ML is about creating a process that produces models from data in a way that is likely to generalize to new data. For example if you just start trying lots of r

ML is about creating a process that produces models from data in a way that is likely to generalize to new data. For example if you just start trying lots of rules by hand on your fraud data set there is a good chance you'll come up with a rule that looks good on your data but doesn't generalize to new data. The number of models (or rules or formulas) you try by hand increases this chance (this related to multiple testing) and worse the process that generates them isn't repeatable since one you know a feature worked on the data set you're biased towards finding it again. So in ML you try to come up with a model generating process that is entirely automated and repeatable. This means you can do it repeatedly in cross validation, over bootstraps, out of time etc on the same data set and be fairly sure that it will generalize. The goal can still be a simple rule or formula but you achieve this simplicity for simplicity by penalizing complexity (as in lasso) or doing explicit simplification (as in pruning). The reason that complex "black box" models are so popular is that they often have really nice statistical properties in terms of generalization. It's fairly intuitive that averaging over a bunch of slightelly perterbed simple models will give you a nice combined model as in a random forest, gbm or other ensemble. Deep neural networks are less intuitive but it's been hypothesized that the depth makes them less prone to overfitting than a shallow network. A shallow network will have one global optimal set of weights that's simple and really good but it also has lots of locally optimal states that aren't as good...and the chances are high your training procedure (or manual search for a simple model that works) will get caught in one of these. It's been shown that the for deeper networks these local optima tend to be much closer in performance to the global optima so in effect making the model more complex makes it less likely you'll end up with a bad model.