Personal blog of Yzmir Ramirez

The world of consumer banking has seen a massive shift in the last ten years. Gone are the days where you could open an account, take out a loan, or discuss changing the terms of your banking only by visiting a physical branch. Now, you can do all this and more with a few quick taps on your phone screen — a shift that has accelerated with customers expecting and demanding even faster and more responsive banking services.

As one mark of that switch, today a startup called Thought Machine, which has built cloud-based technology that powers this new generation of services on behalf of both old and new banks, is announcing some significant funding — $83 million — a Series B that the company plans to use to continue investing in its platform and growing its customer base.

To date, Thought Machine’s customers are primarily in Europe and Asia — they include large, legacy outfits like Standard Chartered, Lloyds Banking Group, and Sweden’s SEB through to “challenger” (AKA neo-) banks like Atom Bank. Some of this financing will go towards boosting the startup’s activities in the US, including opening an office in the country later this year and moving ahead with commercial deals.

The funding is being led by Draper Esprit, with participation also from existing investors Lloyds Banking Group, IQ Capital, Backed and Playfair.

Thought Machine, which started in 2014 and now employs 300, is not disclosing its valuation but Paul Taylor, the CEO and founder, noted that the market cap is currently “increasing healthily.” In its last round, according to PitchBook estimates, the company was valued at around $143 million, which, at this stage of funding, puts this latest round potentially in the range of between $220 million and $320 million.

Thought Machine is not yet profitable, mainly because it is in growth mode, said Taylor. Of note, the startup has been through one major bankruptcy restructuring, although it appears that this was mainly for organisational purposes: all assets, employees and customers from one business controlled by Taylor were acquired by another.

Thought Machine’s primary product and technology is called Vault, a platform that contains a range of banking services: checking accounts, savings accounts, loans, credit cards and mortgages. Thought Machine does not sell directly to consumers, but sells by way of a B2B2C model.

The services are provisioned by way of smart contracts, which allows Thought Machine and its banking customers to personalise, vary and segment the terms for each bank — and potentially for each customer of the bank.

Food for Thought (Machine)

It’s a little odd to think that there is an active market for banking services that are not built and owned by the banks themselves. After all, aren’t these the core of what banks are supposed to do?

But one way to think about it is in the context of eating out. Restaurants’ kitchens will often make in-house what they sell and serve. But in some cases, when it makes sense, even the best places will buy in (and subsequently sell) food that was crafted elsewhere. For example, a restaurant will re-sell cheese or charcuterie, and the wine is likely to come from somewhere else, too.

The same is the case for banks, whose “Crown Jewels” are in fact not the mechanics of their banking services, but their customer service, their customer lists, and their deposits. Better banking services (which may not have been built “in-house”) are key to growing these other three.

“There are all sorts of banks, and they are all trying to find niches,” said Taylor. Indeed, the startup is not the only one chasing that business. Others include Mambu, Temenos and Italy’s Edera.

In the case of the legacy banks that work with the startup, the idea is that these behemoths can migrate into the next generation of consumer banking services and banking infrastructure by cherry-picking services from the VaultOS platform.

“Banks have not kept up and are marooned on their own tech, and as each year goes by, it comes more problematic,” noted Taylor.

In the case of neobanks, Thought Machine’s pitch is that it has already built the rails to run a banking service, so a startup — “new challengers like Monzo and Revolut that are creating quite a lot of disruption in the market” (and are growing very quickly as a result) — can integrate into these to get off the ground more quickly and handle scaling with less complexity (and lower costs).

Money talks

Taylor was new to fintech when he founded Thought Machine, but he has a notable track record in the world of tech that you could argue played a big role in his subsequent foray into banking.

Formerly an academic specialising in linguistics and engineering, his first startup, Rhetorical Systems, commercialised some of his early speech-to-text research and was later sold to Nuance in 2004.

His second entrepreneurial effort, Phonetic Arts, was another speech startup, aimed at tech that could be used in gaming interactions. In 2010, Google approached the startup to see if it wanted to work on a new speech-to-text service it was building. It ended up acquiring Phonetic Arts, and Taylor took on the role of building and launching Google Now, with that voice tech eventually making its way to Google Maps, accessibility services, the Google Assistant and other places where you speech-based interaction makes an appearance in Google products.

While he was working for years in the field, the step changes that really accelerated voice recognition and speech technology, Taylor said, were the rapid increases in computing power and data networks that “took us over the edge” in terms of what a machine could do, specifically in the cloud.

And those are the same forces, in fact, that led to consumers being able to run our banking services from smartphone apps, and for us to want and expect more personalised services overall. Taylor’s move into building and offering a platform-based service to address the need for multiple third-party banking services follows from that, and also is the natural heir to the platform model you could argue Google and other tech companies have perfected over the years.

Draper Esprit has to date built up a strong portfolio of fintech startups that includes Revolut, N26, TransferWise and Freetrade. Thought Machine’s platform approach is an obvious complement to that list. (Taylor did not disclose if any of those companies are already customers of Thought Machine’s, but if they are not, this investment could be a good way of building inroads.)

“We are delighted to be partnering with Thought Machine in this phase of their growth,” said Vinoth Jayakumar, Investment Director, Draper Esprit, in a statement. “Our investments in Revolut and N26 demonstrate how banking is undergoing a once in a generation transformation in the technology it uses and the benefit it confers to the customers of the bank. We continue to invest in our thesis of the technology layer that forms the backbone of banking. Thought Machine stands out by way of the strength of its engineering capability, and is unique in being the only company in the banking technology space that has developed a platform capable of hosting and migrating international Tier 1 banks. This allows innovative banks to expand beyond digital retail propositions to being able to run every function and type of financial transaction in the cloud.”

“We first backed Thought Machine at seed stage in 2016 and have seen it grow from a startup to a 300-person strong global scale-up with a global customer base and potential to become one of the most valuable European fintech companies,” said Max Bautin, Founding Partner of IQ Capital, in a statement. “I am delighted to continue to support Paul and the team on this journey, with an additional £15 million investment from our £100 million Growth Fund, aimed at our venture portfolio outperformers.”

One of the bigger trends in enterprise software has been the emergence of startups building tools to make the benefits of artificial intelligence technology more accessible to non-tech companies. Today, one that has built a platform to apply the power of machine learning and natural language processing to massive documents of unstructured data has closed a round of funding as it finds strong demand for its approach.

Eigen Technologies, a London-based startup whose machine learning engine helps banks and other businesses that need to extract information and insights from large and complex documents like contracts, is today announcing that it has raised $37 million in funding, a Series B that values the company at around $150 million – $180 million.

The round was led by Lakestar and Dawn Capital, with Temasek and Goldman Sachs Growth Equity (which co-led its Series A) also participating. Eigen has now raised $55 million in total.

Eigen today is working primarily in the financial sector — its offices are smack in the middle of The City, London’s financial center — but the plan is to use the funding to continue expanding the scope of the platform to cover other verticals such as insurance and healthcare, two other big areas that deal in large, wordy documentation that is often inconsistent in how its presented, full of essential fine print, and typically a strain on an organisation’s resources to be handled correctly — and is often a disaster if it is not.

The focus up to now on banks and other financial businesses has had a lot of traction. It says its customer base now includes 25% of the world’s G-SIB institutions (that is, the world’s biggest banks), along with others that work closely with them, like Allen & Overy and Deloitte. Since June 2018 (when it closed its Series A round), Eigen has seen recurring revenues grow sixfold with headcount — mostly data scientists and engineers — double. While Eigen doesn’t disclose specific financials, you can see the growth direction that contributed to the company’s valuation.

The basic idea behind Eigen is that it focuses what co-founder and CEO Lewis Liu describes as “small data.” The company has devised a way to “teach” an AI to read a specific kind of document — say, a loan contract — by looking at a couple of examples and training on these. The whole process is relatively easy to do for a non-technical person: you figure out what you want to look for and analyse, find the examples using basic search in two or three documents and create the template, which can then be used across hundreds or thousands of the same kind of documents (in this case, a loan contract).

Eigen’s work is notable for two reasons. First, typically machine learning and training and AI requires hundreds, thousands, tens of thousands of examples to “teach” a system before it can make decisions that you hope will mimic those of a human. Eigen requires a couple of examples (hence the “small data” approach).

Second, an industry like finance has many pieces of sensitive data (either because it’s personal data, or because it’s proprietary to a company and its business), and so there is an ongoing issue of working with AI companies that want to “anonymise” and ingest that data. Companies simply don’t want to do that. Eigen’s system essentially only works on what a company provides, and that stays with the company.

Eigen was founded in 2014 by Dr. Lewis Z. Liu (CEO) and Jonathan Feuer (a managing partner at CVC Capital Partners, who is the company’s chairman), but its earliest origins go back 15 years earlier, when Liu — a first-generation immigrant who grew up in the U.S. — was working as a “data-entry monkey” (his words) at a tire manufacturing plant in New Jersey, where he lived, ahead of starting university at Harvard.

A natural computing whiz who found himself building his own games when his parents refused to buy him a games console, he figured out that the many pages of printouts he was reading and re-entering into a different computing system could be sped up with a computer program linking up the two. “I put myself out of a job,” he joked.

His educational life epitomises the kind of lateral thinking that often produces the most interesting ideas. Liu went on to Harvard to study not computer science, but physics and art. Doing a double major required working on a thesis that merged the two disciplines together, and Liu built “electrodynamic equations that composed graphical structures on the fly” — basically generating art using algorithms — which he then turned into a “Turing test” to see if people could detect pixelated actual work with that of his program. Distill this, and Liu was still thinking about patterns in analog material that could be re-created using math.

Then came years at McKinsey in London (how he arrived on these shores) during the financial crisis where the results of people either intentionally or mistakenly overlooking crucial text-based data produced stark and catastrophic results. “I would say the problem that we eventually started to solve for at Eigen became tangible,” Liu said.

Then came a physics PhD at Oxford where Liu worked on X-ray lasers that could be used to decrease the complexity and cost of making microchips, cancer treatments and other applications.

While Eigen doesn’t actually use lasers, some of the mathematical equations that Liu came up with for these have also become a part of Eigen’s approach.

“The whole idea [for my PhD] was, ‘how do we make this cheaper and more scalable?,’ ” he said. “We built a new class of X-ray laser apparatus, and we realised the same equations could be used in pattern matching algorithms, specifically around sequential patterns. And out of that, and my existing corporate relationships, that’s how Eigen started.”

Five years on, Eigen has added a lot more into the platform beyond what came from Liu’s original ideas. There are more data scientists and engineers building the engine around the basic idea, and customising it to work with more sectors beyond finance. 

There are a number of AI companies building tools for non-technical business end-users, and one of the areas that comes close to what Eigen is doing is robotic process automation, or RPA. Liu notes that while this is an important area, it’s more about reading forms more readily and providing insights to those. The focus of Eigen is more on unstructured data, and the ability to parse it quickly and securely using just a few samples.

Liu points to companies like IBM (with Watson) as general competitors, while startups like Luminance is another taking a similar approach to Eigen by addressing the issue of parsing unstructured data in a specific sector (in its case, currently, the legal profession).

Stephen Nundy, a partner and the CTO of Lakestar, said that he first came into contact with Eigen when he was at Goldman Sachs, where he was a managing director overseeing technology, and the bank engaged it for work.

“To see what these guys can deliver, it’s to be applauded,” he said. “They’re not just picking out names and addresses. We’re talking deep, semantic understanding. Other vendors are trying to be everything to everybody, but Eigen has found market fit in financial services use cases, and it stands up against the competition. You can see when a winner is breaking away from the pack and it’s a great signal for the future.”

When you watch a commercial for one of the major stock exchanges, you are welcomed into a world of fast-moving, slick images full of glistening buildings, lush crops and happy people. They are typically interspersed with shots of intrepid executives veering out over the horizon as if to say, “I’ve got a long-term vision, and the exchange where my stock is listed is a valuable partner in achieving my goals.” It’s all very reassuring and stylish. But there’s another side to the story.

I have been educated about the realities of today’s stock exchange universe through recent visits with Brad Katsuyama, co-founder and CEO of IEX (a.k.a. The Investors Exchange). If Katsuyama’s name rings a bell, and you don’t work on Wall Street, it’s likely because you remember him as the protagonist of Michael Lewis’s 2014 best-seller, Flash Boys: A Wall Street Revolt, which explored high-frequency trading (HFT) and made the case that the stock market was rigged, really badly.

Five years later, some of the worst practices Lewis highlighted are things of the past, and there are several attributes of the American equity markets that are widely admired around the world. In many ways, though, the realities of stock trading have gotten more unseemly, thanks to sophisticated trading technologies (e.g., microwave radio transmissions that can carry information at almost the speed of light), and pitched battles among the exchanges, investors and regulators over issues including the rebates stock exchanges pay to attract investors’ orders and the price of market data charged by the exchanges.

I don’t claim to be an expert on the inner workings of the stock market, but I do know this: Likening the life cycle of a trade to sausage-making is an insult to kielbasa. More than ever, trading is an arcane, highly technical and bewildering part of our broader economic infrastructure, which is just the way many industry participants like it: Nothing to see here, folks.

Meanwhile, Katsuyama, company president Ronan Ryan and the IEX team have turned IEX into the eighth largest stock exchange company, globally, by notional value traded, and have transformed the concept of a “speed bump” into a mainstream exchange feature.

Brad Katsuyama. Image by Joshua Blackburn via IEX Trading

Despite these and other accomplishments, IEX finds itself in the middle of a vicious battle with powerful incumbents that seem increasingly emboldened to use their muscle in Washington, D.C. What’s more, new entrants, such as The Long-Term Stock Exchange and Members Exchange, are gearing up to enter the fray in US equities, while global exchanges such as the Hong Kong Stock Exchange seek to bulk up by making audacious moves like attempting to acquire the venerable London Stock Exchange.

As my discussion with Katsuyama reveals, IEX may have taken some punches in carving out a position for itself in this high-stakes war characterized by cutting-edge technology and size. However, the IEX team remains girded for battle and confident that it can continue to make headway in offering a fair and transparent option for market participants over the long term.

Gregg Schoenberg: Given Flash Boys and the attention it generated for you on Main Street, I’d like to establish something upfront. Does IEX exist for the asset manager, the individual, or both?

Brad Katsuyama: We exist primarily for the asset manager, and helping them helps the individual. We’re one step removed from the individual, and part of that is due to regulation. Only brokers can connect to exchanges, and the asset manager connects to the broker.

Schoenberg: To put a finer point on it, you believe in fairness and being the good guy. But you are not Robinhood. You are a capitalist.

Katsuyama: Yes, but we want to make money fairly. Actually, we thought initially about starting the business as a nonprofit, But once we laid out all the people we would need to convince to work for us, we realized it would’ve been hard for us to attract the skill sets needed as a nonprofit.

Schoenberg: Do you believe that the US equity market today primarily serves investors or traders?

In the years following the financial crisis, de novo bank activity in the US slowed to a trickle. But as memories fade, the economy expands and the potential of tech-powered financial services marches forward, entrepreneurs have once again been asking the question, “Should I start a bank?”

And by bank, I’m not referring to a neobank, which sits on top of a bank, or a fintech startup that offers an interesting banking-like service of one kind or another. I mean a bank bank.

One of those entrepreneurs is Judith Erwin, a well-known business banking executive who was part of the founding team at Square 1 Bank, which was bought in 2015. Fast forward a few years and Erwin is back, this time as CEO of the cleverly named Grasshopper Bank in New York.

With over $130 million in capital raised from investors including Patriot Financial and T. Rowe Price Associates, Grasshopper has a notable amount of heft for a banking newbie. But as Erwin and her team seek to build share in the innovation banking market, she knows that she’ll need the capital as she navigates a hotly contested niche that has benefited from a robust start-up and venture capital environment.

Gregg Schoenberg: Good to see you, Judith. To jump right in, in my opinion, you were a key part of one of the most successful de novo banks in quite some time. You were responsible for VC relationships there, right?

Judith Erwin: The VC relationships and the products and services managing the balance sheet around deposits. Those were my two primary roles, but my background is one where people give me broken things, I fix them and give them back.

Schoenberg: Square 1 was purchased for about 22 times earnings and 260% of tangible book, correct?

Erwin: Sounds accurate.

Schoenberg: Plus, the bank had a phenomenal earnings trajectory. Meanwhile, PacWest, which acquired you, was a “perfectly nice bank.” Would that be a fair characterization?

Erwin: Yes.

Schoenberg: Is part of the motivation to start Grasshopper to continue on a journey that maybe ended a little bit prematurely last time?

Erwin: That’s a great insight, and I did feel like we had sold too soon. It was a great deal for the investors — which included me — and so I understood it. But absolutely, a lot of what we’re working to do here are things I had hoped to do at Square 1.

Image via Getty Images / Classen Rafael / EyeEm

Schoenberg: You’re obviously aware of the 800-pound gorilla in the room in the form of Silicon Valley Bank . You’ve also got the megabanks that play in the segment, as well as Signature Bank, First Republic, Bridge Bank and others.

Technology has been used to manage regulatory risk since the advent of the ledger book (or the Bloomberg terminal, depending on your reference point). However, the cost-consciousness internalized by banks during the 2008 financial crisis combined with more robust methods of analyzing large datasets has spurred innovation and increased efficiency by automating tasks that previously required manual reviews and other labor-intensive efforts.

So even if RegTech wasn’t born during the financial crisis, it was probably old enough to drive a car by 2008. The intervening 11 years have seen RegTech’s scope and influence grow.

RegTech startups targeting financial services, or FinServ for short, require very different growth strategies — even compared to other enterprise software companies. From a practical perspective, everything from the security requirements influencing software architecture and development to the sales process are substantially different for FinServ RegTechs.

The most successful RegTechs are those that draw on expertise from security-minded engineers, FinServ-savvy sales staff as well as legal and compliance professionals from the industry. FinServ RegTechs have emerged in a number of areas due to the increasing directives emanating from financial regulators.

This new crop of startups performs sophisticated background checks and transaction monitoring for anti-money laundering purposes pursuant to the Bank Secrecy Act, the Office of Foreign Asset Control (OFAC) and FINRA rules; tracks supervision requirements and retention for electronic communications under FINRA, SEC, and CFTC regulations; as well as monitors information security and privacy laws from the EU, SEC, and several US state regulators such as the New York Department of Financial Services (“NYDFS”).

In this article, we’ll examine RegTech startups in these three fields to determine how solutions have been structured to meet regulatory demand as well as some of the operational and regulatory challenges they face.

Know Your Customer and Anti-Money Laundering

The Valley’s rocky history with clean tech investing has been well-documented.

Startups focused on non-emitting generation resources were once lauded as the next big cash cow, but the sector’s hype quickly got away from reality.

Complex underlying science, severe capital intensity, slow-moving customers, and high-cost business models outside the comfort zones of typical venture capital, ultimately caused a swath of venture-backed companies and investors in the clean tech boom to fall flat.

Yet, decarbonization and sustainability are issues that only seem to grow more dire and more galvanizing for founders and investors by the day, and more company builders are searching for new ways to promote environmental resilience.

While funding for clean tech startups can be hard to find nowadays, over time we’ve seen clean tech startups shift down the stack away from hardware-focused generation plays towards vertical-focused downstream software.

A far cry from past waves of venture-backed energy startups, the downstream clean tech companies offered more familiar technology with more familiar business models, geared towards more recognizable verticals and end users. Now, investors from less traditional clean tech backgrounds are coming out of the woodworks to take a swing at the energy space.

An emerging group of non-traditional investors getting involved in the clean energy space are those traditionally focused on fintech, such as New York and Europe based venture firm Anthemis — a financial services-focused team that recently sat down with our fintech contributor Gregg Schoenberg and I (check out the full meat of the conversation on Extra Crunch).

The tie between clean tech startups and fintech investors may seem tenuous at first thought. However, financial services has long played a significant role in the energy sector and is now becoming a more common end customer for energy startups focused on operations, management and analytics platforms, thus creating real opportunity for fintech investors to offer differentiated value.

Finance powering the world?

Though the conversation around energy resources and decarbonization often focuses on politics, a significant portion of decisions made in the energy generation business is driven by pure economics — Is it cheaper to run X resource relative to resources Y and Z at a given point in time? Based on bid prices for Request for Proposals (RFPs) in a specific market and the cost-competitiveness of certain resources, will a developer be able to hit their targeted rate of return if they build, buy or operate a certain type of generation asset?

Alternative generation sources like wind, solid oxide fuel cells, or large-scale or even rooftop solar have reached more competitive cost levels – in many parts of the US, wind and solar are in fact often the cheapest form of generation for power providers to run.

Thus as renewable resources have grown more cost competitive, more, infrastructure developers, and other new entrants have been emptying their wallets to buy up or build renewable assets like large scale solar or wind farms, with the American Council on Renewable Energy even forecasting cumulative private investment in renewable energy possibly reaching up to $1 trillion in the US by 2030.

A major and swelling set of renewable energy sources are now led by financial types looking for tools and platforms to better understand the operating and financial performance of their assets, in order to better maximize their return profile in an increasingly competitive marketplace.

Therefore, fintech-focused venture firms with financial service pedigrees, like Anthemis, now find themselves in pole position when it comes to understanding clean tech startup customers, how they make purchase decisions, and what they’re looking for in a product.

In certain cases, fintech firms can even offer significant insight into shaping the efficacy of a product offering. For example, Anthemis portfolio company kWh Analytics provides a risk management and analytics platform for solar investors and operators that helps break down production, financial analysis, and portfolio performance.

For platforms like kWh analytics, fintech-focused firms can better understand the value proposition offered and help platforms understand how their technology can mechanically influence rates of return or otherwise.

The financial service customers for clean energy-related platforms extends past just private equity firms. Platforms have been and are being built around energy trading, renewable energy financing (think financing for rooftop solar) or the surrounding insurance market for assets.

When speaking with several of Anthemis’ clean tech portfolio companies, founders emphasized the value of having a fintech investor on board that not only knows the customer in these cases, but that also has a deep understanding of the broader financial ecosystem that surrounds energy assets.

Founders and firms seem to be realizing that various arms of financial services are playing growing roles when it comes to the development and access to clean energy resources.

By offering platforms and surrounding infrastructure that can improve the ease of operations for the growing number of finance-driven operators or can improve the actual financial performance of energy resources, companies can influence the fight for environmental sustainability by accelerating the development and adoption of cleaner resources.

Ultimately, a massive number of energy decisions are made by financial services firms and fintech firms may often times know the customers and products of downstream clean-tech startups more than most.  And while the financial services sector has often been labeled as dirty by some, the vital role it can play in the future of sustainable energy offers the industry a real chance to clean up its image.

OpenFin, the company looking to provide the operating system for the financial services industry, has raised $17 million in funding through a Series C round led by Wells Fargo, with participation from Barclays and existing investors including Bain Capital Ventures, J.P. Morgan and Pivot Investment Partners. Previous investors in OpenFin also include DRW Venture Capital, Euclid Opportunities and NYCA Partners.

Likening itself to “the OS of finance,” OpenFin seeks to be the operating layer on which applications used by financial services companies are built and launched, akin to iOS or Android for your smartphone.

OpenFin’s operating system provides three key solutions which, while present on your mobile phone, has previously been absent in the financial services industry: easier deployment of apps to end users, fast security assurances for applications and interoperability.

Traders, analysts and other financial service employees often find themselves using several separate platforms simultaneously, as they try to source information and quickly execute multiple transactions. Yet historically, the desktop applications used by financial services firms — like trading platforms, data solutions or risk analytics — haven’t communicated with one another, with functions performed in one application not recognized or reflected in external applications.

“On my phone, I can be in my calendar app and tap an address, which opens up Google Maps. From Google Maps, maybe I book an Uber . From Uber, I’ll share my real-time location on messages with my friends. That’s four different apps working together on my phone,” OpenFin CEO and co-founder Mazy Dar explained to TechCrunch. That cross-functionality has long been missing in financial services.

As a result, employees can find themselves losing precious time — which in the world of financial services can often mean losing money — as they juggle multiple screens and perform repetitive processes across different applications.

Additionally, major banks, institutional investors and other financial firms have traditionally deployed natively installed applications in lengthy processes that can often take months, going through long vendor packaging and security reviews that ultimately don’t prevent the software from actually accessing the local system.

OpenFin CEO and co-founder Mazy Dar (Image via OpenFin)

As former analysts and traders at major financial institutions, Dar and his co-founder Chuck Doerr (now president & COO of OpenFin) recognized these major pain points and decided to build a common platform that would enable cross-functionality and instant deployment. And since apps on OpenFin are unable to access local file systems, banks can better ensure security and avoid prolonged yet ineffective security review processes.

And the value proposition offered by OpenFin seems to be quite compelling. OpenFin boasts an impressive roster of customers using its platform, including more than 1,500 major financial firms, almost 40 leading vendors and 15 of the world’s 20 largest banks.

More than 1,000 applications have been built on the OS, with OpenFin now deployed on more than 200,000 desktops — a noteworthy milestone given that the ever-popular Bloomberg Terminal, which is ubiquitously used across financial institutions and investment firms, is deployed on roughly 300,000 desktops.

Since raising their Series B in February 2017, OpenFin’s deployments have more than doubled. The company’s headcount has also doubled and its European presence has tripled. Earlier this year, OpenFin also launched it’s OpenFin Cloud Services platform, which allows financial firms to launch their own private local app stores for employees and customers without writing a single line of code.

To date, OpenFin has raised a total of $40 million in venture funding and plans to use the capital from its latest round for additional hiring and to expand its footprint onto more desktops around the world. In the long run, OpenFin hopes to become the vital operating infrastructure upon which all developers of financial applications are innovating.

“Apple and Google’s mobile operating systems and app stores have enabled more than a million apps that have fundamentally changed how we live,” said Dar. “OpenFin OS and our new app store services enable the next generation of desktop apps that are transforming how we work in financial services.”

Atlanta-based Movius, a company that allows companies to assign a separate business number for voice calls and texting to any phone, today announced that it has raised a $45 million Series D round led by JPMorgan Chase, with participation from existing investors PointGuard Ventures, New Enterprise Associates and Anschutz Investment company. With this, the company has now raised a total of $100 million.

In addition to the new funding, Movius also today announced that it has brought on former Adobe and Sun executive John Loiacono as its new CEO. Loiacono was also the founding CEO of network analytics startup Jolata.

“The Movius opportunity is pervasive. Almost every company on planet Earth is mobilizing their workforce but are challenged to find a way to securely interact with their customers and constituents using all the preferred communication vehicles – be that voice, SMS or any other channel they use in their daily lives,” said Loiacono. “I’m thrilled because I’m joining a team that features highly passionate and proven innovators who are maniacally focused on delivering this very solution. I look forward to leading this next chapter of growth for the company.”

Sanjay Jain, the chief strategy officer at Hyperloop Transportation Technologies, and Larry Feinsmith, the head of JPMorgan Chase’s Technology Innovation, Strategy & Partnerships office, are joining the company’s board.

Movius currently counts more than 1,400 businesses as its customers, and its carrier partners include Sprint, Telstra and Telefonica. What’s important to note is that Movius is more than a basic VoIP app on your phone. What the company promises is a carrier-grade network that allows businesses to assign a second number to their employees’ phones. That way, the employer remains in charge, even as employees bring their own devices to work.

With its latest $34 billion acquisition of Red Hat, IBM may have found something more elementary than “Watson” to save its flagging business.

Though the acquisition of Red Hat  is by no means a guaranteed victory for the Armonk, N.Y.-based computing company that has had more downs than ups over the five years, it seems to be a better bet for “Big Blue” than an artificial intelligence program that was always more hype than reality.

Indeed, commentators are already noting that this may be a case where IBM finally hangs up the Watson hat and returns to the enterprise software and services business that has always been its core competency (albeit one that has been weighted far more heavily on consulting services — to the detriment of the company’s business).

Watson, the business division focused on artificial intelligence whose public claims were always more marketing than actually market-driven, has not performed as well as IBM had hoped and investors were losing their patience.

Critics — including analysts at the investment bank Jefferies (as early as one year ago) — were skeptical of Watson’s ability to deliver IBM from its business woes.

As we wrote at the time:

Jefferies pulls from an audit of a partnership between IBM Watson and MD Anderson as a case study for IBM’s broader problems scaling Watson. MD Anderson cut its ties with IBM after wasting $60 million on a Watson project that was ultimately deemed, “not ready for human investigational or clinical use.”

The MD Anderson nightmare doesn’t stand on its own. I regularly hear from startup founders in the AI space that their own financial services and biotech clients have had similar experiences working with IBM.

The narrative isn’t the product of any single malfunction, but rather the result of overhyped marketing, deficiencies in operating with deep learning and GPUs and intensive data preparation demands.

That’s not the only trouble IBM has had with Watson’s healthcare results. Earlier this year, the online medical journal Stat reported that Watson was giving clinicians recommendations for cancer treatments that were “unsafe and incorrect” — based on the training data it had received from the company’s own engineers and doctors at Sloan-Kettering who were working with the technology.

All of these woes were reflected in the company’s latest earnings call where it reported falling revenues primarily from the Cognitive Solutions business, which includes Watson’s artificial intelligence and supercomputing services. Though IBM chief financial officer pointed to “mid-to-high” single digit growth from Watson’s health business in the quarter, transaction processing software business fell by 8% and the company’s suite of hosted software services is basically an afterthought for business gravitating to Microsoft, Alphabet, and Amazon for cloud services.

To be sure, Watson is only one of the segments that IBM had been hoping to tap for its future growth; and while it was a huge investment area for the company, the company always had its eyes partly fixed on the cloud computing environment as it looked for areas of growth.

It’s this area of cloud computing where IBM hopes that Red Hat can help it gain ground.

“The acquisition of Red Hat is a game-changer. It changes everything about the cloud market,” said Ginni Rometty, IBM Chairman, President and Chief Executive Officer, in a statement announcing the acquisition. “IBM will become the world’s number-one hybrid cloud provider, offering companies the only open cloud solution that will unlock the full value of the cloud for their businesses.”

The acquisition also puts an incredible amount of marketing power behind Red Hat’s various open source services business — giving all of those IBM project managers and consultants new projects to pitch and maybe juicing open source software adoption a bit more aggressively in the enterprise.

As Red Hat chief executive Jim Whitehurst told TheStreet in September, “The big secular driver of Linux is that big data workloads run on Linux. AI workloads run on Linux. DevOps and those platforms, almost exclusively Linux,” he said. “So much of the net new workloads that are being built have an affinity for Linux.”