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>enough to solve this problem

The goal isn't to solve the problem, as in predict with 100% accuracy. The goal is to improve by a few percentage points.

Since categorization is what ML does well and credit histories are great annotated training sets, it seems like a perfect fit.

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A bank in Belarus scrapped ML model that was used to issue consumer loans after a year. Initially the model gave better returns, but after a year it was underperforming. The bank went back to a weighted average of few factors like it was before with humans reviewing the weights. In retrospect this is not surprising. The model tries to predict long-term behaviour, but besides few stable factors like profession, income, education human life still rather unpredictable even after averaging hundreds of thousands cases.

It would definitely seem like ML could improve performance here, if they fed it all the right features.

I feel like outstanding debts (amount, time since last payment) could be really helpful in this regard so people can't rack up huge debts from multiple lenders, but that would also be a privacy nightmare.

I thought credit reporting agencies already provide this data?

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