Using Machine Learning for Anti-Money Laundering
As money laundering fines have grown, so have regulatory expectations, and banks’ determination to meet them. But it’s not easy – criminal typologies are changing as well, and banks can find it hard to keep up.
This combination of factors is placing major strain on the financial investigation units responsible for identifying and reporting suspicious activity – false-positive rates can be as high as 95 percent.
It’s no surprise that banks are drafting robots in to help – machine learning (ML) tools seem ideally suited to weeding out suspicious activity, for example. But it can be hard to explain how some forms of machine learning reach their conclusions, presenting the industry with a different challenge.
Listen to this on-demand webinar to hear how practitioners are approaching the use of ML in their anti-money laundering programs, including:
- What are regulators expecting from your crime detection and analytics capabilities?
- Where can ML help – and where should it be avoided?
- Model risk challenges for ML
- How to address concerns about explainability