Money laundering often involves significant financial impact, and is also associated with activities such as people/drug trafficking, terrorism and corruption. It is so important that the Department of Treasury in the US produced a National Money Laundering Risk Assessment report in 2015.
Machine learning, in particular deep learning, has emerged as one of the most powerful classes of artificial intelligence algorithms. In recent years, deep learning has made significant impacts in various applications, from computer vision to speech processing to natural language processing.
In this project, you will implement and study important issues related to the use of machine learning against money laundering. You will work on real data provided by an online bank in China.
UROP1100 UROP2100 UROP3100 UROP4100
The student will implement and experiment with novel algorithms. Strong interest in machine learning and good programming skills are expected.
Applicant's Learning Objectives:
1) Understanding machine learning, and in particular deep learning;
2) Understanding anti-money laundering (AML);
3) Invent and implement new ideas related to machine learning and AML.