> But rules can be converted into features, which you use as primitives in an ml model "Rule" has a very specific meaning in this industry, and it's basically

> But rules can be converted into features, which you use as primitives in an ml model "Rule" has a very specific meaning in this industry, and it's basically a manual decision that exists outside the ML model - usually written by experts. The people who build the models never see the rules (per se), and the rules are integrated after the models are deployed. I wouldn't describe a modeler integrating logic into the modeling process a "rule" because it would confuse terminology. The whole rule-based thing is sort of a testament to how old-fashioned the industry is IMO. The problem is two-fold: banks don't fully trust ML (or the "experts" want to keep their jobs), and they don't tolerate much change in their computing platforms. That last point is pretty important: banks/issuers don't refresh their fraud models very often, so they can miss new types of fraud, which ends up being a great use-case for rules.