Difference is that random decision forests learn the rules for themselves, they don't have to be programmed in manually by experts. They're one of the most performant "pre-deep-learning" machine learning models and a sensible baseline for many ML tasks before you go out and buy a $1500 GPU
The ability to program approval rules and understand why decisions have been made (and then tweak the ruleset based on economic/statistical analysis) is a feature for these sort of organizations, not a limitation.
There will be elements of AI which are useful, but ultimately banks will want to know why a certain decision was made, and want to incorporate their own economic calculations and forecasts into the model.
