual basis.
The University's Recognition of Prior Learning Policy provides guidance on the assessment of experiential learning (RPEL). Please visit http://go.qub.ac.uk/RPLpolicy for more information.
Applicants are advised to apply as early as possible and ideally no later than 16th August 2024 for courses which commence in late September. In the event that any programme receives a high number of applications, the University reserves the right to close the application portal. Notifications to this effect will appear on the Direct Application Portal against the programme application page.
Please note: international applicants will be required to pay a deposit to secure a place on this course.
International Students
Our country/region pages include information on entry requirements, tuition fees, scholarships, student profiles, upcoming events and contacts for your country/region. Use the dropdown list below for specific information for your country/region.
English Language Requirements
Evidence of an IELTS* score of 6.5, with not less than 5.5 in any component, or an equivalent qualification acceptable to the University is required.
*Taken within the last 2 years.
International students wishing to apply to Queen's University Belfast (and for whom English is not their first language), must be able to demonstrate their proficiency in English in order to benefit fully from their course of study or research. Non-EEA nationals must also satisfy UK Visas and Immigration (UKVI) immigration requirements for English language for visa purposes.
If you need to improve your English language skills before you enter this degree programme, INTO Queen's University Belfast offers a range of English language courses. These intensive and flexible courses are designed to improve your English ability for admission to this degree.
Academic English: an intensive English language and study skills course for successful university study at degree level
Pre-sessional English: a short intensive academic English course for students starting a degree programme at Queen's University Belfast and who need to improve their English.
As many financial firms are substantially expanding their risk management functions, this programme is likely to open a wide range of new and exciting career opportunities including utilising cutting-edge quantitative modelling techniques and working in collaboration with traders to develop bespoke financial products, as well as portfolio and product risk management and monitoring.
Graduate prospects from the MSc Financial Risk Management are excellent; culminating in Queen’s being ranked third in the UK for Graduate Prospects in Accounting and Finance (Times and Sunday Times Good University Guide 2024). Graduates from this programme have secured roles with employers such as Royal Bank of Scotland, BNY Mellon, Citi, PwC, EY, Intel, and many others. Typical roles include financial and data analysts, risk managers, management consultants, and project managers. https://www.qub.ac.uk/directorates/sgc/careers/
Graduate Plus/Future Ready Award for extra-curricular skills
In addition to your degree programme, at Queen's you can have the opportunity to gain wider life, academic and employability skills. For example, placements, voluntary work, clubs, societies, sports and lots more. So not only do you graduate with a degree recognised from a world leading university, you'll have practical national and international experience plus a wider exposure to life overall. We call this Graduate Plus/Future Ready Award. It's what makes studying at Queen's University Belfast special.
1EU citizens in the EU Settlement Scheme, with settled status, will be charged the NI or GB tuition fee based on where they are ordinarily resident. Students who are ROI nationals resident in GB will be charged the GB fee.
2 EU students who are ROI nationals resident in ROI are eligible for NI tuition fees.
3 EU Other students (excludes Republic of Ireland nationals living in GB, NI or ROI) are charged tuition fees in line with international fees.
All tuition fees quoted relate to a single year of study unless stated otherwise. Tuition fees will be subject to an annual inflationary increase, unless explicitly stated otherwise.
Terms and Conditions for Postgraduate applications:
1.1 Due to high demand, there is a deadline for applications.
1.2 You will be required to pay a deposit to secure your place on the course.
1.3 This condition of offer is in addition to any academic or English language requirements.
Depending on the programme of study, there may be extra costs which are not covered by tuition fees, which students will need to consider when planning their studies.
Students can borrow books and access online learning resources from any Queen's library. If students wish to purchase recommended texts, rather than borrow them from the University Library, prices per text can range from £30 to £100. Students should also budget between £30 to £75 per year for photocopying, memory sticks and printing charges.
Students undertaking a period of work placement or study abroad, as either a compulsory or optional part of their programme, should be aware that they will have to fund additional travel and living costs.
If a programme includes a major project or dissertation, there may be costs associated with transport, accommodation and/or materials. The amount will depend on the project chosen. There may also be additional costs for printing and binding.
Students may wish to consider purchasing an electronic device; costs will vary depending on the specification of the model chosen.
There are also additional charges for graduation ceremonies, examination resits and library fines.
How do I fund my study?
The Department for the Economy will provide a tuition fee loan of up to £6,500 per NI / EU student for postgraduate study. Tuition fee loan information.
A postgraduate loans system in the UK offers government-backed student loans of up to £11,836 for taught and research Masters courses in all subject areas (excluding Initial Teacher Education/PGCE, where undergraduate student finance is available). Criteria, eligibility, repayment and application information are available on the UK government website.
