University of Turin

What you'll study

The programme provides an integrated curriculum of mathematics, statistics, finance and insurance theory, with practical training in computational methods and data analysis. Teaching typically combines core modules, elective specialisations, laboratory classes and a research-oriented master's thesis.

• Core quantitative foundations: real and functional analysis, probability theory, stochastic processes and stochastic calculus for modelling asset prices and insurance risks.
• Financial mathematics and derivatives: pricing of derivatives, risk-neutral valuation, term-structure models, credit risk and fixed-income mathematics.
• Actuarial and insurance mathematics: survival models, life contingencies, collective risk models, premium principles, reserving techniques and basics of non-life insurance modelling.
• Econometrics and time series: likelihood-based inference, state-space models, volatility modelling (GARCH), and forecasting methods applied to financial and insurance data.
• Numerical methods and computation: Monte Carlo simulation, finite-difference methods for partial differential equations, optimisation techniques and numerical linear algebra, with practical work in Python, R and/or MATLAB.
• Risk management and regulation: market, credit and operational risk measurement, enterprise risk management, insurance regulation and solvency frameworks, model validation and stress testing.
• Machine learning and data analytics: supervised and unsupervised learning for pricing, claims analytics, clustering, feature selection and big-data tools relevant to finance and insurance.
• Practical components: laboratory courses, project work with real datasets, an industry internship or applied project and a supervised master's thesis demonstrating research or applied skills.

Entry requirements

Applicants are normally expected to hold a recognised bachelor's degree (or equivalent) in mathematics, statistics, physics, engineering, economics, finance or a closely related discipline, with substantial quantitative content. Typical entry preparation includes coursework in calculus, linear algebra, probability and statistics, and introductory programming.

• Academic background: degree with adequate mathematical and statistical training. Graduates from other disciplines may be considered if they can demonstrate the required quantitative skills.
• Language: proficiency in English is required for international students; some courses or assessments may also be offered in Italian depending on the track. Proof of language ability is requested where relevant.
• Additional assessment: selection may involve evaluation of transcripts, a CV, reference letters and an interview; some candidates may be asked to complete a short quantitative test or provide examples of previous quantitative work.
• Preparatory courses: applicants lacking specific prerequisites may be asked to take bridging modules before or during the first semester.

Career prospects

Graduates from the programme are prepared for quantitatively demanding roles across finance and insurance. The curriculum balances theory and applied skills to make graduates competitive in both industry and research roles.

• Quantitative analyst (pricing models, derivative desks, model development)
• Actuary and actuarial analyst (life and non-life insurance pricing, reserving and capital modelling)
• Risk manager (market, credit and operational risk measurement and regulatory compliance)
• Data scientist or statistician for financial and insurance datasets
• Portfolio manager or quantitative researcher in asset management
• Regulatory and consulting roles focused on solvency, stress testing and model validation
• Progression to PhD or academic research in probability, financial mathematics or actuarial science

Why study at University of Turin

The University of Turin is one of Italy's long-established universities with active research groups in applied mathematics, statistics, economics and finance. The programme benefits from interdisciplinary teaching across departments that emphasise both theoretical rigour and practical applications.

• Research and teaching strength: access to faculty engaged in mathematical finance, econometrics and actuarial research, and opportunities to participate in research projects.
• Practical links: collaborations with local and national financial and insurance organisations, offering internship opportunities and applied project work.
• Computational facilities: practical training in standard industry tools and programming languages, and access to computing resources for numerical projects.
• Location and network: studying in Turin offers links to a regional business ecosystem and professional networks in finance, insurance and consulting.