Those of you who have been longtime readers of WatersTechnology may remember a bit of a craze years ago. It was when buy-side firms were actively examining the investment book of record (Ibor) and, well, we were all over it like a cheap suit.
We’ve been comparatively restrained on blockchain. That’s not because we don’t think it’s important—clearly, dozens of banks can never be wrong about something, right? It’s because we feel that there’s a tendency to label blockchain as being all things to all people.
With Ibor, though, there could be a real use case brewing. Most solid movements within the blockchain world have focused primarily on two areas—asset transfer, be that currency, other materials, or information in underserved markets such as small-to-medium enterprise segments, or secondly, on the post-trade front.
But when I first started learning about blockchain—and I’ll admit to being late to the party on this, it was only when the financial services industry started sitting up and taking notice—one of the things that struck me was how similar the terminology was between Ibor and blockchain.
The basic description of an Ibor system is that it’s a platform designed to provide the front, middle and back offices of a financial institution with a single source of the truth. A blockchain system is designed as a distributed ledger where all nodes are in agreement over a single version of the truth.
It’s not too dissimilar, on a theoretical basis. There are major differences, of course. Ibor systems, particularly those currently in production by companies including SimCorp with its Dimension platform, or BlackRock with Aladdin—itself a descendant of one of the original Ibor systems that was built at Barclays Global Investors—are advanced technologies, feeding inputs from all areas of a business to create an interoperable database where all stakeholders can access particular views of information, confident that it is consistent across those views.
Then there are enhanced analytics layers, compatibility with multiple other systems, and its role as a nervous system for all areas of the business. It’s a pure data architecture project like few others.
Blockchain will take some development in order to be appropriate for this function, but an Ibor system built on blockchain architecture could, in theory, be a particularly powerful platform.
I’ll have a wider piece out on this next week, but feel free to shoot me your thoughts. I can be reached on [email protected].
This week on Buy-Side Technology:
- Speaking of things that have far too much hype, my colleague Anthony Malakian takes a long look at artificial intelligence in this great piece, bringing in insights from Brown Brothers Harriman and others.
- Euronext and LCH appear to have kissed and made up after the post-Deutsche Börse merger fallout, when let’s be honest, hardly anyone was at their best. Aside from me. I was on a Federally-mandated holiday at the time (not prison).
- Fidelity announced its read-only update to its portfolio view, allowing Coinbase customers to view their bitcoin, ether and litecoin holdings as part of their overall assets, which we reported on last month, and again this week.
- The Chicago Mercantile Exchange put the final nail in the coffin for its European clearinghouse, which further proves, along with the aforementioned London Stock Exchange-Deutsche Börse snafu, that Europe doesn’t much care for derivatives-market competition.
- Finally, while we’re talking about derivatives, guess what might be in the European trading obligation’s future? That’s right, delays, if Isda et al get their way.
Also: Trading Technologies is developing an OMS for the sell side and Orbital Insight is embracing a platform-as-a-service model.Subscribe to Weekly Wrap emails
- Wavelength Podcast Episode 142: AWS Talks Cloud Adoption in the Capital Markets
- Alt Data’s Ethical Day of Reckoning
- Wavelength Podcast Episode 141: Brexit and Blockchain and Data, Oh My
- Data-Driven Regulators: Handling the Uptick in Regulatory Reporting
- Evolve or Die: Asset Managers Cultivate Data Science Teams