Machine Learning (ML)
Machine Learning offers the greatest advantage through its ability to learn from existing systems, reducing the need for manual input into monitoring, reducing false positives and identifying complex cases, as well as facilitating risk management.
The most useful applications of Machine Learning to AML/CFT include:
- Identification and Verification of customers through authentication AI, including
biometrics, and liveness detection techniques (micro expression analysis, anti-spoofing
checks, fake image detection, and human face attributes analysis)
- Monitoring of the business relationship and behavioural and transactional analysis by
using Machine Learning algorithms to place customers with similar behaviour into
cohesive groupings to monitor and alert scorings to focus on patterns of activity
- Identification and implementation of regulatory updates: Machine Learning techniques
can scan and interpret big volumes of unstructured regulatory data sources on an
ongoing basis to automatically identify, analyse and then shortlist it based on the
institutions’ requirements.
- Automated data reporting (ADR) can make granular data available in bulk to
supervisors.
Sources: