In the age of AI, traditional rules-based fraud detection solutions are no longer sophisticated enough to catch fraudsters (who are constantly at the cusp of the technological curve).
In addition, they often create work for teams who must manually review potential fraud since they are not precise enough.
This guidebook includes:
- A broader overview of the role of anomaly detection in banking (beyond fraud) and ways to integrate the process into existing workflows.
- Code samples for a simple machine learning-based fraud detection model, along with ways to customize and improve it.
- Use case examples from innovative banking organizations.
