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philodendron

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Having worked at a fintech startup specializing in decision systems for regulatory compliance, I second this. There are interesting theoretical aspects stemming from the fact that domain is very tricky to model.

* There is considerable structural sharing inside finance regulations, but it is not hierarchical

* Regulations may be changed at any time, and these changes may be future-dated or back-dated

* Users insist on arbitrarily complex exceptions and overrides

* Nontechnical users (particularly legal) will insist on being able to vet and introspect

In my experience, anyone can do this badly, but to make it scalable and robust requires subtlety and a good understanding of formal logic, ontology and DSL design. It's a good fit for FP. Oh, and there's plenty of room for machine learning: Automated discovery, natural language processing, noncompliance detection, etc.

The problem of turning regulations into a expert system that is robust against future changes has not been solved, and there's demand for such systems from even the most sophisticated financial companies, even though everyone expects they'd be able to build such systems in-house.