SS&C Technologies: Multi-Tenant Machine Learning at Scale

SS&C Technologies: Multi-Tenant Machine Learning at Scale

For the past few years I’ve poked around the machine learning (“ML”) and artificial intelligence (“AI”) space. I advised Boston-based DataRobot back in 2014 when they started to build their machine learning platform. I’ve thought about how we at CEORater may leverage ML to score CEOs and companies. Typically when we read about ML and AI it’s from the perspective of a pure-play vendor who markets and licenses its platform across multiple industries for a variety of use cases. Often the use cases we read about are focused on “power users” – people who have a PhD in Statistics or some similar quantitative background.

Recently I had the opportunity to demo SS&C’s (tkr: SSNC), new back-office, middle-office platform (“Singularity“) which has machine-learning, artificial intelligence and robotic process automation (“RPA”) at its core. This was my first opportunity to observe a fintech platform that was built from the ground-up to fully-leverage ML, AI and RPA.

From a background perspective, Asset Management firms of all flavors (small, mid-sized and large, traditional, hedge funds, private equity etc.), Fund Administrators and Insurers use a variety of SS&C products and services to value assets (equity and fixed income securities, derivatives, bank loans, private placements and real assets to name a few asset classes)/ strike an NAV, settle trades and report on asset holdings. The company’s Singularity initiative will replace siloed products with a common ML-based core layer that will have modular AI and RPA services that sit on top.

Multi-tenant machine learning is a significant competitive differentiator. Some readers pride themselves on identifying businesses that have a competitive “moat”. For non-investors a “moat” is a source of sustainable competitive differentiation. Challengers who wish to compete against companies with established moats best be prepared to completely shift the paradigm and render the moat obsolete. You’re simply not going to spend your way around, over or through a moat. Brute force won’t work. If any company ever had a moat, SS&C has one in the world of portfolio accounting systems.

SS&C’s moat is about to get significantly wider and deeper as Singularity is rolled out. This is in no small part due to the multi-tenant machine learning layer. This means that as Customer X has an experience that requires a “learning”, the benefit of that learning is enjoyed not only by Customer X but also by the other customers on the platform. This multi-tenant element to Singularity’s machine learning layer is a powerful scale differentiator primarily for three reasons:

  • Large installed customer base: SS&C has a great many customers and users – therefore more opportunities for machine-driven learnings – the benefits of which accrue to all SS&C Singularity customers.
  • Purpose-built from the ground up: SS&C has incorporated machine learning into Singularity from Day One, providing the company with a significant and sustainable advantage over competitors who may try to retrofit a third-party’s machine learning layer on top of legacy products and services. Retrofitting legacy technology simply can not be as effective from a throughput and efficiency standpoint as a new, modern-architected platform.
  • Cost prohibitive: It’s not an insignificant dollar amount that’s required to build a modern, ML/ AI/ RPA-powered Fintech platform from scratch. To replicate Singularity from a domain-expertise and technology perspective would be cost prohibitive.

VC’s would be wise to avoid trying to disrupt this market. As I see it, the only way to replicate what SS&C has built would be to acquire the company.

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