* Rating (a deep, classical, tabular ML problem) is still catching up to SOTA techniques in many insurance companies + uneven regulatory environment makes it tricky to navigate.
* Marketing is another classical ML problem, the goal is to optimize ad/lead broker spend/conversion funnel.
* CS of course has huge opportunity with LLMs.
* Automated claim fraud detection is a very difficult problem amenable to LLMs/MM DL models. Lots of "pivot to AI" going on here.
And there are others of course. The main thing is that insurance companies have limits on product innovation - policy terms are in many cases mandated by law, so the main differentiator is operational efficiency and exellency. Solutions that move the needle on big revenue/cost levers are very valuable and can be applied widely.
Question: in what ways does insurance domain knowledge set you apart?
I understand certain industries benefit from Engineers with domain experience (e.g. Healthtech companies needing HIPAA knowledge).
I'm curious as to what particular skills insurance companies need from Engineers?