In Python or R, like the rest of us do. Or SAS, Stata, or even SPSS if they want a familiar interface (although using the SPSS GUI instead of its scripting interface will put them at risk of the same kinds of mistakes as Excel is).
Ensuring reproducibility and robustness isn't rocket science, and it doesn't require the obscurity of Haskell or the verbosity of Java to do properly. But it does require learning to script to program their models, instead of relying on GUI (which should be perfectly natural to analysts and their long Excel formulas).
Edit: To be clear, when I say "scripting" here, I mean text-driven programming, as opposed to using a GUI interface (which is rarely robust or reproducible, and certainly isn't in plain Excel).
Why in the world would Python or R be inadequate for that type of work, if that's what you're implying? What do you think they lack that Excel has, other than a friendly GUI interface? Or SAS, Stata, or SPSS (all of whom have versions that they market heavily toward finance).
Models that take up entire spreadsheets are not models. There is never enough data to validate the sheer number of degrees of freedom that such models contain. There would be things like sub-models of entire divisions of firms. Who even has data that could validate all the potential things that could happen? These spreadsheets are pure ludic fallacy; advisors pretending they know how adding or removing some employees will affect some merger.
Yes, I agree. And I think Excel is an appropriate tool for that job.
Would they be more productive if you took Excel from them?
> I remember a billion dollar merger where the analyst in charge of the "modelling" showed me how they reached the line limit. It's as if making things complicated justified their salaries, so maybe that's why excel is so popular.
How should the analyst write his model in a simple way? As a Java (or Haskell, or whatever firs your ideal of simplicity) program? Or maybe he could simplify the model until he can write it down in a piece of paper.