David talks with Jim Hatcher and Jeremy Hanna about use cases for fraud detection, how the landscape has changed over time, learns what fraudsters are, and how graph databases are perfect to fit the need for modern requirements.

Highlights!

0:16 - Welcoming Jim and Jeremy to the episode

0:53 - What is the landscape of fraud use cases?

2:22 - We talk through some examples of fraud detection

4:08 - Different SLA’s for an insurance claim compared to point of sale

5:06 - The balance of security vs. convenience

6:05 - Balancing the business value of aggressive fraud detection

6:45 - How much data, what’s the required SLA that fits the use case?

9:45 - What are fraudsters?

11:05 - Detecting human trafficking with automatic fraud detection

12:56 - Differences between fraud detection technology in the past with rules engines compared to current strategies using machine learning

17:38 - Using graph databases becoming more of a trend for fraud detection

20:50 - Fraudsters are pretty tech savvy

21:51 - Visualization can help when humans are in the loop

26:22 - Fraud is a perfect use case for a graph database

WATCH

Speakers

Jim Hatcher

at DataStax

David Jones-Gilardi Headshot

David Jones-Gilardi

Developer Advocate at DataStax