Well, that's kind of the point: sure, you can get it to function easily, but getting it to actually provide you with solid coverage takes some effort.

>Disclosure: I'm a happy current customer whose model isn't quite right yet, but is getting there.

That pretty much seals my point.

I mean, who wants semi-effective fraud protection? That's where the extra work comes in. And what you will find is that fraudsters change their game and you have to evolve your model, even once you get it customized. So, if you are simply using the "easy-to-use" out of the box signals, you are going to find that fraudsters have already been trained by other Sift customers. Sure, it might take them a moment to realize that you have implemented these measures, but they will adapt quickly when they do. If not, then you are dealing with amateurs and you really don't have much of a fraud problem.

And, that's one of the reasons it surprises me when these services publish fraud signals (as in the subject post), even if it's only a few simple ones. You will find that one of your most effective weapons against fraudsters is secrecy and misdirection. Never "train" them by letting on how you are trying to detect them and never give immediate feedback when you bust them. For instance (and I will give only a couple of simple examples), make them believe there is a problem with the site or that their order is on the way (just don't send it). The longer it takes for them to realize that their game isn't working, the better. And the more confused they are about whether it actually is working, the better. You want them confused, frustrated, and ready to move on to an easier target.

Again, my point is that for anyone who has a real fraud problem and needs a service like this, devising an *effective" strategy for using it is not simple. Instead of simplicity, Sift should advertise that fraud is tough, but they are tougher. They should talk up their models, machine learning, etc., but also their experience in and commitment to understanding customers and leading them to an effective use of their tools and mitigation strategies. This stuff isn't turnkey. It is very much a customer service and consulting business, whether they would prefer it to be pure self-serve SaaS or not.

In any event, good luck.

That is exactly why I want a machine learning model, so that it can constantly be modifying and improving itself. Even better that it has a network of many clients, so that it can see lots of fraud examples and IP addresses where fraud is happening in real time.

> you are going to find that fraudsters have already been trained by other Sift customers.

The opposite should be true: fraud attempts on other Sift customers have helped improve your fraud protection.

> who wants semi-effective fraud protection?

If I can get immediate marginal improvement inexpensively with a probability that the fraud protection becomes very effective, then yeah...sounds good. Sign me up.