Drawing parallels with evolving technology trends, this article examines the shift from traditional fraud detection methods to AI-powered systems. It outlines how Nikhil Kapoor reviews supervised, unsupervised, and deep learning techniques driving real-time fraud analysis. For example, decision trees and neural networks enhance transaction monitoring, reducing false positives in financial sectors.
Q&A
- What advantages does AI offer over traditional fraud detection?
- How do supervised and unsupervised learning differ in this context?
- What are the remaining challenges in AI-driven fraud detection?
