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May 26, 2019
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Trinity Ventures

Partnering with Visa, emerging market lender Branch International raises $170 million

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The San Francisco-based startup Branch International, which makes small personal loans in emerging markets, has raised $170 million and announced a partnership with Visa to offer virtual, pre-paid debit cards to Branch client networks in Africa, South-Asia and Latin America. 

Branch — which has 150 employees in San Francisco, Lagos, Nairobi, Mexico City and Mumbai — makes loans starting at $2 to individuals in emerging and frontier markets. The company also uses an algorithmic model to determine credit worthiness, build credit profiles and offer liquidity via mobile phones.

“We’ll use [the money] to deepen existing business in Africa. Later this year we’ll announce high-yield savings accounts…in Africa,” says Branch co-founder and chief executive Matt Flannery.

The $170 million round from Foundation Capital and its new debit card partner, Visa, will support Branch’s international expansion, which could include Brazil and Indonesia, according to Flannery. Branch launched in Mexico and India within the last year. In Africa, it offers its services in Kenya, Nigeria and Tanzania.

A potential Branch customer

The Branch-Visa partnership will allow individuals to obtain virtual Visa accounts with which to create accounts on Branch’s app. This gives Branch larger reach in countries such as Nigeria — Africa’s most populous country with 190 million people — where cards have factored more prominently than mobile money in connecting unbanked and underbanked populations to finance.

Founded in 2015, Branch started operating in Kenya, where mobile money payment products such as Safaricom’s M-Pesa (which does not require a card or bank account to use) have scaled significantly. M-Pesa now has 25 million users, according to sector stats released by the Communications Authority of Kenya. Branch has more than 3 million customers and has processed 13 million loans and disbursed more than $350 million, according to company stats.

Branch has one of the most downloaded fintech apps in Africa, per Google Play app numbers combined for Nigeria and Kenya, according to Flannery.

Already profitable, Branch International expects to reach $100 million in revenues this year, with roughly 70 percent of that generated in Africa, according to Flannery.

In addition to Visa and Foundation Capital, the $170 Series C round included participation from Branch’s existing investors Andreessen Horowitz, Trinity Ventures, Formation 8, the IFC, CreditEase and Victory Park, while adding new investors Greenspring, Foxhaven and B Capital.

Branch last raised $70 million in 2018. The company’s overall VC haul and $100 million revenue peg register as pretty big numbers for a startup focused primarily on Africa. Pan-African e-commerce startup Jumia, which also announced its NYSE IPO last month, generated $140 million in revenue (without profitability) in 2018.

Startups building financial technologies for Africa’s 1.2 billion population have gained the attention of investors. As a sector, fintech (or financial inclusion) attracted 50 percent of the estimated $1.1 billion funding to African startups in 2018, according to Partech.

Branch’s recent round and plans to add countries internationally also tracks a trend of fintech-related products growing in Africa, then expanding outward. This includes M-Pesa, which generated big numbers in Kenya before operating in 10 countries around the world. Nigerian payments startup Paga announced its pending expansion in Asia and Mexico late last year. And payment services such as Kenya’s SimbaPay have also connected to global networks like China’s WeChat.

Healthcare wearables level up with new moves from Apple and Alphabet

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Announcements that Apple has partnered with Aetna health insurance on a new app leveraging data from its Apple Watch and reports that Verily — one of the health-focused subsidiaries of Google‘s parent company — Alphabet, is developing a shoe that can detect weight and movement, indicate increasing momentum around using data from wearables for clinical health applications and treatments.

For venture capital investors, the movea from Apple and Alphabet to show new applications for wearable devices is a step in the right direction — and something that’s been long overdue.

“As a healthcare provider, we talk a lot about the important of preventative medicine, but the US healthcare system doesn’t have the right incentives in place to pay for it,” writes Cameron Sepah, an entrepreneur in residence at Trinity Ventures. “Since large employers largely pay for health care (outside of Medicaid and Medicare), they usually aren’t incentivized to pay for prevention, since employees don’t stay long enough for them to incur the long-term costs of health behaviors. So most startups in this space end up becoming an expendable wellness perk for companies. However, if an insurer like Aetna keeps its members long enough, there’s better alignment for disseminating this app.”

Sepah sees broader implications for the tie ups between health insurers and the tech companies making all sorts of devices to detect and diagnose conditions.

“Most patients relationship with their insurer is just getting paper bills/notifications in the mail, with terrible customer satisfaction (NPS) across the board,” Sepah wrote in an email. “But when there’s a way to build a closer relationship through a device that sits on your wrist, it opens possibilities to partner with other health tech startups that can notify patients when they are having mental health issues before they even recognize it (e.g. Mindstrong); or when they should get treatment for hypertension or sleep apnea (e.g. Cardiogram); or leverage their data into a digital chronic disease treatment program (e.g. Omada Health).”

Aetna isn’t the first insurer to tie Apple Watch data to their policies. In September 2018, John Hancock launched the Vitality program, which also gave users discounts on the latest Apple Watch if they linked it with John Hancock’s app. The company also gave out rewards if users changed their behavior around diet and exercise.

In a study conducted by Rand Europe of 400,000 people in the U.S., the U.K., and South Africa, research showed that users who wore an Apple Watch and participated in the Vitality benefits program averaged a 34 percent increase in physical activity compared to patients without the Apple Watch. It equated to roughly 5 extra days of working out per month.

“[It will] be interesting to see how CVS/Apple deal unfolds. Personalized health guidance based on a combination of individual medical records and real time wearable data is a huge and worthy goal,” wrote Greg Yap, a partner at the venture capital firm, Menlo Ventures . But, Yap wrote,I’m skeptical their first generation app will have enough data or training to deliver value to a broad population, but we’re likely to see some anecdotal benefits, and I find that worthwhile.”

Meanwhile the types of devices that record consumer health information are proliferating — thanks in no small part to Verily.

With the company reportedly working to co-develop shoes with sensors that monitor users’ movement and weight, according to CNBC, Verily is expanding its portfolio of connected devices for health monitoring and management. The company already has a watch that monitors certain patient data — including an FDA approved electrocardiogram — and is developing technologies to track diabetes-related eye disease in patients alongside smart lenses for cataract recovery.

It’s part of a broader push from technology companies to tie themselves closer to consumer health as they look to seize a part of the nearly $3 trillion healthcare industry.

If more data can be collected from wearable devices (or consumer behavior) and then monitored in a consistent fashion, tech companies ideally could suggest interventions faster and provide lower cost treatments to help avoid the need for urgent or emergency care.

These “top of the funnel” communications and monitoring services from tech companies could conceivably divert users and future healthcare patients into an alternative system that is potentially lower-cost with more of a focus on outcomes than on the volume of care and number of treatments prescribed.

Not all physicians are convinced that the use of persistent monitoring will result in better care. Dr. John Ioannidis, a celebrated professor from Stanford University, is skeptical about the utility of monitoring without a better understanding of what the data actually reveals.

“Information is good for you provided you know what it means. For much of that information we have no clue what it means. We have absolutely no idea what to do with it other than creating more anxiety,” Dr. Ioannidis said

The goal is to provide personalized guidance where machine learning can be used to identify problems and come up in concert with established therapeutic practices, according to investors who back life sciences starups.

“I think startups like Omada, Livongo, Lark, Vida, Virta, and others, can work and are already working on this overall vision of combining real time and personal historical data to deliver personalized guidance. But to be successful, startups need to be more narrowly focused and deliver improved outcomes and financial benefits right away,” according to Yap.

 

Kite raises $17M for its AI-driven code completion tool

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Kite, a San Francisco-based startup that uses machine learning to build what is essentially a very smart code-completion tool, today announced that it has raised a $17 million funding round. The round was led by Trinity Ventures, with personal participation from now-GitHub CEO Nat Friedman. In addition to the funding, Kite also today announced that its tools are now significantly smarter and that developers can run them locally on their machines, even if they don’t have an internet connection.

As Kite founder and CEO Adam Smith told me, the idea for Kite is based on the simple fact that a lot of programming is repetitive. “That’s why [developers] spend so much time on Stack Overflow. That’s why they spend so much time debugging really basic errors and looking up documentation, but not so much time looking at how the solution should work,” he said. “We thought we can use machine learning to fix that.”

Standard code completion tools often still use alphabetical sorting while Kite uses AI to infer what a developer is likely trying to do (though to be fair, the likes of IntelliSense and others are also starting to get smarter). In its first iteration, Kite, which sadly still only works for Python code right now, sorted its hints by popularity. Unsurprisingly, that was already more useful than alphabetical sorting and the right answer appeared in the top three results 37 percent of the time.

What’s interesting here is that if you can predict the next part of a line of code with high accuracy, you can start predicting a few more words ahead, too. And that’s exactly what Kite is starting to do now.

To do this, the team had to build its own machine learning models that worked well for code. As Smith told me, Kite first looked at using standard natural language processing (NLP) models, but it turns out that those don’t really work well for code, which has a different structure. As training data, Kite fed the system all the Python code on GitHub .

Looking ahead, what Smith really wants to achieve is what he calls ‘fully automated programming.’ “It’s that Star Trek vision of where you tell computers in a high-level language what to do,” he said. “If it’s ambiguous, the computer will ask questions.”

It’ll take a few more breakthroughs in AI to realize that vision, but for the time being, Kite’s tools are freely available and come with editor plugins for Atom, Sublime Text3, VS Code, Vim, PyCharm and IntelliJ. Currently, about 30,000 Python developers use its tools.

With today’s release, developers can also use these models locally, without the need for an Internet connection. That’s a sign of how efficient the models are, but as Smith also acknowledged, running the model locally means his company doesn’t have to manage a complex cloud infrastructure either. This should also make the tool more appealing to more developers — especially in larger corporations — given that the original tool would send all of your code to Kite’s servers (and in that context, it’s worth noting the company managed to create its own little scandal around some open source contributions that favored its auto-completion engine).

The company plans to use the new funding to build out the team, which mostly consists of engineers. It’ll also build out its product, with a special focus on supporting more languages.

As for its business model, it’s worth noting that Kite did test a subscription service last year, but as Smith argues, that was mostly to test if the company could monetize the service. “Now we want to optimize for growth,” he said and noted that the focus of the company’s monetization strategy will be on enterprise users. Indeed, that’s a common refrain I hear from startups that focus on developers. It’s very hard to sell subscriptions to individual developers, it seems, so most start to focus on enterprises sooner or later.

SynapseFi raises $17M to develop its fintech and banking platform

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SynapseFi, a startup that helps banks and fintech companies work together to develop technology, has announced that it raised a $17 million Series A funding round.

The funding actually closed at the back end of last year, but CEO Sankaet Pathak said the company has been so busy developing new products, hiring and more than that it is only getting around to disclosing the deal now. The investment was led by Trinity Ventures and Core Innovation Capital, with participation from other unnamed backers.

The San Francisco-based startup has sat under the radar for a while now despite starting up in 2014. Its core product is a platform that helps banks and developers work together. That involves developer-facing APIs that allow companies to connect with banks to offer services, and also bank-facing APIs that allow banks to automate and extend back-end operations.

Pathak describes the vision as making it possible for anyone around the world to get access to high-quality financial products. The first focus is to make financial products “like Lego bricks” to enable banks to add new products and services easily. As it stands today, development is a painful process that requires specific infrastructure development, but SynapseFi aims to standardize a lot of the processes and platform to make things much simpler.

The idea for the business came when Pathak, who moved to the U.S. from India in 2010, grew frustrated at being unable to get a bank account or loan because he had no social security history. He left the University of Memphis, where he had studied computer engineering and sciences, and founded the startup in April 2014 alongside his friend Bryan Keltner.

Initially, the business focused on payments, but it gradually tilted to become a technology enabler for the financial industry.

Today, SynapseFi has over 60 staff and it works with a roster of over 100 financial industry clients. Its products include the basics like payment, deposit, lending and investment services, but it has also ventured into crypto with services that include a white-label wallet.

To date, it claims to have processed over $10 billion in transactions and helped bank more than 1.5 million people through its technology.

The SynapseFi team

“There are three core things we want to fix in banking,” Pathak told TechCrunch in an interview. “The back office is still mostly manual today and we want to automate that. There’s a need for vertical integration… we want any large or small financial company to be able to come to us and operate at the same scale as the likes of Wells and Chase. We also want to automate financial advice using behavior science.”

Pathak added that the startup also harbors an ambition to expand overseas. That’s likely to mean Europe first — potentially a market like the UK or Germany — but it’ll require fairly intensive localization as the SynapseFi platform is customized to accommodate U.S. APIs and data pipes, none of which will work outside of the country.

An expansion would be likely to happen around the time that the company looks to raise its Series B, although Pathak stressed that he is also focused on building a sustainable business and not simply relying on venture capital money. Indeed, he said that the company is likely to reach breakeven by the end of the year.

“I still think it’s a healthy business practice,” Pathak said.

Weights & Biases raises $5M to build development tools for machine learning

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Machine learning is one of those buzzwords that nearly every tech company likes to throw around nowadays — but according to Lukas Biewald, it represents a genuinely new approach to programming.

“Software has eaten a lot of the world, and machine learning is eating software,” Biewald said.

In his view, there are “fundamental” differences between the two approaches: “One important difference is if all you have is the code you used to train the program, you don’t really know what happened … If I had all the code that was used to train a self-driving car algorithm but I don’t have the data, I don’t know what went down.”

Along with Chris Van Pelt, Biewald previously founded CrowdFlower (now known as Figure Eight), which launched nearly a decade ago at the TechCrunch 50 conference, and which has created tools for training artificial intelligence.

Biewald (whom I’ve known since college) and Van Pelt, plus former Google engineer Shawn Lewis, have now started a new company called Weights & Biases to build new tools for machine learning developers. They’ve also raised $5 million in Series A funding from Trinity Ventures and Bloomberg Beta.

“Artificial Intelligence has so much potential, but few companies are implementing it yet because the development process is too complicated for all but a small number of highly trained engineers,” said Trinity’s Dan Scholnick, who’s joining the startup’s board of directors. (Scholnick previously backed CrowdFlower.) “W&B aims to dramatically streamline the machine learning software development process so that AI benefits can be unlocked across industries and no longer restricted to the few firms able to hire extremely skilled and extraordinarily expensive AI developers today.”

The eventual goal is to create a whole suite of development tools, but Weights & Biases’ first product records and visualizes the process of training a machine learning algorithm. Biewald explained that this makes it possible for developers to go back and see what they were doing, say, a month ago and to share that information with teammates. And it’s already being used by the nonprofit research company OpenAI.

Biewald added that when he talked to his friends in the field about their biggest problems, this was the first thing that came up. That’s how he hopes to approach future products as well — working with developers to figure out what they really need.

“I don’t want to help with the hype,” he said. “I want to help with the real problems that really get in the way … to make this stuff actually work.”

Biewald also offered more details about his vision for the company in a blog post:

You can’t paint well with a crappy paintbrush, you can’t write code well in a crappy IDE, and you can’t build and deploy great deep learning models with the tools we have now. I can’t think of any more important goal than changing that.

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