Privacy Technologies for Financial Intelligence

It took a little while to write, but hopefully the following survey paper by Yang Li, Thilina Ranbaduge and yours truly can help demystify financial intelligence and privacy technologies for practitioners and technologists alike. The focus is on anti-money laundering and counter-terrorism financing, but the opportunity set is much broader. https://arxiv.org/abs/2408.09935 Here’s the abstract of … More Privacy Technologies for Financial Intelligence

Detecting Financial Crimes: Current State, Limitations, and A Way Forward

Financial Intelligence Units (FIUs) around the world collect data like threshold transaction reports, international fund transfer reports, and suspicious matter/activity reports from Reporting Entities (REs), which include banks, money remitters, casinos, law firms, real-estate companies, and financial companies. They may also get data about entities of interest from partner agencies (PAs) like law-enforcement agencies (LEAs) … More Detecting Financial Crimes: Current State, Limitations, and A Way Forward

Privacy Preserving Outlier Detection: A Tutorial

Outlier detection is an important tool in risk modelling. In the context where data are distributed across multiple locations and data privacy is a concern, we need to start looking at privacy-preserving techniques for doing outlier detection. Linked here is a tutorial introduction to this topic I recently prepared. Privacy Preserving Outlier Detection The presentation … More Privacy Preserving Outlier Detection: A Tutorial

Privacy Preserving Support Vector Machines: A Simple Version

Suppose we have two entities P1 and P2 and P1 holds a training dataset and P2 holds a dataset . (Assume for simplicity that the x’s all have the same dimension and each y is a real number. We’ll deal with the case when P1 and P2 measure different sets of variables in a different … More Privacy Preserving Support Vector Machines: A Simple Version