[Guest42]

I’ve worked in credit risk modeling and it is rather strict the predictors that can be used and well documented. Data comes in from a variety of sources and it is favorable to be skilled in established models than to try something obscure that isn’t intuitive. The models have to work across different sets of time and the varying business processes that may have been in place. Fraud modeling is more flexible, but seemed to have fairly similar results although more trendy things like random forests and neural nets would show up.

[tadkar]

That is definitely changing in credit risk and underwriting as well. There are several companies like [1] applying deep neural nets to the credit risk problem. This on top of a lot of in house work in the big banks to “supplement” what is available on the open market

[1] https://zest.ai/

[lcrmorin]

There is a big difference between dumping a dataset in the latest hot ML model and building something that offer some actual explainability, deal with intrinsic biases, have some stability over time and recalibration and that will go trough an unprecedented crisis for which you don't have any data to learn from. That mean the model usually has to go trough a lot of internal commities and different external agencies. I highly suspect that an external proprietary solution won't go very far in the credit rating field.

[112012123]

This is definitely a super interesting company/approach - thanks for the link! I'm definitely curious as to whether they're actually using things like neural nets (or any other more-sophisticated technical techniques). The traditional problem is that those models aren't explainable, and potentially have hidden biases in them, so I'd be really curious what their approach is.

[Guest42]

Do you work for them? What is your background in academia/business?