This project explores how advanced data analysis can help to estimate these trade-based money laundering risks.
Employing anomaly detection in data to combat money laundering.
Applying network and data science techniques to uncover how multinational corporations minimise tax payments.
In this research, we use neural networks for sustainable risk management, and for efficient computation of implied volatility.
A Utrecht University and ING collaborative project focused on advancing the bank's performance at identifying financial risk.
The aim of this project is improving the current anti-money laundering (AML) systems at the ING bank using machine learning technology.
Utrecht UniversityHeidelberglaan 83584 CS UtrechtThe NetherlandsTel. +31 (0)30 253 35 50