Our main objectives are:
- Doing academic research on AI methods specifically developed for Fintech and their use and regulation - published in leading academic journals and conferences
- Knowledge dissemination, primarily at our Meetups
- Development of new insights, products and services, useful to the industry, startups and government
- Courses and Masterclasses
We primarily work on the following topics:
- Compliance of AI algorithms in Finance
- Fraud detection and money laundering using AI
- Risk management with AI and Computational/Mathematical modelling
- Responsible AI based Financial Services
- Sustainable Finance
Read more about Drona Kandhai
Prof. Drona Kandhai is head of the Quantitative Analytics department of Financial Markets in ING Amsterdam. He is professor in Computational Finance at the Computational Science Lab and the Stochastics group of the University of Amsterdam. Drona has been active in the financial industry for close to 20 years where he has worked on pricing and risk model validation, development and integration in IT systems. Kandhai has extensive experience in a broad range of financial products and has worked in several departments of ABN AMRO and ING Bank. He holds a PhD in Computational Physics from the University of Amsterdam on the subject of numerical modelling and simulation of complex fluid flows.
His primary research interests are on the use of data-driven microscopic simulations as a way to understand the complex behaviour of financial markets, and how to include the driving mechanisms in multi-scale models for derivatives pricing and risk management. Kandhai has published extensively in various scientific journals.
[Vacancy] - Lead Non-Financial Risk Theme
This position is currently vacant.
Read more about Erman Acar
Dr. Erman Acar is an assistant professor of explainable AI (XAI) for finance, with a joint appointment shared between Socially Intelligent Artificial Systems (SIAS) group at Informatics Institute, and the Cognition, Language and Computation (CLC) lab at the Institute of Logic, Language and Computation. His current research focuses on integrating machine learning systems with causal and logical symbolic components to extend their capabilities in reasoning and explainability.
Erman completed his PhD in automated decision making using knowledge representation in single-agent and multi agent settings in 2018 at the University of Mannheim, Germany, and later worked as a postdoctoral researcher in the Knowledge Representation and Reasoning group at the Vrije Universiteit Amsterdam and the Reinforcement Learning group at Leiden University. During his career, he has been publishing his research in major AI journals and conference, and he has done research visits to various institutes including Free University of Bolzano, University of Calabria, University of Amsterdam and University of Oxford.
Read more about Fernando Santos
Dr. Fernando Santos is an Assistant Professor at the University of Amsterdam, at the Socially Intelligent Artificial Systems (SIAS) group. He has an ICAI joint appointment position with ING Bank, developing research on fairness, transparency, and strategic dynamics in AI. Fernando received his PhD in Computer Science and Engineering in 2018, from Instituto Superior Técnico (Lisbon, Portugal).
Fernando’s research lies at the interface of AI and complex systems: He is interested in understanding collective dynamics in multiagent systems and in designing fair/pro-social AI. Before joining UvA, Fernando was a James S. McDonnell postdoctoral fellow at Princeton University (Levin Lab). He was a visiting student at Princeton, Université Libre de Bruxelles and TU Delft.
Read more about Simon Trimborn
Simon Trimborn (PhD) is an Assistant Professor of Econometrics and Data Science at the Amsterdam School of Economics, University of Amsterdam. Before joining the UvA, Simon was an Assistant Professor at the Department of Management Sciences at City University of Hong Kong and a Research Fellow at National University of Singapore.
Simon’s research focuses on high-dimensional data analysis to tackle specific problems in the cryptocurrency market, blockchain and social networks. For his research Simon is developing and using network methods, text analysis methodologies, and investment techniques. Simon’s work introduced the CRIX index family which is owned by Royalton Partners and computed by S&P Global: https://www.royalton-crix.com/ . Simon serves on the Scientific Board for the CRIX index and is an Associate Editor for the journals Digital Finance and Annual Review of FinTech.
Read more about Marc Salomon
Prof. Marc Salomon is program co-director of AI4Fintech, Dean of the Amsterdam Business School (ABS), Professor in Decision Sciences and Program Director of the MBA in AI, Data and Analytics.
His main teaching and research interests are in Business Analytics: using statistics, mathematics, and IT to solve business problems. Prior to joining the University of Amsterdam, Marc was Director of the Center for Applied Mathematics at Rabobank (1996-1998). COO and Director of Research at McKinsey & Company (1998-2004) and COO at law firm Stibbe (2004-2013). From 1996 to 2005 he had a part-time appointment at Tilburg University as a professor in Operations Research. Marc holds a Master of Science (MSc) in Econometrics from VU Amsterdam University, and he obtained a PhD from Erasmus University Rotterdam. He published numerous articles in leading academic journals, such as Operations Research, Management Science and Transportation Science
Read more about Marcel Worring
Prof. Marcel Worring is program co-director of AI4FinTech, a full professor in the Informatics Institute and leading the MultiX group. With his group he focuses on AI based techniques which extract rich information from the data and support the interaction with the user in a hybrid intelligence setting. Advanced visualizations make the interaction intuitive and explainable. A focus of the group is hypergraph based methods which we are now developing into tools for Anti Money Laundering. Worring has a long history in working in interdisciplinary research and was the initiator of the AI4Fintech program.
Actively Contributing Members
Read more about Giuseppe Dari-Mattiacci
Giuseppe Dari-Mattiacci is professor of law and economics, a fellow of the Tinbergen Institute and a research member of the European Corporate Governance Institute. He studied in law (D.Jur. University of Rome “La Sapienza”, LL.M. and J.S.D. Columbia Law School), law & economics (LL.M. and Ph.D. Utrecht University) and mathematics (B.A. University of Amsterdam). He has been a professor at Columbia Law School and a vising professor at George Mason University, the University of Chicago, Columbia University, and New York University. In 2018-2020 he served as the president of the European Association of Law & Economics. His research focuses on the legal and organizations determinants of innovation in finance, contracts and law.
Read more about Cees Diks
Prof. dr. Cees Diks is Professor of Data Analysis and Economics Statistics. He is Co-director of the Center for Nonlinear Dynamics in Economics and Finance (CeNDEF), General Director of the Research Centre for Sustainable Investments and Insurance (RCSII) and Programme Director of the MSc in Data Science and Business Analytics.
Co-supervisor of the PhD project: Environment, Social and Governance (ESG) regulation impact on financial stability
Read more about Marc Francke
Prof. Marc Francke is full professor Real Estate Analytics at the Finance Group of the University of Amsterdam Business School. Most of his research involves commercial and residential real estate space and asset markets, including real estate property valuation using traditional econometric and machine learning approaches. In addition to his position at the university,
Professor Francke is affiliated with Ortec Finance's R&D Lab. He holds a PhD in Econometrics from the Vrije Universiteit Amsterdam on the topic of state space modelling with applications to real estate valuation. Professor Francke is president (2023 – 2024) of the European Real Estate Society and secretary of the European Commercial Real Estate Data Alliance.
His primary research interests are price dynamics and property price Index construction, market and funding liquidity, and automated valuation models. His academic research has resulted in publications in various scientific journals.
Read more about Davide Grossi
Dr. Davide Grossi is associate professor at the Amsterdam Law School, the Institute for Logic, Language and Computation of the University of Amsterdam, and the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence of the University of Groningen. He holds a PhD in Computer Science from the University of Utrecht (2007). Grossi’s research focuses on algorithmic aspects of collective decision making.
He contributes to several areas within AI and computer science research, including: multi-agent systems, knowledge representation and reasoning, blockchain, computational logic.
Read more about Paul Groth
Paul Groth is Professor of Algorithmic Data Science at the University of Amsterdam where he leads the Intelligent Data Engineering Lab (INDElab) and directs the UvA’s Data Science Center. He holds a Ph.D. in Computer Science from the University of Southampton (2007) and has done research at the University of Southern California, the Vrije Universiteit Amsterdam and Elsevier Labs.
His research focuses on intelligent systems for dealing with large amounts of diverse contextualized data. Paul is scientific director of the. Additionally, he is co-scientific director of two Innovation Center for Artificial Intelligence (ICAI) labs. Data is critical for enabling Fintech and his work aims to make data estates that are more useful and governable.
Read more about Kristina Irion
Kristina Irion is Associate Professor at the Institute for Information Law (IViR) at Amsterdam Law School. She is the Director of the Academic Excellence Track (AcET) and IViR’s Summer Course on Privacy Law and Policy. A baseline of Kristina’s research is the interpretation and analysis of the transformational processes that reconfigure the legal properties of digital data in line with societal needs.
Kristina’s research agenda focuses on the governance of transnational digital technologies and global data value chains from the perspective of European law and international economic law. Research projects she led or participated in have achieved high scientific recognition and societal impact. She frequently provides expertise to EU institutions, the Council of Europe, the OECD, the Dutch government as well as civil society organisations.
Kristina is a member of the Scientific Committee of the annual Computer Privacy and Data Protection (CPDP) International Conferences and the International Advisory Board of the Electronic Privacy Information Center (EPIC). In the AI4Fintech programme Kristina co-supervises a PhD research project on “Systems for AI Data Quality in Finance”.
Read more about Jaap Kamps
Dr.ir. Jaap Kamps is an associate professor of information retrieval at the University of Amsterdam, at the natural language processing and digital humanities group of the Institute for Logic, Language and Computation.
His research interests span all facets of information storage and retrieval from user-centric to system-centric, and from basic research to applied research. Current interests are in “IR for social good” by working on novel access tools for cultural heritage, political,
and legal data, and on developing more efficient and interpretable neural models for search and analytics.
Read more about Sara Magliacane
Dr. Sara Magliacane is an assistant professor in Causality at the Amsterdam Machine Learning Lab at the University Amsterdam, and a Research Scientist at MIT-IBM Watson AI Lab. Her research focuses on three directions, causal representation learning, causality-inspired machine learning and how causality ideas help RL adapt to new domains and nonstationarity faster. Her goal is to leverage ideas from causality to make ML methods robust to distribution shift and generalizable across domains and tasks. Sara also continues working on her previous research on causal discovery, i.e. learning causal relations from data.
Sara received a PhD at the VU Amsterdam on learning causal relations jointly from different experimental settings, even with latent confounders and small samples. She has interned at Google Zurich and NYC, and then she was a postdoctoral researcher at IBM Research NY, working on methods to design experiments that would allow one to learn causal relations in a sample-efficient and intervention-efficient way. During Spring 2022, she was a visiting professor in the Simons Institute in Berkeley for a semester on Causality.
Read more about Edoardo D. Martino
Edoardo D. Martino is a assistant professor (tenure track) of Law & Finance at the University of Amsterdam (UvA) and a Research Associate at the European Banking Institute (EBI).
Edoardo earned his PhD in Law and Economics at Erasmus University Rotterdam (EUR), an LL.M. in Law & Economics (EUR - with distinction) and JD-equivalent Law Degree from (University of Florence-summa cum laude). He was a visiting researcher in several Universities, such as Oxford University, University of Hamburg, University of Bologna, Goethe University Frankfurt.
Edoardo engages in research and teaching activities in the broad field of Law & Finance. His research focuses on financial regulation, corporate governance and fintech. On these topics, he published book chapters in houses such as Routledge and Elgar, as well as articles in journals such as the European Business Organization Law Review (EBOR), the Journal of Corporate Law Studies (JCLS), and the European Company and Financial Law Review (ECFR). He is a guest contributor to, among others, the Oxford Business Law Blog (OBLB) and the Columbia Blue Sky Blog.
Read more about Maarten Marx
Dr Maarten Marx is an assistant professor in the Information Retrieval Lab at the Informatics Institute of the University of Amsterdam. His research is on information retrieval from semi structured data, with an emphasis on governmental data. Currently he applies IR technology to documents released under the Dutch Freedom of Information Act, treating those documents as FAIR data.
Within AI4FinTech Marx is connected to the AIDA (Artificial Intelligence for Due-diligence Analysis) project.
Read more about Ana Mićković
Dr. Ana Mićković is an assistant professor of Accounting at the University of Amsterdam since 2019. She earned her PhD from the Karlsruhe Institute of Technology and holds both a Bachelor's and Master's degree in Economics from the University of Zagreb's Faculty of Economics and Business, as well as a Bachelor's degree in Mathematics from the Faculty of Science. Ana gained professional experience at the Ministry of Finance of the Republic of Croatia, University College for Law and Finance, and Croatian National Bank. Her research focuses on the application of artificial intelligence in company performance forecasting and accounting fraud detection, including the detection of greenwashing behavior.
Read more about Hossein Nabilou
Hossein Nabilou is an Assistant Professor of Law at the University of Amsterdam, Amsterdam Law School. Prior to the UvA, Hossein was UNIDROIT - Bank of Italy Chair at the International Institute for the Unification of Private Law (UNIDROIT), where he was involved in the digital assets and private law and bank insolvency projects. Hossein was also a postdoctoral researcher and lecturer in Banking and Financial Law at the Faculty of Law, Economics and Finance of the University of Luxembourg. He had held postdoctoral and visiting researcher positions at the Ludwig-Maximilians-University (LMU) Munich, Columbia University in the City of New York, and EUROPAINSTITUT in Basel, where he had worked on banking structural reforms, money view of banking, and regulation of hedge funds, respectively. Hossein holds a Ph.D. from the Erasmus University of Rotterdam, an LL.M. from the University of Pennsylvania Law School, an LL.M. in Public Law, and an LL.B. both from Shahid Beheshti University School of Law,
Hossein’s academic interests include Law & Finance, Regulation of Financial Markets and Institutions, European and International Financial Regulation, Law & Financial Technology, and Company Law. His research focuses on monetary and banking law, shadow banking, financial market infrastructures, banking structural reforms, financial contracting, and legal issues of fintech, payments, blockchain, and crypto-assets. Hossein is the author of several award-winning articles published in internationally renowned peer-reviewed law journals.
Read more about Debraj Roy
Dr. Debraj Roy is an Assistant professor at the Computational Science Lab, University of Amsterdam. He was an Assistant professor at the Faculty of Behavioural, Social and Management Science, University of Twente. Debraj is also a research fellow at the 4TU.DeSIRE - the Strategic Research Program on Resilience, the Netherlands and the Amsterdam Institute for Global Health and Development. He holds a PhD degree in Computational Science from Nanyang Technological University, Singapore.
Debraj has a strong passion for understanding how societies ‘emerge’ due to uncoordinated self-organization and how sustainable development can be achieved in this era of rapid transitions. His current research focuses on the dynamics of complex social systems such as understanding the interaction between climate change and poverty traps. Debraj is also interested to develop computational methods such as natural-scale agent-based models and methods for uncertainty quantification, necessary to understand these complex systems.
Read more about Michael Werner
Dr. Michael Werner is an Associate Professor of Accounting Information Systems (AIS) at the Amsterdam Business School, University of Amsterdam. Before joining UvA he worked at the Copenhagen Business School in Denmark, the Auckland University of Technology in New Zealand, and as a professional auditor at PwC Germany for several years specialised in IT and business process audits. He is certified as an Information Systems Auditor (CISA), Information Security Manager (CISM), and in the Governance of Enterprise IT (CGEIT).
His primarily design science driven research focusses on data science in the context of accounting and auditing. He investigates how novel data analysis techniques can be developed and applied particularly in the context of statutory audits. His research on process mining for financial statement audits was awarded the American Accounting Association’s AIS Section Outstanding Dissertation Award, and he received several scholarships related to his work. His research was published by the International Journal of Accounting Information Systems (IJAIS), the Journal of Information Systems (JIS), the IEEE Transactions on Services Computing (IEEE TSC), and several leading information system conferences. He served as an academic advisor to the International Auditing and Assurance Standards Board (IAASB) as a member of its Data Analytics Project Advisory Panel and currently acts as a special editor for the JIS.
Read more about Amit Zac
Amit Zac is a postdoctoral researcher at the University of Amsterdam and ETH Zurich. Before joining UvA, Amit was a PhD student and a teaching assistant in Competition Law and Empirical Research Methods at The University of Oxford, Law Faculty. He obtained his LL.M. from Erasmus University Rotterdam and the University of Hamburg in Law and Economics with distinction.
Amit’s work focuses on competition policy and its welfare implications. His research looks at social and economic challenges associated with the digital economy, and specifically economic inequality and privacy. His scholarship combines legal and policy analysis with empirical studies, integrating law research within the wider social sciences. His previous research project, funded by the Leverhulme trust, focuses on the causal effect of competition law on economic inequality, and incorporates a range of methodological approaches.
Currently Amit is working on a two-year Swiss Science Foundation project on the automation of privacy laws enforcement using machine learning and natural language processing (NLP).
Cross-faculty PhD research projects
AIDA: Artificial Intelligence for Due-diligence Analysis
In this project, we aim to develop information retrieval and natural language processing technology for e-discovery and due diligence analysis on legal and financial textual documents, and to support legal professionals searching for very specific information in huge sets of disclosed documents.
Marc Francke (ABS), Jaap Kamps (ILLC)
Imprima, Zuva AI
Environment, Social and Governance (ESG) regulation impact on financial stability
In this project, we will analyze the driving factors behind ESG ratings via ML and XAI which will lead to a clearer understanding of how companies will be affected by ESG regulation. The insights gained from this analysis will allow us to study the effects on financial stability in a simulation study using agent-based models under realistic settings derived from empirical analysis.
Simon Trimborn (ASE), Debraj Roy (IvI)
HyperMining: Explainable Anti-Money Laundering using Process Mining on Hypergraphs
In this project we aim to develop an innovative Anti-Money Laundering methodology using advanced AI methods using hypergraph representations and process mining which can give an integral view of the transactions involved, deal with the inherent complexity of the data, and still be understandable for the experts analyzing the data so they can substantiate their decisions.
Marcel Worring (IvI), Michael Werner (ABS)
Transaction Monitoring Netherlands
Knowledge-Driven Learning for XAI in Fraud Detection
In this project we aim to develop a novel neuro-symbolic framework that mainly combines the strengths of both the data-driven approaches (which comes with adaptability, autonomy, and good qualitative performance) and the knowledge-driven approaches (which comes with interpretability, maintainability, and well-understood computational characteristics) to provide explanations for experts in terms of relevant features and the structures in-between.
Erman Acar (IvI/ILLC), Ilker Birbil (ABS)
Robust fraud detection through causality-inspired ML
Payment platforms like Adyen use technology to efficiently detect fraud. Fraud detection is challenging, since both the genuine and fraudulent customer behavior changes over time and across markets. Machine learning is crucial for this task, but current methods are susceptible to learning spurious correlations. The goal of this project is to leverage causality-inspired machine learning methods to improve the robustness of fraud detection methods to distribution shifts.
Sara Magliacane (IvI), Ana Mickovic (ABS)
Systems for AI Data Quality in Finance
The impact of data errors on the output of AI models is difficult to anticipate and measure, and these errors can negatively impact regulatory compliance. Therefore, this project aims to enable non-technical users to validate and increase the quality of their data. For that, these users should be able to express data quality rules in natural language. We will design a data driven approach to leverage such rules to assist a domain expert to finetune data quality rules and “stress test” downstream AI models. This project favors a strong data engineering background combined with an interest to engage with European regulation applicable to financial data.
Sebastian Schelter (IvI), Kristina Irion (IViR)
The external partners that we will be collaborating with on above projects include:
- Zuva AI
- Transaction Monitoring Netherlands