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.

Supervisors

Marc Francke (ABS), Jaap Kamps (ILLC)

External partners

Imprima, Zuva AI

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.

Supervisors

Simon Trimborn (ASE), Debraj Roy (IvI)

External partner

ING

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.

Supervisors

Marcel Worring (IvI), Michael Werner (ABS)           

External partner

Transaction Monitoring Netherlands

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.

Supervisors

Erman Acar (IvI/ILLC), Ilker Birbil (ABS)

External partners

Mollie, ING

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.

Supervisors

Sara Magliacane (IvI), Ana Mickovic (ABS)

External partner

Adyen

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.

Supervisors

Sebastian Schelter (IvI), Kristina Irion (IViR)

External partner

ABN-AMRO