The Black Liszt

What is technical due diligence for Venture Capital?

I’m Technology Partner in a VC firm, Oak HC/FT. I help look at companies we’re considering investing in, and I help out as needed after investment. Recently I’ve been thinking about what I do, mostly because I’ve encountered multiple cases of technology due diligence performed on companies I know well. To put it plainly, I don’t do it the same way.

The firms whose work I’ve seen are professional, well-regarded groups. Their work fits into a pattern of similar work I’ve seen over many years. Their work can have value.

My Evolution in Tech Due Diligence

When I started performing tech due diligence for the VC firm Oak Investment Partners in 1992, I had no specific idea of how to do it. My background had been almost exclusively on the “other side,” i.e., working and building software, mostly for small, entrepreneurial companies. But I’d had occasion to dive into bodies of software over the years and figure them out, so I guessed I’d be able to figure it out better than any non-programmer could. All I can say is, I went through quite an evolution from there.

The starting point was clear and simple: evaluate the software. There’s a lot of work involved and not many people have the skills to do it well, but there’s no magic. I quickly developed an outline for what an evaluation consists of. It was a few pages long. Here’s the last part:

The earlier parts … well, let’s just say it was comprehensive.

I marched forward doing the work and interacting with the people in the company being evaluated and with the partners in the VC firm. I evolved my views of tech due diligence.

In the late 1990’s, I did an on-site tech diligence of a little company called Inktomi. It was started by a couple guys from U Cal Berkeley, one a young professor and the other a grad student. They were hard at work on a project called NOW – the network of workstations, which was an early attempt to build software that would enable a bunch of workstations connected by fast networking to solve really big computing problems, of the kind normally only so-called super-computers could handle. They built the software, and needed a big, big problem to solve to demonstrate that it worked. There were already full-text databases around that ran on standard computers. Also at the time, the internet was exploding, with no end in sight. There was an emerging need to help people find things on the internet. At the time, the best available solutions were “portals,” of which Yahoo was a leading example. A portal was a densely populated page managed by people who would put up links to the main things people were interested in. But what if you wanted more? Wouldn’t it be great to have a full-text search engine that could index and search the entire internet, even as it grew endlessly?

This is the problem Inktomi took on. No existing search engine could come within a factor of 100 of indexing and searching the whole internet at the time. The boys at Inktomi used their NOW infrastructure to attack the problem, first building a crawling and index-building system, then building the search. It wasn’t easy, but it turns out that it fit right into the NOW approach. If you understood it, you could see that by adding workstations to the NOW, you could scale the index endlessly, no matter how large the internet grew.

I walked into the second-floor warren of offices above stores on Shattuck Ave. in Berkeley, with machines and people crammed in anywhere they would fit. I already had heard the basic idea. I looked over people’s shoulders at screens, and noticed some things about the C code there. I asked questions and discovered they had implemented parallelism not by using standard UNIX multi-tasking, but by a super-low-overhead coding method. I saw the cables and boards, and found out the clever things they had done to minimize inter-workstation latency. And some more stuff.

Before long, and without doing anything like the exhaustive inventory of my original approach to due diligence, I had discovered the originality of their approach and their impressive implementation of it, which would give them a lead over any competitors that would emerge. I became a strong advocate of the deal.They took our money, the largest investment we had made in a company to that date, and ended up making over 100X return.

Of course today, we don’t “inktomi” for things on the internet, we “google.” That’s a story for another time, and happened after Inktomi had attained a public market valuation in excess of $10 Billion.

I tell the story to illustrate one of the fulcrum points of my evolution in performing tech due diligence. The way I did it and the resulting win helped spur me on to doing what the VC really needs, which is to leverage my experience and skills into getting insights into a potential deal that are invisible to the other members of the firm because of their lack of detailed knowledge of software and systems. The insights can range from “makes no difference here” to “OMG negative” to “how could I have known that super-positive.”

What I finally evolved to, and continue to work at, revolves around this simple fact: the VC firm wants to know if this technology, the way it is today and is likely to grow into the future, will make the company into a success and end up making money for us and our investors. That’s it! Everything else is a footnote or appendix.

The vast majority of tech due diligence I see performed by firms who specialize in performing that work, and who are regularly retained by groups doing investments and/or acquisitions, is not about this. What I almost universally see is something like what I started with long ago, with this very important added element: how do the tech, the people, the organization, the process, the deployment and the architectural choices made by the firm compare to widely held industry standards, best practices and regulations? When I look back at my old outline, I see this element entirely missing! Thinking back, I realize that the reason is that I had already decided that those widely held standards, unlike the ones in law and accounting, were a pile of crap. I had already noticed what I later began to systematically study and write about, the difference between the "war time" software practices of winning startups, compared to the widely accepted "peace time" methods.

Conclusion

If you want to acquire a tech-oriented company, you naturally want to perform due diligence. If you’re a big company, you may want to acquire it for its momentum and market edge; Facebook and Google do lots of acquiring for this reason, among others. You want to know how well the target company conforms to the kinds of standards, practices and procedures that your internal organization is held to. But if you’re a smart VC, you want different things from tech diligence. Does the tech give the company an “unfair advantage?” Do they approach tech with a speed-oriented, get-stuff-done attitude? The tech may be 2X better than the incumbents, but could a newcomer pretty easily get 5X? Sounds improbable, but this kind of thing happens all the time. If you’re a VC about to place a bet on an up-and-coming company, that’s the kind of tech input you should want.

Getting Results from ML and AI 6: Fintech Chatbot

While the success patterns laid out in the prior posts in this series may seem clear in the abstract, applying them in practice can be hard, because nearly everyone who thinks or talks about AI (these sets over overlap very little, sadly) takes a different approach.

http://www.blackliszt.com/2018/03/getting-results-from-ml-and-ai-1.html

http://www.blackliszt.com/2018/04/getting-results-from-ml-and-ai-2.html

http://www.blackliszt.com/2018/04/getting-results-from-ml-and-ai-3-closed-loop.html

I previously discussed the application of the principles to healthcare, with specific examples:

https://www.blackliszt.com/2018/08/getting-results-from-ml-and-ai-4-healthcare-examples.html

I've discussed the application of the principles to fintech, with a focus on anti-fraud:

https://www.blackliszt.com/2019/12/getting-results-from-ml-and-ai-5-fintech-fraud.html

In this post, I'll show how things can play out with a stellar example in fintech chatbots.

Computers talking with People -- Using People Language

The idea that computers should talk with us in our language rather than people struggling to learn computer talk has been around nearly as long as computers have. The earliest "high level languages" like FORTRAN and COBOL were each attempts to let people use English-like language for telling a computer what to do. They were baby steps, and people quickly decided that computers can and should do better. This thought was one of the earliest practical drivers towards Artificial Intelligence (AI) in general, and Natural Language Processing (NLP) in particular. 

One of the acknowledged milestones towards a computer that can talk like people was the work of Terry Winograd at MIT during 1968-1970. He created a talking robot, SHRDLU, that could talk about and act in a special world of blocks:

SHRDLU could have conversations about the block world that amazed people at the time. Here's an excerpt, see this for more.

I was at college at the other end of Cambridge at the time, heard about SHRDLU, and got even deeper into the AI work I was engaged in at the time as a result, for example this project. SHRDLU was wonderful, but I wondered about and worked on how to represent knowledge and actions internally. After I graduated, "everyone" was convinced that "talking computers" were going to burst on the scene any day now. A couple decades passed, and nothing.

Talking Computers Today

Here we are, 50 years later, and things have definitely advanced. We have Alexa and Siri, and in banking we have Bank of America's much-promoted Erika. But in many ways, these programs remain primitive, and are far from being able to understand context and sequence the way people do.

One of the reasons for this is that human language understanding and generation is really hard. Another is that the vast majority of people who work on the problem are thoroughly immersed in the other-worldly vapors of academia, in which publishing papers and gaining esteem among your fellows are the all-consuming goals -- to the exclusion of building products that do things that normal people value and, you know, work.

The State of the Art in Chatbots

The founders of the Oak HC/FT portfolio company Kasisto come from that world, and also from the industrial labs of Stanford and IBM that try hard to commercialize such fancy stuff, with products like Siri as results. The Kasisto people are obviously exceptional not just in their field, but in their drive to make code that does real things in the real world. If that's your goal, there is exactly one standard for measurement: what people do and say.

That's the background of the first remarkable thing about Kasisto, which I learned when I probed details of their process. Pretty much everyone who deals with AI/ML builds and builds in the lab, until they have achieved amazing things that they're ready to roll out ... which promptly belly-flops in the real world. We'll do better next time, promise! Kasisto works the way everyone should work, but practically no one does -- people first!

This means they get in the middle of a huge number of human-to-human chat sessions, building and tuning their software first by human judgment, but increasingly by machine judgment. They "try" to answer the human's question, and just as important, they rate their ability to answer the question appropriately. After tens of thousands of tests, their ability to respond like a human would improves -- and just as important, their self-rating of how well they're likely to do improves as well.

As their answers and the associated self-ratings get good, they start actively playing a role in the live question-answer flow. For exactly and only the human questions for which Kasisto is "confident" (using an adjustable threshold) it will answer well, its answers go to the human -- but are still routed to a human for double-checking, with a frequency that goes down over time.

There will always be questions that are beyond the capability of the Kasisto bot, but so long as it is "self-aware" of which ones, the customers continue to get great service, with an ever-shrinking fraction of the questions being routed to humans for answers. If Kasisto can handle 95% of the questions, this might means that instead of a staff of 100 to respond to customers, a staff of 5 could do the job.

This way of thinking is far removed from the typical academic head-set -- and I've just given a couple highlights, there's actually much more where that came from!

Beating the Pack

What I've already described enables Kasisto to deliver chat results to customers that are far superior to anyone else in the field. But the overall head-set and people-first technique, combined with some true tech smarts, leads to further things.

The first thing is context. All the other chatbots are lucky if they can give reasonable answers to isolated questions. The combination of human-first, good tech and practical methods enables Kasisto to not only answer isolated questions far better than others, but follow-on questions as well -- questions that make no sense by themselves, but perfect sense when taken in the context of an interactive sequence. As a simple example, consider this:

"How much did I spend last month?"

"How much of it was on restaurants?"

"How about the previous month?"

That's a simple one for humans, but way beyond what other chatbots can do. Why? The computer has to figure out that "how much of it" means "How much did I spend on restaurants last month?" Not to mention, handling multiple languages; performing transactions; making changes and selling products.

While Kasisto started with consumer banking, it's already added high-value functionality for treasury, business banking and more. The key is: Kasisto, unlike everyone else in the field, didn't just start selling into those applications -- they followed the laborious but effective, bottoms-up, people-first method of exhaustive training and seamless integration with humans doing chat.

Conclusion

Kasisto is an excellent example of the incredible results that can come from following the path briefly outlined in these blog posts. You would think with all the talk about AI and ML, and the all efforts and announcements, that highly capable systems would be popping out all over. They're not! That makes the few that follow the success patterns I've discussed here all the more impressive.

Getting Results from ML and AI 5: Fintech Fraud

While the success patterns laid out in the prior posts in this series may seem clear in the abstract, applying them in practice can be hard, because nearly everyone who thinks or talks about AI (these sets over overlap very little, sadly) takes a different approach.

http://www.blackliszt.com/2018/03/getting-results-from-ml-and-ai-1.html

http://www.blackliszt.com/2018/04/getting-results-from-ml-and-ai-2.html

http://www.blackliszt.com/2018/04/getting-results-from-ml-and-ai-3-closed-loop.html

I previously discussed the application of the principles to healthcare, with specific examples: https://www.blackliszt.com/2018/08/getting-results-from-ml-and-ai-4-healthcare-examples.html

In this post, I'll show how things can play out with a stellar example in fintech anti-fraud.

The Use of ML in Fraud Detection

Credit card fraud is a difficult, ever-evolving problem, not unlike cyber-security in general. In most cases, real money is involved, billions of dollars of it -- over $20 Billion world-wide as of a few years ago!

Robert Hecht-Nielsen was a pioneer in machine learning in the 1980's. He started HNC software to apply ML to a variety of problems. He was particularly fond of Neural Networks. After a few years of not doing well, he encountered a visionary executive of Household Finance, a large company that provided credit, including cards, to sub-prime customers. The HFC executive convinced Hecht-Nielsen to concentrate on card fraud, which was a large and growing problem for the card industry in general, and HFC in particular.

With HFC's support, HNC built the first effective card fraud detection system. It centered around humans analyzing recent instances of card fraud, building a detector for each particular kind, and training neural nets to combine all the detectors to be applied to each new transaction as it came through.

Since the model's effectiveness depended on having an ever-growing library of fraud detectors, HFC supported HNC creating a network model, in which each participating card issuer would contribute the details of each new case of fraud it encountered. HNC analysts would add detectors, train new models and give updated fraud detecting models to each participating company. It quickly became the industry standard, since all the participants benefited from the experience of all the members. HNC went public in the mid-1990's and later merged with FICO.

Enter Feedzai

How could any new entrant possibly compete against the data monopoly and neural networks of FICO/HNC? Somebody new wouldn't have access to the incredible database of card fraud. Feedzai, backed by Oak HC/FT, had some things that were more important.

First of all, it helped that the Feedzai founders were broadly expert in machine learning and other analytic methods. It took them a while to focus on "just" card fraud. Being a general expert and then deciding to focus on a narrow problem is just like HNC, and is a general success pattern. But HNC got frozen and dabbled in many other domains, failing to continue to innovate in their core product.

Once Feedzai got engaged with a card issuer, they noticed that the FICO solution was so slow that it couldn't detect whether an authorization was fraudulent in real time -- only after it was already approved. Result: every fraudster got at least one free! Beating FICO would require real-time analytics. Next, Feedzai noticed that FICO was trying to see if each transaction was BAD, and that seeing if each transaction was NOT GOOD was just as good -- even better, since you don't care how inventive the bad guys are, just that they're not acting like the real cardholder would act. That meant you could train your models on a single card issuer's customer base, ignoring the rest of the world. Cool! Finally, that meant you could use modern, transparent ML algorithms, and dispense entirely with the time-consuming and error-prone FICO method of modeling each type of fraud. And that meant that your first couple of appropriately skeptical customers could try you out, side-by-side, against the incumbent FICO, and see who won the catch-the-fraud race. Hooray, Feedzai won!

It's important to note that Feedzai also won because they did all the foundational steps described in the earlier posts in this series correctly, including the all-important know-your-data and closed-loop steps.

FICO/HNC moved on from their success in card fraud, thinking they had an absolute lock on the market -- which they did, for many years, but only because someone like Feedzai didn't go after them sooner. Feedzai isn't making the FICO/HNC mistake; they are continuing to build on their lead, both deepening and extending it. It's a big subject, but here are a couple of the highlights:

• Feedzai has built a tool that does much of the work highly skilled engineers used to have to do when supporting a new client. The original tool detects fraud; the new tool automates building a fraud detection tool. The speed and quality that results is unprecedented.
• While rarely discussed, every practical detection system has a set of human-created rules. Feedzai is the first company to automate the evaluation of the rules, and decide which should be changed or dropped. This is huge.
• Feedzai is now, step by step, introducing their technology to adjacent areas such as AML. The incentives created by the heavy regulation in AML are perverse, and has led to bloated costs in banks with no real improvement in AML detection. Feedzai's novel approach is actually making headway in spite of the regulations, and not just doing normal reg-tech automation.

Conclusion

Being an excellent generalist is often a good way to become a superb specialist. But it's rare to find a group of people who are truly up to snuff in the abstruse technologies of AI/ML, but also able to dive into the ugly details of data and real-life cases to make a system that beats the real-world incumbents. As we know from FICO/HNC and many other cases, the pattern is then to declare victory and move on. Feedzai is an object lesson in how to go deeper and deeper, continuing to innovate and automate. They are an excellent example of the principles described in the earlier posts in this series.

Take-aways from the 2019 Edition of the Money 2020 Conference

The 2019 US edition of Money 20/20 is a wrap. All of us who attended are recovering – and digesting.

Money 20/20 is now an established event – even though the first one was just seven years ago, in 2012! You might think it would be getting stodgy and repetitive by now, with bosses sending their underlings. Not so! Among the over 7,000 people attending were an amazing number of top, big-company executives – along with hundreds of startups, investors, and companies ranging from emerging to established. All the tech firms you’d expect also attended.

So what happened? Anything new? Big themes? Here are a couple:

Entrepreneurs

The startup energy was amazing, partly because there were literally hundreds of startups in attendance, many in little booths, many participating in the events and meetups that were organized for them and the larger groups – investors, partners and buyers – who want to know what’s going on. There’s no way I’m going to pick a couple as examples of the hundreds, but as a test, I’d suggest you come up with a couple ideas you think are novel, walk next year’s show, and see how novel your ideas really are. Unless you’re incredibly better than most, you’ll find a startup with your idea – and if not, maybe it’s your time to start a company!

The Feedzai Financial Crime Summit

We’re glad to be investors in Feedzai, and amazed at the sub-conference they’ve run the last couple of years. It’s too bad Agatha Christie couldn’t attend this year to help solve financial crimes, though the stellar crowd recruited by Feedzai did a fine job of it. The top person from Microsoft described how he spends nearly $1 Billion a year on cyber-security, with competing teams of hackers and defenders, and much else.

With so much of money being electronic and transactions being conducted on phones and computers, the silent, remote hacker is indeed the modern equivalent of Willie Sutton – whose answer to why he robbed banks was simple – because that’s where the money is. Now that the money is in computers and transmitted by electronic networks, that’s where the money is, and there was a great deal to be said about this ongoing battle between good guys and bad guys – and the rapidly escalating arms race that makes the outcome of any one battle so uncertain.

It’s clear that this topic will continue to grow in importance in coming years.

Cryptocurrency and blockchain

There was a whole track devoted to this hot topic, and some time on the big stage. In particular, David Marcus, the leader of Facebook’s Libra cryptocurrency effort, had the center seat of the giant center stage to himself, answering softball questions from a moderator. His smooth explanations acknowledged the widespread attention and questioning the Libra effort has engendered, while giving comforting answers and putting a calm, confident face on the effort.

There was quite a contrast between all the sessions devoted to this subject and what you saw on the show floor, which wasn’t much. Given that Money 20/20 is a show about payments, and given that cryptocurrency is, after all, a kind of currency, it’s natural there should be widespread interest in the topic, fueled by a generous portion of FOMO. There are lots of efforts and noise on the subject, but still not much evidence of real action. Everyone follows and will continue to follow the high-profile Facebook Libra effort with interest.

New Technologies: AI and ML

AI and ML continue to be the buzzwords nearly everyone wants to be associated with, in one way or another. This was particularly remarkable when you walked the show floor, with a high fraction of the booths claiming leadership in these subjects. Given the arcane nature of the underlying technology and the widely diverse technologies that are reasonably described as AI or ML – combined with the fact that most peoples’ eyes quickly glaze over when a nerdish person tries to explain what makes one of the dozens of machine learning algorithms different from another – it’s clear that the marketing person has an impossible task, and most of the show attendees have a challenge figuring out which approach is better than the others.

Nonetheless, this is one of those cases where the hype is clearly justified. For example, Feedzai is catching more of the bad guys than its direct competitors because its technology is just plain better, even though the others arguably also use AI/ML.

Eventually, people will figure out that this has happened before. It’s like decades ago, when a few leading companies claimed to be better than the competition because … they used computers! Yes, those incredibly advanced things that no one really understands. It wasn’t long before everyone first claimed to be using them, and then, eventually, everyone still in business was actually using computers! This year’s Money 20/20 clearly demonstrated that we’re still in the intermediate stage, in which the claims to be using AI/ML are widespread, and the reality is still in catch-up mode. We’ll know we’re really there when it’s just assumed – if you’re still in business, you must be using AI/ML; the only question is, who does it better. We’re getting there!

Lending, payments, banking

The show contained an amazing demonstration of continuing innovation in making it easier for people and businesses to send money, get money and borrow money. The days of checking that you’re properly dressed and groomed for your important meeting with the bank lending officer are long gone. Even the days of filling out forms, not just on paper, but also on the computer. As ever-growing portions of your financial history are stored and available in various online repositories, the job is mostly making sure you really are who you say you are. Once that’s settled, a couple simple actions on your phone are increasingly sufficient to get, send and borrow money, cheaply and quickly.

We saw an array of companies from point products to comprehensive, one-stop services, coming ever-closer to making every aspect of banking as quick and painless as messaging on your phone. The competition is fierce. It’s enough to make you wonder why the big banks survive. But all the big-name players are in the game and were at the show as well, with their own approach to making transactions as easy and efficient as possible for consumers and merchants. While everyone likes convenience, change isn’t particularly convenient; the big institutions have most of the customers and aren’t going to give them up without a fight. So they’re offering their own brands of ease and convenience to their customers. It was all on display at the show, and it’s clear that the war for the customer is far from over.

Regulation

As one of the most regulated industries, payments is doing an amazing job of innovating while avoiding the long arm of the bureaucratic enforcers. This is partly because regulation technology is advancing, as we saw at the show. But it’s got a ways to go. Automating regulation is a bit like teenagers eating vegetables – they respond to parental demands, but it’s not the first choice. As regulators continue to ratchet up the stakes and catch up to modern technologies, this is a field that continues to grow in importance at Money 20/20, both in the speakers and the exhibits.

Money 20/20

Next year will be 2020. Will this show be re-named Money 20/30? Or will moving towards 20/20 vision be the metaphor that justifies sticking with the name? Regardless of the name, the show will go on. It’s the one place you can go and see everyone who is anyone, and everyone who wants to be someone, all in one place in a short period of time. We’re already looking forward to next year.

Barriers to Software Innovation: Radiology 2

Value-creating innovations are rarely the result of a bright new A-HA moment, though an individual may have that experience. A shocking number of innovations are completely predictable, partly because they've already been implemented -- but put back in the vast reservoir of ready-to-use innovations, or implemented in some other domain. This fact is one of the most important patterns of software evolution.

Sometimes the innovation is created, proven and fully deployed in production, like the optimization method Linear Programming, which I describe here. In other cases, like this one, the innovation is built as a functioning prototype with the cooperation of major industry players -- but not deployed.

In a prior post I described how I went to the San Francisco bay area in the summer of 1971 to help a couple of my friends implement a system that would generate a radiology report from a marked-up mark-sense form. We got the system working to the point where it could generate a customizable radiologist's report from one of the form types, the one for the hand. Making it work for all the types of reports would have been easy -- we demonstrated working software, and wrote a comprehensive proposal for building the whole system. It was never built.

True to the nature of software evolution, the idea probably pounded on many doors over the years, always ignored. But about 10 years ago, a pioneering radiologist in Cleveland came up with essentially the same idea. Of course, instead of paper mark-sense forms, the radiologist would click on choices on a screen, and would usually look at the medical image on the computer screen. This enabled the further benefit of reducing the work, and letting doctors easily read images that were taken in various physical locations. Tests showed that doctors using the system were much more productive than those who worked in the traditional way. Finally, they decided that mimicking the radiologist's normal writing style was a negative, and that the field would be improved by having all reports follow a similar format, with content expressed in the same order in the same way. This was actually a detail, because the core semantic observations would be recorded and stored in any case, enabling a leap to a new level of data analytics. It also, by the way, made the report generation system much easier to build than the working prototype we had built decades earlier, which enabled easy customization to mimic each radiologist's style of writing.

The founding radiologist was a doctor, of course, and knew little about software. He did his best to get the software written, got funding, and got the system working. Professional management was hired. My VC group made an investment. Many people saw the potential of the system; it was adopted by a famous hospital system in 2015. But in the end, the company was sold off in pieces.

Nearly 50 years after software was first written that was able to produce medical imaging diagnostic reports quickly and reliably while also populating a coded EMR to enable analytics, the system is sitting in the vast reservoir of un-deployed innovations. It can be built. It saves time. It auto-populates an EMR.

Many people have opined on why this particular venture failed to flourish. It's a classic example of the realities of software innovation and evolution. The reasons for failure were inside the company and outside the company. For the inside reasons, let's just say that the work methods of experienced, professional managers in the software development industry lead to consistently expensive, mediocre results. Nonetheless, the software worked and was in wide production use, delivering the advertised benefits. For the outside reasons, let's say that, well, the conditions weren't quite right just yet for such a transformation of the way doctors work to take place.

The conditions that weren't right just yet for this and uncountable other innovations add up to the walls, high and thick, behind which a reservoir of transformative innovation and "new" software awaits favorable conditions. In other words, the reservoir of innovations wait for that magic combination of software builders who actually know how to build software that works, with a business/social nexus that accepts the innovation instead of the standard no-holds-barred resistance.

Corporations promote what they call innovation. They are busily hiring Chief Innovation Officers, creating innovation incubation centers, hanging posters about the wonders of innovation, etc. etc. They continue to believe the standard-issue garbage that innovation needs to be invented fresh and new.

The reality is that there is a vast reservoir of in-old-vations that are proven and frequently deployed in other domains. All that's needed is to select and implement the best ones. HOWEVER, a Chief Innovation Officer is STILL needed -- to perform the necessary function of identifying and breaking down the human and institutional barriers that have prevented the in-old-vations from being deployed, in many cases preventing roll-out for -- literally! -- decades!!

How We Got Chatbots for Mobile Financial Apps

Chatbots for financial and other applications aren’t just a cool new thing. They’re a necessity! They solve the worsening problem of too many options to choose from on shrinking screens, with unhelpful help screens.

Do you access your financial accounts online? If you do, perhaps you’ll remember that the first time you tried to do something, you had the fun of poring over the menu system to find what button to click, sometimes only to reach another screen full of buttons and menus. On the plus side, online financial systems let you get a lot done. On the minus side, jumping through the hoops to actually get them to do what you want can be a long slog. Have you ever gotten frustrated and tried to click on Help? And gotten the long, unhelpful Help stuff? Helped a whole lot, didn’t it?

Today, nice big screens with high resolution, sitting on a desk somewhere, are used less and less. Cute, portable little screens with phone and camera built in are used more and more, partly because it’s in your pocket, right there when you need it. What happens to all those giant screens packed with menu choices? I guess the designers could have reduced the typeface so much that you’d need a magnifying glass to read them, but they bowed to reality and put just a few menu choices on each screen. Nice to read, but the result is that your multi-screen journey to get to the one you want got even longer. Assuming you remember how to get there. And what if you want something more elaborate, like how close am I getting to hitting my budget for restaurants this month? Fuggedabout it.

The designers of mobile apps for financial applications aren’t between a rock and a hard place. It’s worse. They’re stuck way back in a long, narrow cave that floods. What to do?

Echo and Siri have been training us to just ask for things. Who won the game last night? What’s the weather forecast? Even jokes! But banks … now that’s a different matter altogether. Banks are serious things. There’s money involved. Not just any money – MY money!

So what’s a bank app creator supposed to do?

It’s actually pretty simple, because there’s not much choice. If you want the bank app to be, you know, USED by the people who have it, you have to make it USABLE. Period. There’s not enough room for menus, except maybe a couple super-popular buttons. Help files? Waste of time. You’re down to one choice: make it so you can TALK (or chat) to the app, and ask it to do stuff for you.

That’s the logic. Even better, it’s really happening. In real life!

Bank of America is advertising its chatbot Erica heavily in some parts of the country. The reason is simple: they want their customers to be able to USE the BofA app, not be frustrated by it. There’s the chatbot technology provider Kasisto (Disclosure: Oak HC/FT is an investor) used by multiple banks to power human interactions. The bar has now been raised for financial institutions with apps.

Let’s review some history. Years ago, what financial institutions had to have was a website for customers to access their accounts. As smartphones spread, the bar was raised: OK, you’ve got a website, but do you have an app? Not having an app was a reason for customers to move to a place that did. Just as most of the financial institutions were breathing sighs of relief that they’ve caught up, the bar is raised again: you mean we have to make the app USABLE? What’s a chatbot, anyway? The pattern here is clear: technology keeps marching along, some financial institution applies it to their customers’ and their own benefit, and the others scramble to catch up. What’s next?

To find out what’s next, all we have to do is look at the non-financial domains that use chatbots. For example, look at Amazon’s Echo. It started out pretty primitive, but it’s adding capabilities all the time – as Amazon puts it, new “skills.” The bar is already being raised by technology vendors in two specific, technically challenging areas:

It’s one thing to answer a simple question, like what’s my balance? But real chatting as done by people requires that the chatbot take into account complex questions, context and history.

A complex question requires doing some real “thinking.” For example, “How much did I spend on eating out last month?” has loads of complexity. The system has to know that “eating out” means spending money on the merchant category “restaurants.”  It’s got to find all those transactions that took place in the month before today’s month, add them up and give you the answer.

Taking conversation history and context in account is even trickier. Suppose you get the answer to the eating out question. Suppose you now ask “ How about the prior month?” Easy for a human; for a bot, not easy at all! The bot has to figure out that you’re still talking about eating out (restaurants) and that you want the total for a month. It’s also got to figure out that you want not last month, but the one before that. These are the kind of amazing interactions supported by Kasisto.

Chatbots for financial apps are just being rolled out to solve the otherwise unsolvable problem of screen real estate. Even though we’re still in the early roll-out stages, the next battle is clear. Most of today’s chatbots are in elementary school – soon they’ll need to graduate to middle school!

A slightly different version of this post originally appeared in Forbes.

Here's what we can learn from the shift to smart credit card terminals

I’ve been involved in computer software for decades. Lots has changed over that time. One thing that hasn’t changed is the question people most like to ask me. It’s this: “What do you see that’s new and interesting?” It’s a perfectly reasonable question, though one for which I rarely have a ready-made answer.

A question I never hear goes something like this: “What do you see that’s touted as the newest new thing, but is mostly old stuff, and was completely predictable?” Now that’s an interesting question. And the un-helpful but honest answer is “Practically everything that’s touted as a new thing is mostly old stuff, with a little bit of ‘obvious next step’ thrown in for variety.”

Still, there are some unpredictable aspects of the fancy new things: it’s really hard to know WHEN the new thing will happen and WHO will make it happen.

A case in point is … [I’m not sorry about the pun] … the new smart card terminal company Poynt. (Disclosure: my VC fund, Oak HC/FT, is an investor.) I can see eager marketing people at Poynt are raising their hands in the back at this … ahem … point, all anxious to point (groan…) out that Poynt is a pioneer in the market, arguably the inventor of the smart terminal, an amazing device that not only takes card payments, but also rings up items just like a POS terminal and hosts endless numbers of third-party apps. True! I happily concede the point. But I hasten to point out that there are robust competitors in the market, notably including Square and Clover.

The smart terminal is a new thing, and the market is glad to have it, but it’s hardly a NEW new thing, or something where you’d knock your head and say “now who’d-a thought-a that!?”

It’s natural for consumers of technology to look at the new devices and appreciate them for what they are. That’s like being a tourist, driving on a road through the country-side, appreciating the nice new views. That’s nice for the tourist, but are there patterns here, patterns that would enable an educated person to expect something like a smart terminal to appear, and womdering when it would happen?

Yes there are. The main pattern at work here is the rate of change of the underlying hardware. Today’s hardware is something like 1,000 times faster than much larger, more expensive hardware at the turn of the last century, less than 20 years ago. That number may not seem like much, but think of this: the average human walking speed is about 3 mph. The speed of a commercial jet while flying is less than 600 mph, about 200 times faster. Now imagine a human evolving so quickly that the human could walk at the speed of a jet -- and increase in computer speed is 5 times greater than that, in less than 20 years!

What’s the point? Or Poynt? Here it is: there are underlying geology-like forces in the world of computing that make it highly likely that something very much like Poynt would be invented – though as I said, predicting who will do it and when they’ll do it is a whole other thing.

The first step in creating a technology solution to a problem is often building problem-specific hardware. Then the technology evolves, getting faster, cheaper and more capable. Then there’s a tipping point, at which the purpose-specific hardware is replaced by general-purpose hardware, and most of the specific features of the device are implemented in sofware that runs on the general-purpose hardware. Then a new era begins. The general pattern is that special-purpose devices are supplanted by general-purpose ones.

In the case of card processing technology, first we see imprints of cards made on paper, with the physical paper being sent to a central place for processing. Then the big jump to computer technology and networking: a series of increasingly-better charge terminals, specifically made for processing card charges. The terminals evolved from dial-up networking to the internet, and from stand-alone to connected to a point-of-sale system. Wonderful devices!

Now think about cell phones. If you’ve been around for a little while, you remember big phones getting better and smaller and finally evolving into flip phones. Great phones, … but they’re phones. Then came the big shift, to a next generation of phones that were really small, portable, general-purpose cmputers that can run a myriad of applications … with cell phone hardware and software built in. Yes, it’s a phone. But it runs Facebook, email, and any of thousands of applications avialable in the app store. It’s a “smart phone!”

You know this. The reason I’m reminding you of that history is that it’s exactly the transition that card-charging “terminals” are going through right now – as they become “smart terminals,” i.e., small, portable, general-purpose computers that can run a myriad of applications … with card charging hardware and software built in. Yes, it’s a terminal, but a smart one.

How often do you see “I’m just a phone” devices? Flip phones? Yup! The old card-charge terminal will become just as rare a sight in the next couple of years.

So are the new “smart terminals” new? Yes! But hardly unexpected, at least to those who see the clearly repeating patterns of the underlying technology.

A less Poynted version of this post was previously published at Forbes.

Hospital-based Innovation in Wellness

I was shocked to discover on a recent visit that a giant but innovative local hospital system has implemented a break-through in wellness. They have adapted some of the industry's leading-edge employee wellness techniques and made them work for patients visiting their hospital, thus adding a whole new dimension in the way they make their patients healthy. Much like my previous report of a EMR interchange break-through, it's so radical and unexpected I wouldn't have believed it unless I had experienced it myself.

Employee Wellness

There has been growing recognition that healthy, happy employees are productive and good for business. There has also been growing recognition that being healthy goes way beyond responding effectively when you get sick. People increasingly understand that when you're active, fit, engaged and have good eating habits, you are more likely to be healthy and happy.

There's an amazing Oak HC/FT company that's at the forefront of this movement, Limeade. Here's their summary of what they do:

You can see that they clearly understand the relationship between wellness and health.

Even the picture implies that getting people moving, fit and engaged is a major key to success.

Wellness for patients in the hospital

Hospitals are all about old-style health, i.e., responding effectively when people get sick. But some hospitals are really innovative. I visited one today, and the banner they had proudly hanging in a busy central hallway made their commitment to innovation clear.

I admit I thought their innovations were limited to "just" making sick people better. Hah! They are actually pioneering the application of modern wellness techniques to patients visiting for treatment!

Wellness techniques

I guess it's worth reviewing briefly what some of the most important techniques are. I don't think it's mysterious; most people know what they are:

• Exercise. Without exercise, good things don't happen. You've got to move those muscles!
  
• Heart Rate. Yes, you can lazily move your muscles. But that's not exercise -- you've got to elevate your heart rate, so that key muscle also gets exercise!
• Mental exercise. Particularly as you age, exercising your mind in new ways helps keep you young. But even for young people, learning new things and thinking outside your normal comfort zone can give you a major boost.

Wellness during a hospital visit

It would be one thing for a stodgy old hospital to put up signs that encouraged wellness. No big deal! But that's not what these guys did. The very best techniques are ones that don't feel like a burden. They "trick" you into doing something you might think is fun, and along they way, something good takes place, like wellness in this case. It's called "game-i-fi-cation." And that's exactly what I experienced during the course of a normal, every-day visit for a diagnostic procedure at this amazing hospital.

The game started before I got in the door. I was given the address: right on Fifth Avenue, that can't be too hard. But right away, I couldn't find it! I walked up and down the street, finding addresses that are larger and smaller than the one I had been given, and finally concluded that this numberless entrance was probably the right one.

You might think that this is just someone having trouble finding an address. But it's really the low-key start of the game -- they draw you in slowly. I looked and looked, and there just was no number! In retrospect, the conclusion is obvious: this is the building in which wellness is slyly delivered to improve everyone's health.

I walked in and found myself in a huge open space. Where should I go?

I walked and turned my head as I went and finally noticed the place where it had to be:

This is surely it -- it's clearly labelled cardio-vascular repeatedly, and I was having a heart test. Done. Still clueless about the wellness being delivered to me, I walked in and talked with the nice ladies at the counter. After they determined that I wasn't in the process of dying in front of them, they returned to what they were doing and eventually found out who I was and what I wanted. Oops. I'm in the wrong place. I should return to the giant hall and ask the guard.

Eventually, the helpful guard pointed and gave directions involving walking, turning left and/or right, and going through various doors. Here's the view at this point:

It's a good thing I paid attention, because part of the game is the absence of signs and directions. The theme of finding the right building was intensified once you were inside. And I was beginning to get anxious. While I had left lots of time, this was taking a while, and I didn't want to be late.

I followed the directions carefully and eventually found myself at another counter with friendly people. After identifying myself, I received another set of directions involving things like going straight that way until you get to the grey doors, then go through them and immediately turn right until you get to the end of the hall ... well, leaving out details, I found another counter.

Please pay attention to the pattern here, and notice the clear and obvious relationship to wellness techniques:

• Exercise. Definitely.
• Heart rate. I didn't walk that fast, but those clever people managed to get my heart rate up by inducing anxiety!
• Mental exercise. Definitely. Finding the place was at least as good as a Pokemon search! Not having signs or directions is part of the plan! They're really committed to this wellness thing -- imagine the trouble they took to assure that all the old signs were removed.

Finally I got to what turned out to be the right place:


But needless to say, my adventure wasn't over. What's a visit to a health professional without a good solid dose of papers with minuscule print, the obvious result of welfare work for lawyers and bureaucrats? But I got a break. Whoever designed the system decided that after such a large and unexpected dose of wellness, the patient should be given a light load of paperwork.
It was laughably small.

And to put it in context, dealing with it was a good way to "cool down" after my adventure in exercise, heart rate elevation and mind stretching achieved by next-generation, gamified wellness techniques.

With any luck, other hospitals will copy this amazing innovation. Who knows, maybe some of them already are!

But that's how hospitals are!

Yes, you're right. But it doesn't have to be that way. Retail stores, for example, compete for customers. They compete on multiple dimensions -- product selection, quality and price, but also convenience and overall customer experience. There is no reason why hospitals couldn't pay some moderate amount of attention to the people who are, after all, their paying customers.

Giant, multi-national companies like Ikea, which is many times larger than any hospital, show that it's possible. Ikea puts real effort into creating a good customer experience. Which includes helping customers go where they need to go. They have a mobile app which helps you. They have maps:

And they have signs in the stores, even on the floor and hanging from the ceiling:

Hospitals aren't too big. Their executives are not under-paid. They just have to care.

Investing in Healthcare Innovation

There is a clear spectrum of innovation in healthcare. I've described the spectrum here, ranging from simple, blocking-and-tackling at one end to exotic AI-related things at the other end, with smart, data-driven ventures in the middle. The exotic end of the spectrum gets most of the money and attention, while the simple end is largely ignored. The middle of the spectrum is occupied by smart, data-driven people who see a problem in the way healthcare works today, and build here-and-now solutions to make it better. Even though there are sometimes structural obstacles to overcome, these entrepreneurs find ways to work with the system and overcome the obstacles, because their solutions benefit everyone involved: payers, providers and above all patients.

Oak HC/FT invests in this kind of middle-spectrum venture, ventures that are bold and smart, but also practical with right-now benefits. Here are a couple of examples.

Aspire Health    

Aspire Health uses analytics to identify patients who may be approaching the end of their lives, often as shown by increasingly serious health problems. In the normal course of events, these patients would spend an increasing fraction of their time bouncing from one facility to another, each provider doing his or her best, but each acting in completely isolated silos. With Aspire, the patient has the opportunity to have a dedicated care team that meets with them and their families, understands their situation and their desires, and takes charge of each aspect of their care from that point on, making adjustments as required. The Aspire team is a true, takes-charge primary care team, assuring that your needs are met. Typically, patients spend more time at home and less time in hospitals and ER's. The result is that patients and their families are much happier and less stressed, with a primary care team that takes responsibility and gets things done.

Limeade    

At first glance, Limeade may not seem like a healthcare company. But what else would you call a company that works with a group of employees to encourage them to eat, exercise and generally act in ways that promote health? While many diseases just happen to people regardless of their actions, many others either start or are exacerbated by behaviors. Limeade applies analytics and smart technology to identify, support and promote healthy behavior. Result? Employees that are not only healthier, but happier and more productive. Everyone wins.

 Quartet Health    

Quartet Health applies analytics to identify people who have behavioral problems and significant non-behavioral health problems that could be interacting with each other to make things worse for the patient. They pay special attention to these patients, and apply an evolving set of automated tools and human intervention to understand the interaction among the issues. In particular, they identify particular combinations for which intervention can make things better for the patient, and then guide the concerned parties to take the actions that will lead to a better outcome, involving the patient and care providers as appropriate. Net result: patients get healthier than they otherwise would have been. And by the way, costs are lower.

VillageMD    

When you have a problem, the first person you're supposed to see is your primary care doctor. Founded by visionary, award-winning Dr. Clive Fields, VillageMD has done extensive longitudinal analytics on patient outcomes, and discovered things the primary care physician can do to improve care while reducing costs by an average of over 20%. Having proven the methods in their own practice, VillageMD is now delivering their techniques to other primary care practices in a highly systematic, targeted way. Everyone wins: payers pay less, patients are healthier, and primary care doctors have greater impact and make more money. The VillageMD techniques are evolving and becoming more powerful with additional experience.

Conclusion

Oak HC/FT has invested in excellent companies in healthcare. The four companies briefly described here are particularly good examples companies in the center of the "simple-to-exotic spectrum" that I have described. These companies deliver here-and-now results using advanced but non-exotic technology combined with win-win business models. Companies like this that use "big data" in practical ways are out of fashion in the world of healthcare IT investment for reasons that are a mystery to me. All I can say is that they're all the rage in the world of Oak HC/FT.

 

The Healthcare Innovation Spectrum: From Washing Hands to AI

There's a spectrum of ways to innovate in healthcare. On one end is simple stuff, like making sure things are clean and germ-free. On the other end is exotic stuff, like using AI: Artificial Intelligence and Cognitive Computing. Obvious questions: (1) where is the money going? (2) where is the value? (3) Is the money going where the value is? Simple answer: the "smart" money is going to exotic gee-gaws, ignoring near-term value and patient health.

Where the Money is going

The money is clearly going to exotica. Ignoring for the moment the billions IBM and others are pouring into what they call Cognitive Computing, VC's are investing heavily in healthcare-directed AI. See this:

We're talking serious money here:

While there are loads of conferences, trials, talks and articles talking about the great future here, there is an obvious conclusion to be drawn: while the money is being spent now, the benefits (if any) are in the future.

That's about all you need to say about it.

The middle of the spectrum

While things like AI are clearly at one far end of the spectrum of healthcare innovation, there are intelligent, educated things in the middle of spectrum. Lots of people are pursuing these innovations with great energy. I've discussed an example of one such approach here.

The Oak HC/FT portfolio company VillageMD is another clear example of data-driven innovation in healthcare. No new math or fancy computers are required. "Just" educated, dedicated people looking at the data and making required behavioral changes based on those facts. The founder of VillageMD, Clive Fields, just won a major award for his work, using all-organic and natural intelligence -- no artificial ingredients! Guess what: it's here and now! The outcomes of real patients are being improved as you read this!

The basic end of the spectrum

On the other end of the spectrum from AI, we've got things that shouldn't need "innovation." They should be standard practice. They have huge impact. They are the shocking, scandalous modern equivalent of antiseptic surgery -- things that no one seriously disagrees with, but which the important experts and leadership type people somehow can't lower themselves to pay serious attention to. Or when they pay attention, it's with actions that do nothing to solve the problems.

A good candidate for the poster child of this end of the spectrum is what the CDC calls healthcare-associated infections, HAI's. In other words, getting sick from going to the hospital. Here is the CDC's summary of the situation:

I don't know about you, but this makes me sick. 75,000 preventable deaths in a year, preventable using non-exotic methods. No Cognitive Computing required! There are cures, demonstrated at multiple hospitals that have put serious effort into it. This article summarizes the efforts and approaches, ranging from simple changes of cleaning practices to fancy new machines.

Conclusion

There's a clear spectrum of innovation in healthcare, ranging from blocking-and-tackling basics at one end, to exotic new things based on various forms of Artificial Intelligence at the other end, with smart, non-exotic, data-driven methods occupying the middle ground. Most of the "smart" money appears to be going to the fancy exotic end, with results sometime in the indefinite future, while the rest of the spectrum trundles along, largely under the radar, delivering results to patients today.

Healthcare Innovation: Can Big Data and Cognitive Computing Deliver It?

Most people seem to agree that healthcare is ripe for innovation, and badly needs it. Lots of people are talking up two potential sources for that innovation: Big Data and Cognitive Computing.

I'm strongly in favor of data, the bigger the better. But is the Big Data movement going to make a difference? I'm strongly in favor of cognition, computing, and computing that is smarter rather than dumber. But is the Cognitive Computing movement likely to make a difference? Here's a summary of some thoughts.

Process Automation and continuous improvement

Here is a description of the core process automation process implemented by a company I've invested in, Candescent Health. It describes the process that can and should be applied to all of health care.

The point isn’t that there’s data and analytics – the point is that there’s a closed-loop process of continuous improvement where actions are based on rules. This is the framework that is required to make anything happen. Without it, you can’t put your proposed new clinical action into practice with double-blind A-B test and see if the results of your analytics actually deliver benefits in the real world! Or even just deploy it!

How about just making the basics work?

Here is the story illustrated by Mt Sinai hospital about how everyone focuses on “innovation” and fancy new things, when just having the computer systems run reliability has a huge impact on patients – and unless those systems run, the results of fancy new analytics can’t be delivered to benefit patients.

If the car won't start or run reliably, who cares how good the fancy sound and navigation systems are?

How about making the computers work?

I love data and analytics. But doesn’t it make sense to focus on getting the operational computer systems to actually run well before moving on to the fancy stuff?

Paying top dollar for computers doesn't make them work

In fact, just about anything you do with healthcare data that is going to be brought to the front line of care requires functioning computer systems to be able to pull off – the big healthcare systems pay Greenwich CT prices and get trailer park results.

Clean data isn't easy to get

Both data warehousing and the fancy new Big Data movement share the under-appreciated problem of getting good quality data in analytics-ready form. Sounds simple, but the difficulties make progress a grinding crawl on many efforts. See this for example.

Big data sets tend to have Big problems

Massive data sets have built-in problems that make it hard to get actionable results.

AI: How about under-promise and over-deliver for a change?

Skepticism about Cognitive Computing in health care is warranted. There is a rich history of over-promise and under-deliver for AI efforts in general.

Real-world solutions waiting to be automated

Meanwhile, there are proven gems in the medical literature just waiting to be disseminated to the front lines of health care via point-of-care computer systems that are languishing in journals.

What can make a difference?

There are lots of practical, tangible ways to make things better, in spite of all the obstacles to change pervading our healthcare system. Here are some examples of people doing the right thing, all them with investments by Oak HC/FT:

• Candescent delivers better imaging results with less expense by applying basic continuous-improvement workflow automation.
• VillageMD delivers better results with lower cost by feeding back results and advice to PCP’s.
• Aspire delivers better results at lower cost for end of life – by having one person be in charge, managing everything from the patient point of view.
• Quartet makes a difference by applying behavior health as needed to help other conditions.

These companies embody some common themes:

• Knock down the silos, have a patient-experience-centric point of view.
• Applying common sense has huge benefits.
• Focus on delivering results to the front line (patient) is hard but necessary.
• A system of continuous learning and delivery is a pre-condition to delivering any results of analytics for patient benefit.

Conclusion

The big hot topics in healthcare of Big Data and Cognitive Computing are little more than fashion statements. Data, of course, is a good thing; so is having computers do smart things. But without doing some basic blocking-and-tackling and applying some practical common sense, a great deal of time, money and energy will be spent accomplishing nothing.

Fintech Business Strategies

The business strategies employed in fintech aren't much different from general tech strategies, and they all leverage the fundamental drivers of innovation that drive all the tech in fintech.

Behind every fintech business strategy are a few simple principles:

• Eliminate places
• Eliminate people
• Eliminate things
• Reduce time
• Reduce cost

Every fintech business strategy is a specific implementation of technology that employs some combination of the principles above.

The leading strategies include:

Expand the pool of consumers/users

Who would have thought that people who operate web sites are good candidates for loans? But like many other businesses, they have to pay suppliers (like data centers) promptly to avoid getting cut off, while their customers (like advertisers) aren't as prompt as they might be in paying their bills. Rapidly growing Fastpay meets this need in a sophisticated, integrated way that includes lending money, but goes way beyond just lending money. For example, here's one of their latest services:

Expand the pool of producers/providers/sellers

What if you sell stuff at the local farmer's market? People keep coming up to you wanting to buy your produce. They don't have enough cash, but they have a credit card. What if you could accept their money without going through the nightmare of expense, hassle and non-portable devices regular stores put up with? Enter Square, whose little device and app turn the smartphone you probably already have into a POS terminal and card acceptance device:

Apply technology to make an existing service: better, faster, cheaper

Lots of fintech is direct to consumer, but important fintech companies operate completely behind the scenes, largely invisible to normal consumers. An exciting company that has a new, machine-learning-based approach to credit card fraud prevention is a good example. By doing a far better job of preventing fraud, Feedzai reduces the cost of providing credit card services dramatically. Here's one way they express the issue:

Replace and enhance existing technology

Sometimes, the innovating fintech company is able to completely replace a legacy product. This is ambitious and difficult to pull off, but the rewards are great if you can do it. Everyone is familiar with point-of-sale (POS) terminals and credit card charge terminals that are normally separate devices. For example, here's a card terminal at my local pharmacy:

Poynt has invented a single device that replaces the card terminal the consumer uses to swipe or insert their card as pictured above, but also POS terminal used by the retail clerk. Here it is:

Cut out a middle-man (disintermediation)

This has been a favorite business strategy since long before 1-800 Flowers started cutting out local florists. It's alive and well in fintech-land. A good example is Insureon, which uses the web to attract very specific groups of small business people and sell them insurance that is completely directed to and appropriate for their needs. For example, how many local insurance offices do you suppose cater specifically to the needs and perspectives of dog walkers?

There are hundreds of fintech companies working to extend and disrupt the financial services industry. The business strategies described above are typical, but not exhaustive; and some companies pursue combinations of them. (Note: all the companies used as examples except Square are ones in which Oak HC/FT has an investment.)