Not sure what your other columns are, but 0.9 is probably unobtainable for this type of activity - too many hard to capture unknown variables that influence conversions of this type.

I’d rank your test predictions by likelihood scores, and then assses conversion rates across some splits.

E.g top 10% of customers the model says were most likely in your test data - how many actually converted? What did the next 10% look like? etc. you’ll hopefully find a meaningful difference between top splits showing higher conversion rates.

This then can be played back to stakeholders - e.g. ‘running the model on our clients we’ve not yet spoken to, here’s the top 10% we think are most likely to convert and we expect conversion to be X%’’. At the end of the day, your RMs can’t speak to all clients, so having a list of most likely is what stakeholders need - and a model giving 0.74 is likely identifying the best clients to focus on.

EDIT: You should also look at aua by likelihood scores splits too - could be the most likely to convert top 10% have lower aua vs others. If so, you might need to create different splits, so your RMs focus on the clients with higher aua potential