Projects
Bridgegap - Bridging the Gaps in Evidence, Regulation, and Impact of Anti-Corruption Policies
This EU-financed Horizon projects chief mission is to complete the creation of a new generation of scientific evidence to enable corruption deterrence and to prevent power abuse for personal, group, or network gain.
A Quantitative Approach to Estimate Trade-Based Money Laundering Risk
This project explores how advanced data analysis can help to estimate these trade-based money laundering risks.
Anti-Money Laundering/ Anomaly Detection in Data
Employing anomaly detection in data to combat money laundering.
Detecting tax avoidance
Applying network and data science techniques to uncover how multinational corporations minimise tax payments.
Neural Networks for Sustainable Risk Management
In this research, we use neural networks for sustainable risk management, and for efficient computation of implied volatility.
SPIN: Financial Risk Assessment with Network Analysis
A Utrecht University and ING collaborative project focused on advancing the bank's performance at identifying financial risk.
Fighting money laundering with machine learning
The aim of this project is improving the current anti-money laundering (AML) systems at the ING bank using machine learning technology.