If I were to try to understand these data, I'd like to know (a) How are "fraudulent transactions" determined? (b) Is the mechanism perfect or just very good? (c) What fraction of fraudulent transactions does it capture? All? Or just most? (d) And I'd like the detailed data series.
Statistically speaking, I'm suspicious regarding the conclusion about lunch hours. In order to identify that feature robustly, it's necessary to very accurately know the variability of the background signal. Does it always dip at lunch hours and all at the same time? Does it always have the same shape? Recall what's being done here is separating the background "non-fraud transactions" from "fraud transactions". If the latter are much smaller in number than the former, and the determination of fraud has a finite miss rate or a finite false alarm rate, then all the implications of Bayes Rule apply, and the fraud signals being seen could be just some transfer function of the fraud mechanism applied to the background signal.
Moreover, do fraudulent signals increase in proportion to total background? Or are they a constant amount?
I think more work needs to go into deconvolving the two kinds of signals before assigning meaning to their parts and features of their parts.
No doubt the story assigned to these is plausible and compelling, but it is based upon unproven inferences. And, in my opinion, assuming that identifying and acting on these signals is simpler than it looks is getting way ahead of the art.