Today’s announcement that Fiserv plans to acquire First Data in a $22 billion all stock deal continues the string of payments-related deals. More importantly it stresses what many know – that the incumbent payment companies are not innovation factories. Acquire in the absence of innovation – although I believe all companies should have an active M&A engine that is operated as a line of business.
The venture capital community ought to think extra hard about how their payments portfolio companies are going to differentiate in the fluid payments landscape.
Private equity needs to take a hard look at the incumbents to determine whether they have the chops to be relevant otherwise deal exits will be difficult. In other words, with the payments incumbents you run the risk of catching a falling knife. Thus, it makes sense for incumbent vendors to prop each other up in the meantime. Strategic-to-strategic deals ought to be the norm for the foreseeable future.
We recently published an article: “Multi-Tenant Machine Learning at Scale”. Think of this RPA (“Robotic Process Automation”) post as a follow-up piece as told with pictures. Our perspective is primarily through the lens of an investor as to how we would value each of the three RPA business models. You may access a PDF version of the slides used in this note HERE. Should you wish to receive a copy of our full RPA tools company list (22 companies), contact us at: email@example.com
Our recent RPA-centric CEORater Podcast – episode 247. “Not All RPA Is Valued Equally”
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.
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.
You are a Technology company CEO and/or Board member and the company generates significant free cash flow. You are thinking through your options as to how best to deploy discretionary capital: 1.) M&A: Our advice is go for it. M&A is rarely easy for a variety of reasons but a disciplined approach can result in…
Regular readers know that we are partial toward Technology Founder CEOs vs. hired CEOs as a general rule. Our experience is that founder CEOs are generally better than hired CEOs at anticipating customer market needs, in many cases before customers know they have a need. Founder CEOs care deeply about details of the business that a hired CEO may not give more than a casual glance. Founders in many cases work to leverage their smart senior business leaders whereas hired CEOs may feel threatened by a direct report’s potential. We could go on.
Our CEORater Technology Founder CEO Index has performed well year-to-date through December 6th enjoying a Total Unweighted Return of 14.2% and a Total Weighted Return of 17.1%. The comparable benchmarks returned 5.1% and 4.4% on an Unweighted and Weighted basis respectively. You may access the detailHERE.
Speaking of Technology Founder CEOs, we recently hosted a podcast with SS&C Technologies (ticker: SSNC) founder & CEO Bill Stone where we discussed M&A strategy, SS&C’s decentralized management approach (another core principal of ours) as well as SS&C’s “Singularity” artificial intelligence (“AI”) and machine learning (“ML”) initiative. In the case of SS&C, we believe that AI and ML will drive efficiencies across back office and middle office-related products and services – enabling customers to drive incremental throughput with less effort. Further, AI & ML has the potential to create revenue and EBITDA opportunities for front-office customers (facilitating deal sourcing as an example). You may access the episode below:
Cryptocurrency firms fell from grace (Chain and Lightyear merged to form Interstellar) as valuations and expectations got too far ahead of themselves for both the various “coin” firms as well as for blockchain – the distributed database technology that enables digital currency transactions. We are long-term bullish on each – particularly blockchain.
The crypto space isn’t the only sector that grew overheated as reality has set in to the autonomous vehicle space. Apparently robot cars won’t be shuttling all of humanity to and fro by 2025. We expect that autonomous vehicle startups will suffer a materially adverse impact to their respective valuations.
The two technology hype scenarios got us thinking about previous technology hype cycles. Thus, we created the TEK2day, CEORater “Technology Hype Curve” to visually represent an all too familiar pattern as it relates to hyping new technologies. Click HERE to download the Technology Hype Curve graphic.
The following 7 rules apply to public companies across a variety of industries – particularly to Enterprise Software, FinTech and Information Services companies. 1.) Make Your Numbers 2.) Regular, Transparent Investor Communication 3.) Drive Expanding Operating/EBITDA Margins 4.) Don’t Stockpile Cash 5.) Control Waste 6.) Use Debt as a Tax Shield 7.) Board Composition –…