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AI4FinTech is a community initiated by the University of Amsterdam. Within the community, AI expertise is brought together with expertise in Financial Technology. We aim to become one of the leading locations worldwide in AI4FinTech research and entrepreneurship. The community is open to anyone employed in academia, the financial industry, governmental institutions and others interested in the topic.

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

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)

    External partners

    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)

    External partner


  • 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)           

    External partner

    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)

    External partners

    Mollie, ING

  • 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)

    External partner


  • 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)

    External partner


External Partners

The external partners that we will be collaborating with on above projects include:

  • Mollie
  • ING
  • Imprima
  • Zuva AI
  • Adyen
  • Transaction Monitoring Netherlands
  • Ernst & Young