I think there's some confusion -- we actually do have a global machine learning model, which is what customers will start with if we haven't received any labels (training examples to learn from). But, your mileage may vary with the global model. We think it's a starting point, but that fraud differs from site to site in subtle ways, and to be truly effective you need to have your own model. This is why training examples are so critical. Once we receive enough training examples, we're able to build a model specific to your site.
That said, I'll be the first to agree with you -- we haven't done a great job setting the right expectations and messaging, especially on the point above (that your mileage may vary with the global model). We've been working hard to improve this, and there are now several reminders in the console, and an introductory tour, that emphasize the importance of a thorough integration with labels to achieve best results. With machine learning especially, bad data in = bad results out. Also, our first UI/UX designer started a month ago, and improving this experience is a top priority.
With regards to making fraud facts more functional and actionable -- we actually do provide reasons we think a user is suspicious in our web console and API (see https://siftscience.com/docs/getting-scores). But, there isn't an "aggregate learnings" page like what's presented in the blog post -- that's on the 3-month roadmap.
Again, sorry for the subpar experience, and I'll follow up with you separately. We have a very technical product -- we're working hard to abstract the complexity, while making sure we set customers up for success. We've also been overwhelmed with customer demand, and have been scaling the team to keep up (7 FT employees two months ago, 15 now). That said, no excuses. Just know that we're not happy to hear about experiences like yours, and are working hard to make it right.
Thanks, Jason
(EDIT) Aaron and I just had a good phone call -- we're going to make things right.