I'm certainly not convinced any ML system can pass a high-stakes, niche-interests version of the Turing test (and since winning people's trust back after losing their money is an emotionally charged thing, probably something akin to Philip K Dick's Voigt Kampf empathy test too). Even if they could, you haven't got a training set because (i) client conversations aren't usually on the record (ii) none of the previous human conversations that could be used if they had been recorded were remotely related to the whys and wherefores of the investment decisions the algorithm actually took (iii) the investment model generated by the ML process probably isn't tractable enough for even its own human designer to convert into words what it's actual strategy was and will be in the next period.

You don't need "any ML system" to be able to not only process the data, but also explain the strategy behind its data processing at various levels of granularity, answer abstract questions and do a convincing impression of listening and responding to client feedback, you need advanced general intelligence.

As for what the more reasonable answer a client would accept is, I don't think I really have the requisite years of investment banking experience to know that...