Loughborough University

What you'll study

The programme blends core topics from finance and economics with practical and theoretical aspects of artificial intelligence, machine learning and data analytics. Teaching typically includes lectures, hands-on labs, group work and a substantial individual project or dissertation that applies AI methods to a real financial problem.

• Core quantitative methods — probability, statistics, time series and computational techniques used in financial modelling.
• Machine learning for finance — supervised and unsupervised learning, deep learning, feature engineering and model evaluation with financial datasets.
• Financial data analytics — data acquisition, cleaning, databases, high-frequency data handling and visualisation techniques specific to market and alternative data.
• Algorithmic trading and portfolio optimisation — execution strategies, backtesting, transaction cost modelling and optimisation methods.
• Blockchain, distributed ledgers and digital assets — technical foundations, smart contracts, token economics and their applications to financial systems.
• Risk management and model validation — credit, market and operational risk considerations for AI models, plus model governance and stress testing.
• Ethics, regulation and FinTech innovation — regulatory frameworks, data privacy, explainability, fairness and the broader impact of AI in finance.
• Individual project or dissertation — an independent research or development project, often carried out with industry datasets or in collaboration with external partners.

Entry requirements

Applicants are normally expected to hold a good undergraduate degree (typically a UK 2:1 or international equivalent) in a numerate discipline such as mathematics, statistics, computer science, engineering, economics or finance. Candidates with substantial relevant work experience in technology or financial services may also be considered.

Admissions panels look for demonstrable programming ability (for example Python, R or similar), quantitative skills and an understanding of basic machine learning or finance concepts. Depending on background, some applicants may be asked to complete preparatory modules.

International applicants must meet the University’s English language requirements; commonly accepted proofs include recognised qualifications demonstrating competence in academic English.

Career prospects

Graduates move into a wide range of technical and advisory roles across financial services, fintech and related industries. Typical job titles include:

• Quantitative analyst / researcher
• Data scientist or machine learning engineer (finance-focused)
• Algorithmic trader or execution strategist
• Risk analyst specialising in model risk and validation
• FinTech product manager or engineer
• Consultant in financial technology, regulatory technology (RegTech) or analytics
• Roles in digital asset firms, crypto exchanges or blockchain-focused ventures

Career development is supported by Loughborough’s careers service and by industry engagement through guest lectures, project collaborations and recruitment events.

Why study at Loughborough University

Loughborough offers a campus environment with strong links between computing, mathematics and management, enabling interdisciplinary teaching that reflects the needs of modern financial technology employers. Students benefit from practical computing facilities, access to data-focused labs and opportunities to work on industry-relevant projects.

The University has established relationships with businesses across finance and technology, which provide sources of real-world problems, guest speakers and recruitment pathways. In addition, students can draw on central career support, research expertise in data and analytics, and a broad network of alumni working in financial services and fintech.