How will developments in AI change the landscape in investment banking at the junior level? Will there be a reduction in analyst class sizes or just less hours to work? Interested as someone considering careers in business.
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Too early to say. Assuming you're in college already, you are fine - banks have essentially banned AI right now, except for small groups working on code, as they figure out how to use it within the bank and with strict data/privacy controls. We'll likely see some efficiency tools within the next few years but analysts are not going away anytime soon.
Jobs are not going to disappear altogether, they'll just be doing something different. Bankers still worked long, hard hours in the 1980s when Excel and PowerPoint didn't exist. Those tools didn't replace the bankers, they just started doing things differently.
What kind of coverage groups would work-directly on code?
No one in IB. I'm talking about engineers elsewhere in the bank who are specifically working on AI and how to integrate it into the broader technology effort of the bank.
Relatively little for the short term. Had a discussion about this with my team, seniors are generally averse to implementing AI for the time being. But think that it will definitely help. Lots of the repetitive IB stuff can be made faster. Few slides are created new, usually you just pull precedents and iterate off of that, so first drafts of decks will be able to be quickly made through AI. Ideal use is if I can throw some info at my computer and it can create a CIM draft. I think modelling might be in the cards as well, but so much of modelling is already using macros, templates etc. An interesting tool would be a better DD helper, I'd love a tool that tells a buyer when their question can be answered from a file thats been sitting in the dataroom for weeks.
One concern is the data, need a secure model as we are dealing with clients info.
Hi OP - agree with the other comments here, i.e. very little will change in the short/medium-term (so likely at least the next 5-10 years). There's a few reasons for this:
- IB is not at the cutting-edge of technology - basically the majority of IB/PE is done in Microsoft Excel, and has been for the past 25 years - and whilst Excel is no doubt a powerful tool, it's fair to say there is analytical/data software out there that is far superior from a purely technical perspective. There's a very good reason for that however - it simply isn't needed.
Excel is the perfect tool for finance - it's user-friendly i.e. you don't need coding knowledge to use it unlike Python, it's very transparent (easy to follow/trace formulas to understand calcs), it is very flexible/easy to update, is universally used in the industry so others easily understand it (and are able to open the files without needing other software!) and most importantly it's already sufficient for the work required. With coding knowledge you could build a DCF model in Python which would probably be "superior" in that it could handle far more variables/scenarios than Excel ever could - but the thing is this just isn't needed unless you were literally running thousands of iterations/scenarios, which would be complete overkill for IB. Likewise any AI/machine-learning techniques you could use in software like Python/R wouldn't really add any value to an IB analysis (more on that below)
- IB is nearly as qualitative as it is quantitative - by this I mean that whilst modeling is important, in IB/PE it is really just a tool to validate an already-existing idea. E.g. a senior banker thinks that company x should be worth y in this scenario based on his industry knowledge - you then need to model that out in Excel and see how it looks/if it makes sense. But the model itself is almost never driving the original investment/valuation hypothesis. So for instance if you gave a banking valuation exercise to a very smart data scientist with no industry/IB knowledge and asked them to come to a valuation for a company based purely on what was in the data room - I guarantee nobody would take it seriously, regardless of whatever cutting-edge machine-learning algorithms they used. As there are so many qualitative factors that go into IB/PE valuation/investment theses that it's impossible to do it purely on the numbers alone.
- IB is not easy to automate - so above we've made the case as to why AI currently wouldn't add any value in improving the analysis in IB, but what about automating an analyst's work? The issue here is so much of IB is very "manual" - for instance you'll get garbage data from a client that needs to be cleaned up, or wider industry data isn't easily available in the public domain so your team has to come up with some assumption based on industry knowledge to fill the gaps. Basically there isn't any way you could just feed in some files from a data room + public data into an AI program and get a great model or presentation, at least not one that a client would likely be impressed by. And that brings us onto another point - the cost of an error in IB is so high, either in terms of legal liability during a deal, financial risk in terms of the client pulling out/not wanting to pay their fee due to an egregious error, or even just embarrassing yourself in an initial pitch deck which has an obvious error that a human would have spotted. And analysts are cheap to the bank in the context of deal fees - so it makes sense just to pay a body in a seat to do the work and check it's 100% correct (and scream at them if it isn't lol).
I would say that maybe Powerpoint presentations could be streamlined somewhat by software, e.g. creating templates which regurgitate the same stock stuff about the firm that goes into slides in every presentation. But then that's not AI, that's just a pre-existing PPT template - and those have been around forever lol.
So yeah in summary OP, I really wouldn't worry about IB/PE being automated away anytime soon! I've never worked in the hedge fund or trading space (in PE now) but I imagine those are areas where AI could potentially become much more impactful. As in contrast to IB those fields involve working with massive datasets where intensive machine-learning analysis would have the ability to possibly provide a significant advantage. Having said that though, quant funds that use cutting-edge technology have been around for a decade-plus and all it's meant is that data scientists and computer science PhDs are very much in demand now - there will still be a place for regular analysts for the foreseeable future (given that not all funds are Renaissance Technologies).
Got it, thanks for the response.
Would it be possible that the number of analysts will be reduced as the role analysts are doing is more geared towards proofreading AI's work (rather than pasting slides together themselves)? I understand that the jobs will not be copmletely eliminated due to the qualitative aspects, and there will still be jobs at the junior level, but how much could AI conceivably change?
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