Insights
Master in Computational Finance (MCF)
MCF Insights
Ideas shaping the future of computational finance.
MCF Insights explores developments shaping the future of computational finance through essays, analysis and perspectives from faculty members, alumni and industry partners.
The goal is to provide practical and informed perspectives on how technology, data and quantitative methods are changing financial practice.
Featured perspectives
Stories from the MCF community
Alumni story
From MCF student to Financial Analyst
Jovana Ljutovac's alumni story explores her transition from MCF student to Financial Analyst at Kvantum Capital Partners and the role quantitative training played in that journey.
Jovana Ljutovac
Program history
From IMQF to MCF
Prof. Branko Urošević reflects on more than two decades of quantitative finance education in the region and the path from IMQF to today's Master in Computational Finance.
Prof. Branko Urošević
What we explore
Finance, code, data and technology
Careers in computational finance
Roles, skills and career paths across quantitative finance, banking, fintech, risk, investment management and technology.
Python & R
Why programming remains a core skill in modern quantitative roles and how Python and R are used in financial practice.
Artificial intelligence
Machine learning, large language models and the growing role of AI in markets, analysis and financial decision-making.
Financial risk
Quantitative risk methods, model validation, regulatory frameworks and the work performed by modern risk teams.
Algorithmic trading
Quantitative strategies, trading systems, market data and how technology changes the way financial markets operate.
Financial technology
New models in fintech, digital finance and the intersection between software engineering, data and financial services.
Current themes
Questions worth exploring
01
Why Python and R still matter in quantitative finance
A practical look at why both languages remain important across modelling, data analysis, research and production workflows.
02
Large language models in financial markets
How generative AI and large language models may be used in research, market analysis and trading environments.
03
What junior analysts actually do in IRB risk models
An applied look at Internal Ratings-Based modelling and the day-to-day analytical work behind modern credit-risk functions.
MCF editorial
Practical perspectives, not abstract commentary.
Insights brings together perspectives from faculty, alumni and industry partners to examine how computational methods are actually used in financial practice - from programming and risk modelling to machine learning, trading and emerging financial technology.