The Art of Cutting Corners

I enjoy brewing my own beer. I make small batches in my kitchen that produce a gallon each. Because I already have most of the necessary cookware, all I need to buy are the ingredients, which amount to about $25 dollars per batch.
Sometimes if I’m brewing something intricate, I’ll put in the extra effort and do everything by the book, but that becomes far more time-consuming. But more often, I just throw everything into a nylon sack and cook up the mash. Bada-bing, bada-boom—all done.
Usually the result is excellent. Sometimes it’s good enough. Every so often, it’s terrible. But even if it’s bad, it’s only a gallon, which ends up being less than a 12-pack, so it’s no big deal throwing it out and starting over.
So what does this have to do with financial technology? Glad you asked.
This week, buy-side vendor SimCorp put out a survey where 82 percent of 135 respondents said that they have to create workarounds in the middle and back office to support their derivatives business. They choose this route, rather than consolidating into one system with all of their asset classes under one environment, which would make it easier to model and launch new products, and it would make it more efficient for client reporting.
They feel that even if it goes awry, they’ll only have to throw out a gallon of beer. No big deal.
Workarounds are attempted shortcuts to save on having to put in the time and cost of implementing a new platform that can bring together all these assets under one roof. Now, I think that everyone would agree that having one, true platform is best, but clearly—as this survey would attest to—using different platforms for different assets is just easier, even if not better.
So in this convoluted analogy, my beer making supplies—the jug, thermometer, nylon sack and pots and pans—are the “infrastructure”. The ingredients are the “models” that will help to create a new beer or, in this case, a new “derivatives product”. My cutting corners and just throwing everything together is the “workaround”.
Unlike at hedge funds, my workarounds don’t really help me save money and they don’t add time to the project—actually it’s just the opposite, as it can cost less and it is a lot quicker to make. But I do it mainly because the undertaking that is involved in doing everything by-the-book is significantly heftier.
Now, back to the survey: Eighty-two percent of these respondents are choosing to employ workarounds; 57 percent said that it takes them two or more months to model and launch a new product, when a couple of weeks would be ideal; and 34 percent admitted that because of their workarounds, client reports were compromised. (And that number is likely higher, as some respondents were likely a bit wary of being honest, even on a blind survey.)
What does that tell us? They are a bit concerned about their workarounds and they know that things can go wrong, but they still employ these workarounds because it’s easier and they are willing to take the risk that things will turn out okay. They feel that even if it goes awry, they’ll only have to throw out a gallon of beer. No big deal.
To be sure, the same can be said of firms that use Excel spreadsheets to manage client data rather than investing in an automated system. There’s a reason why firms do this, even if it’s risky. To be honest, I can’t say they’re entirely wrong. Yes, improper risk and data processes and controls have felled many a hedge fund or asset manager, buy sides aren’t collapsing every week because of this, either. They understand that as technology is concerned, just as in their investments, this is a risk/reward industry.
It may not be politically correct to say, and you can be sure that no hedge fund manager would ever admit to this on-the-record, but sometimes workarounds are better—as long as you don’t ever produce a skunked batch that gives everyone food poisoning.
Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.
To access these options, along with all other subscription benefits, please contact info@waterstechnology.com or view our subscription options here: https://subscriptions.waterstechnology.com/subscribe
You are currently unable to print this content. Please contact info@waterstechnology.com to find out more.
You are currently unable to copy this content. Please contact info@waterstechnology.com to find out more.
Copyright Infopro Digital Limited. All rights reserved.
As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (point 2.4), printing is limited to a single copy.
If you would like to purchase additional rights please email info@waterstechnology.com
Copyright Infopro Digital Limited. All rights reserved.
You may share this content using our article tools. As outlined in our terms and conditions, https://www.infopro-digital.com/terms-and-conditions/subscriptions/ (clause 2.4), an Authorised User may only make one copy of the materials for their own personal use. You must also comply with the restrictions in clause 2.5.
If you would like to purchase additional rights please email info@waterstechnology.com
More on Emerging Technologies
BlueMatrix acquires FactSet’s RMS Partners platform
This is the third acquisition BlueMatrix has made this year.
Waters Wavelength Ep. 331: Cresting Wave’s Bill Murphy
Bill Murphy, Blackstone’s former CTO, joins to discuss that much-discussed MIT study on AI projects failing and factors executives should consider as the technology continues to evolves.
FactSet adds MarketAxess CP+ data, LSEG files dismissal, BNY’s new AI lab, and more
The Waters Cooler: Synthetic data for LLM training, Dora confusion, GenAI’s ‘blind spots,’ and our 9/11 remembrance in this week’s news roundup.
Chief investment officers persist with GenAI tools despite ‘blind spots’
Trading heads from JP Morgan, UBS, and M&G Investments explained why their firms were bullish on GenAI, even as “replicability and reproducibility” challenges persist.
Wall Street hesitates on synthetic data as AI push gathers steam
Deutsche Bank and JP Morgan have differing opinions on the use of synthetic data to train LLMs.
A Q&A with H2O’s tech chief on reducing GenAI noise
Timothée Consigny says the key to GenAI experimentation rests in leveraging the expertise of portfolio managers “to curate smaller and more relevant datasets.”
Etrading wins UK bond tape, R3 debuts new lab, TNS buys Radianz, and more
The Waters Cooler: The Swiss release an LLM, overnight trading strays further from reach, and the private markets frenzy continues in this week’s news roundup.
AI fails for many reasons but succeeds for few
Firms hoping to achieve ROI on their AI efforts must focus on data, partnerships, and scale—but a fundamental roadblock remains.