Corvinus University of Budapest

What you'll study

This graduate diploma focuses on core actuarial methods and their application to insurance, pensions and financial risk management. Teaching mixes rigorous theory with practical, calculation‑based training and casework. Typical modules and topics include:

• Probability and Mathematical Statistics – measure‑theoretic and applied approaches to probability, estimation theory, stochastic modelling for claims and financial data.
• Financial Mathematics – interest theory, bond and derivative pricing, discounted cash flows, replication and hedging techniques.
• Life Contingencies and Survival Models – lifetable construction, survival models, reserving and pricing of life insurance and annuities.
• Non‑life Insurance and Reserving – claims modelling, frequency‑severity models, chain‑ladder and other reserving techniques for property & casualty business.
• Risk Theory and Solvency – ruin theory, capital modelling, Solvency II concepts and regulatory capital frameworks as applied in European markets.
• Statistical Modelling and Data Analytics – generalized linear models (GLMs), time series, machine learning techniques for pricing and underwriting, and practical use of statistical software (e.g. R, Python).
• Actuarial Practice and Professionalism – ethics, communication, report writing, case studies from consulting and insurance operations, and preparation for professional exam syllabuses.

Programme delivery typically combines lectures, tutorial/problem classes, computer laboratories and a capstone project or applied assignment with real or simulated industry data. Assessment is a mix of written exams, coursework, practical assignments and the project.

Entry requirements

Applicants are normally expected to hold a recognised bachelor’s degree with substantial quantitative content, for example in mathematics, statistics, actuarial science, economics with strong mathematics, physics or engineering. Typical academic preparation includes calculus, linear algebra, probability and introductory statistics. Relevant professional experience or successes in mathematical competitions can strengthen an application.

Other entry conditions commonly include:

• A transcript demonstrating strong performance in quantitative courses.
• Proof of English language competence if previous degree was not taught in English (e.g. recognised test scores or institutional evidence).
• A curriculum vitae and a supporting statement describing motivation and relevant background.
• Occasionally an interview or a short diagnostic test to assess mathematical readiness may be requested.

Career prospects

Graduates move into roles across insurance, pensions, banking and risk management. Common job titles include actuarial analyst, pricing analyst, reserving actuary, risk modeller, pensions consultant and solvency specialist. Employers include life and non‑life insurers, reinsurance companies, pension funds, consulting firms, banks and regulatory bodies.

Graduates who combine the diploma with continued study and professional exam passes progress to fully qualified actuary status under national or international actuarial bodies. The programme also provides a foundation for further academic study such as a research master’s or doctoral work in statistics, finance or actuarial science.

Why study at Corvinus University of Budapest

Corvinus has a long tradition in economics and business education in Hungary and Central Europe, offering a curriculum that connects economic theory with quantitative methods and applied practice. The university maintains close links with the Hungarian insurance sector, pension administrators and consulting firms, enabling guest lectures, industry projects and recruitment contacts.

Students benefit from teaching staff with academic and professional actuarial experience, access to statistical and actuarial software, and an international student community in Budapest — a regional hub for finance and insurance. The diploma is structured to support entry into the actuarial profession while also equipping students with transferable skills in modelling, data analysis and risk assessment.