Why is the tech in our industry so terrible?
New to IB, was anyone else surprised by how bad the tech was when they got into banking? I knew it was going to be all excel and powerpoint but I don't think I realized how big the models were that the industry manages in excel.
I work in Infra so everything is one-off and build from scratch. Lots of debt structuring. But I'm literally up half the night just waiting for 30 scenario models to solve and loading results into ppt...
Are other industry groups at BBs like this? Is PE as bad? Looking for hope. Thanks.
yes it's like that everywhere in PE, IBD etc
feel free to come up with something better, you might become wealthier than bloomberg
It is crazy to think that the world economy is literally running on Microsoft Excel 2016
Driver of revenue is MDs, and bad tech does not impact their ability to do their job, just juniors. Considering it costs seniors nothing to have you work later, they’d rather extract more labor from you at a marginal cost of $0 than invest in better tech
100% correct - the other piece is inertia / risk appetite at banks. Look at the fucking Revlon debacle, those systems were 20 years old and being held together by bandaids.
Better tech exists, but if the existing system is fine, no one will take the risk to implement a new one - especially if the only people affected (apart from massive one-off fuck ups) are the junior staff.
At least we got to retire Blackberry and switch over to Outlook Mobile last summer. Praise be.
(I know this sounds like 2002, but trust me it was 2022)
Ability to audit and customize are the major drivers of using excel.
Business models, terms always end up being situation specific. Building something templates would 1) be an insane effort, 2) end up in a black box with no visibility on the workings.
A simple example: I looked recently at a sector specific data provider who came up with a new module that had revenue forecast for products, including assets where there is no consensus.
Immediate reaction “this is great, that’s really going to help us”
Then lifting the hood: it’s algo based, using analogs in the sector with factor based tweaks.
The result could be great, unfortunately it’s pointless:
So easy answer: the tool is completely useless from a workflow perspective, even if the outcome was better than our current framework. Unless you have that ability to translate the outputs from the algo into actionnable datapoints (e.g. for a commercial strategy) that can be diligenced, it’s good for nothing.
Bloomberg, factset, capitaliq all try to provide templated solutions. They’re all pretty useless as things are always a bit more nuanced.
Presumably it’s a regulatory thing. The systems in place already work and it would take serious risk/work to replace them
There isn’t any government regulation on it, but on-boarding a new tech stack in a major bank is a lift and a half
Job security
As posters above have said - the primary reasons for still using Excel models in 2023 are transparency/ease of reviewing and the fact that others are familiar with it. Ultimately it comes down to the fact that Excel is good enough to do any type of IB/PE modelling you want, and there would be no benefit from upgrading to something more complex.
Don't believe me? Ok well let's take something like Python or R - during my years in PE we've had various tech "startups" (usually a one-man band consisting of a single ex-IB guy) as well as consultants e.g. McKinsey pitch us on using these as a replacement to Excel, in addition to various proprietary solutions (i.e. models built by a startup for us to use). Now yes it's true that Python is far better/quicker at calculations than Excel, and also there are tons of clever machine-learning algorithms you can use almost off the shelf (all available open-source). But what is the actual benefit? Basically you can create something that spits out results much quicker.
But now here are the downsides:
a) Difficulty in reviewing - everyone in finance knows how to use Excel to an advanced-level (at least in FO roles). So you can create a model and send it to PE firms, banks, accountancy firms/consultants, hell even lawyers (although they usually struggle to do much beyond opening the file) - and almost certainly everyone will firstly have the software to open it and more importantly be able to dive in and explore the logic/formulas driving the calculations. Now take something more complex like Python - it's the complete opposite. Even if the output is 100% correct/accurate, how is anyone going to know when they can't even open the model let alone understand it? Sure they can get the software and you can walk them through it - but that's insanely time-consuming, and not something any client would want to do. In fact almost all will say "just send me the excel version"!
b) Lack of transparency when using complex calculations - let's say you have some model that uses some algorithm/calculation that is 10x more accurate than in Excel. Again, how is anyone going to test/review this, or make changes to it? Unless you have a background in machine-learning and Python you'll have no idea, and neither will any client or counterparty.
c) Ultimately a lack of any benefit - bear in mind this is IB and PE we're talking about, which means the models are always wrong. Now I don't mean that they're incorrect or complete junk, but rather in almost any transaction there are so many variables (macro-economic, company-specific, industry-specific, black swan event like Covid etc) that the model output vs reality will always be somewhat off. A model in IB/PE is a rough guide and nothing more - it can be a very important rough guide, but that's all it is ultimately. Now of course in some sectors the models can be more complex, but that doesn't make them more accurate necessarily - and tbh in many cases a lot of these models are "over-engineered" by which I mean there are so many drivers/inputs that it gives the illusion of certainty/precision but in fact is barely more accurate than a high-level DCF. So what is more complex software like Python/R/proprietary model going to achieve here? As ultimately even the smartest algorithms can only work with the data provided to them, so tbh this would just be another example of over-engineering/giving an illusion of precision that subsequently doesn't play out.
Now of course in other areas like S&T or quantitative hedge funds, quants likely will be using much more advanced stuff than Excel e.g. Python/R and machine-learning techniques. But that's a completely different ballgame - those models are strictly for internal use i.e. trading and aren't being shared with counterparties, and also are supposed to be much more precise than some IB/PE excel model. But for IB/PE there would be no advantage to going beyond Excel, so the most advanced you'll likely ever get is using VBA (which in fairness can actually be very useful in automating/streamlining a lot of calcs in Excel, and in my experience is used extensively in the finance world especially in the more "advanced" Excel models).
Excel and PPT are built by Microsoft, not by GS internal tech teams. Microsoft doesn’t optimize for latency because majority of Microsoft users don’t care about it. There’re some trading teams in GS that has dedicated tech teams who build high-end stuff like trading systems. But the same thing isn’t justified for banking because it’s not worth it to fund an entire tech team just for reduced PPT loading time
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