What I like best about FullStory is that it gives me a single place to see behavior, device, and identity signals across the whole user journey in real time, which is critical for fraud detection on our platform. In my role on Digital Risk, I use custom segments, alerting, metrics daily to continuously watch for known fraud patterns instead of waiting for batch reports or log pulls.
The most helpful aspects for me are:
Self‑serve segments and filters that map directly to our fraud behaviors, so I can iterate quickly.
High fidelity session replay that lets me distinguish real fraud from noisy signals. I can see exactly how a user moves through account recovery, password reset, MFA setup, past loads, and high‑value commodities, which is often the difference between a bounce and a missed theft attempt.
Impact on outcomes: in 2025, I used FullStory alerts plus manual pattern recognition to stop millions in attempted freight theft, and roughly 90% of those detections originated in FullStory and my custom alerting.
Partnership and fraud specific tooling. FullStory’s fraud team (e.g., Van and Adam) has made us a pilot customer for features like suspicious activity dashboards, credential stuffing and bot behavior signals, and session labeling/tagging (“confirmed fraud” tags), and they actively incorporate my feedback on what matters for freight fraud vs. e-commerce fraud.
Because the product is flexible and the team is collaborative, FullStory has essentially become my primary console for detecting and understanding digital fraud behavior on our platform. Review collected by and hosted on G2.com.
The main downside for my use case is alerting latency and scale out of the box. Before we partnered with engineering to build a Streams/webhook → Snowflake → Slack pipeline, I had to sit on FullStory segment pages and manually refresh them all day to catch new fraud alerts. At our current volume (~20+ alert events per day that require action across ~8+ fraud segments), that workflow was not sustainable, especially heading into holiday theft season when alerts and workload both spike.
A second challenge is coverage gaps when sophisticated actors block tracking. We’ve had confirmed cases where fraud parties successfully logged in, set up phone MFA, and booked or attempted to book loads, but left no trace in FullStory at all because they used ad‑blockers or script‑blocking tools to prevent the FullStory library from loading. Engineering has mitigated a lot of this by proxying FullStory traffic through our own domains so it doesn’t look like a third‑party tracking script, but it’s still a structural limitation: very sophisticated attackers can sometimes stay invisible to any analytics product that relies on client‑side scripts.
Finally, there are a few fraud signals that I can see in replay but can’t always query or alert on directly. For example, very high tab counts, device‑ID searchability, mid‑session IP changes. FullStory shows those behaviors in the session, but historically I haven’t always been able to build segments on them in a self‑serve way, which means more manual review work. To their credit, the team is actively working with me to expose more of those as structured, segmentable events (e.g., “excessive tabs,” “multiple emails,” and device‑ID searchability). Review collected by and hosted on G2.com.
