Financial Engineering Workshops - Alexander Denev, Turnleaf Analytics

Abstract:
Recent advances in AI, particularly large language models (LLMs), have transformed many areas of finance, offering powerful tools for analysts and investors. Yet, when it comes to forecasting financial and economic time series, LLMs often fall short. More traditional approaches—focused on careful data selection, curation, and specialized predictive methods—continue to demonstrate greater robustness, accuracy, and transparency. This talk will explore what truly works in practice, highlighting effective strategies for macroeconomic forecasting and the role AI can realistically play in this domain.

Bio: Alexander Denev is a quantitative data scientist and AI practitioner with over 15–20 years of experience in financial modelling, risk analytics, and advanced quantitative methods. He currently serves as CEO and Head of R&D at Turnleaf Analytics, where he leads development of macro-forecasting models and alternative data strategies. Previously, Denev held senior leadership roles including Head of AI (Financial Services) at Deloitte and Head of Quantitative Research & Advanced Analytics at IHS Markit. He has also worked across banks, risk advisory firms, and development institutions (e.g. RBS, European Investment Bank, Risk Dynamics).
Academically, he holds a degree in Mathematical Finance from the University of Oxford and is a visiting lecturer there on topics of Bayesian risk management, alternative data and graphical modelling. He also holds an MSc in Physics (with emphasis on AI) from the University of Rome. Denev is author or co-author of multiple books and papers on topics including stress testing, scenario analysis, machine learning in finance, and probabilistic graphical modelling

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