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>I am not sure that a purely data centric approach with no rules even makes sense. Fraudsters generally follow fairly specific patterns that can definitely be

by rm999 12y ago

>I am not sure that a purely data centric approach with no rules even makes sense. Fraudsters generally follow fairly specific patterns that can definitely be picked up in a rich enough dataset, and the consortium-based approaches of Falcon and Sift allow the models to generalize pretty well. Rules are way less expressive than complex-enough machine learning methods (combined with good data).

bravura 12y ago
Not exactly correct. Rules composed into cascdes or manual decisions trees are not as powerful as ml. But rules can be converted into features, which you use as primitives in an ml model. In fact, one pattern for feature engineering is to build a good rules-based system. You then can treat that as features of a degenerate model, with weights of plus or minus infinity. By training, you can induce better weights. Edit: in a sense, rules and training method are almost orthogonal concerns.
- rm999 12y ago
  > 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.

by rm999 12y ago

bravura 12y ago

Not exactly correct. Rules composed into cascdes or manual decisions trees are not as powerful as ml. But rules can be converted into features, which you use as primitives in an ml model. In fact, one pattern for feature engineering is to build a good rules-based system. You then can treat that as features of a degenerate model, with weights of plus or minus infinity. By training, you can induce better weights. Edit: in a sense, rules and training method are almost orthogonal concerns.

rm999 12y ago
> 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.