That is definitely a trend. Similarly, Alaska (first in the abbreviated drop down list for states) could be fraudulent due to the same reason. I don't believe that the fraud users are actually from Alaska.
The "night owl thing" isn't misinterpretation. It is true that there are less total transactions at night, but the point and observation is that the fraud "rate" is higher at night.
Another way to put it: fraudsters are more likely to be night-owls than the rest of us.
Possibly, but you could also decide that there simply isn't enough signal to draw a conclusion. We don't know if time zones are accounted for properly. We don't know if this fraction represents a significant number of users - for example, the number of fraudulent users could be the least at 3AM, but if the decrease in users overall is greater, then the percentage increases. The data is misrepresented.
Hi there, I worked on retrieving and distilling this dataset at Sift Science. To address your concerns: Yes, time zones are accounted properly. And yes, the number of data points is significant.
As I've said earlier, you can see in the users count that fraudulent users are indeed the lowest at night (which makes sense, since fraudsters sleep as well). However, we are looking at the "fraud rate" (#fraud / all users in a given hour). I'm not sure what you even mean by "misrepresent" the data. The data speaks for itself.
If you read the labels for the graph, it says "local time" of the user / fraudster. If we didn't take into account the local time of the user, the data would not be very interesting to look at (mostly uniform) since we have users from all over the globe.
