Regus Samsung Hub
What if just one financial crime-fighting solution could empower your business users to improve handling of anti-money laundering, credit card fraud, trade surveillance, and medical fraud?
Most financial crime-fighting systems have two disadvantages:
- They flag too many false positives that cause investigators to focus on the wrong cases.
- They involve manual procedures that result in investigations taking too long to complete.
TIBCO’s solution addresses both these disadvantages. In this Lunch & Learn, Andrew will demonstrate how your organisation can empower business users to improve handling of financial crimes such as anti-money laundering, credit card fraud, trade surveillance, and medical fraud through machine learning. This will be demonstrated in four parts:
- Visualising and understanding your data
- Using supervised learning to optimize known fraud detection cases
- Using unsupervised learning for unknown fraud cases detection
- Applying both models to your current dataset or in real time.
Light lunch will be provided.
For enquiry, please contact us at firstname.lastname@example.org