The Black Liszt

How Elon Musk can implement Payments in Twitter

Elon Musk has talked about implementing payments in Twitter, implementing his full vision for X.com, the company that with narrower focus became Paypal. It’s an excellent idea. But the implementation path has major obstacles he’ll have to overcome.

I have personally seen multiple efforts by tech teams inside and outside the fintech industry get hopelessly stuck in the swamps trying to implement “simple” things like prepaid debit cards, or even “just” rewriting their software using tools less than forty years old. I have also watched with amusement as the obsession with crypto technology continues to inspire major projects doomed to fail at the Fed, exchanges and a variety of prestigious banks, not to mention countless startups. I trust Mr. Musk will continue his results-driven practice by ignoring all this and drive towards a successful implementation of Twitter payments and banking.

The Huge Benefit of Starting with Twitter

Most companies you might start are classic one-to-many companies, i.e., there’s one of you selling to (you hope) many customers. There’s another kind of company, many-to-many. The company is in the middle, attempting to match many people who want something with many people who have something. E-Bay is a classic example of this kind of business, which is sometimes called a “network effect” business. These are really hard to start, because buyers want to go to a place where there’s lots for sale, and sellers only want to go to places where there are lots of buyers. How do you get such a thing started from scratch? It’s hard!

Here's the good news: Twitter is somewhat like a one-to-many business like publishing today. But because it doesn’t itself make what it sells like a classic newspaper does, it’s really a many-to-many business like Facebook, with readers using it because they like the writers they can find and follow on it, and writers using it because there is a large potential audience of readers a.k.a.followers. It has well over 300 million active monthly users!

This is good news because the main barrier of a P2P financial business – getting a large base of users with your app – has already been met and overcome.

The Danger of Typical Technology Methods

I’ve been privileged to spend a couple decades meeting and interacting with smart, accomplished software people who are doing new things. I’ve learned a great deal, both models that work and tendencies to avoid. I’ve written the observations in multiple books.

Banking built on Twitter should be a pretty simple application. But experienced people want to apply their often-inappropriate experience to building the software, and smart people too often want to prove how smart they are by following the latest tech fashions. In either case, the application suddenly becomes really hard to build.

In addition to my tech investing experience, I know the difficulties from personal experience in the world of credit card software. The world of existing issuing and acquiring systems is incredibly complex, and most attempts to build new ones fail. The experts can’t imagine anything different, and the newbies blast ahead with their wonderful modern ideas building great-sounding systems that don’t work. It’s a solvable problem, but the rare combination of in-depth knowledge of the existing systems and the ability to use methods that are modern, powerful and unfashionable is required.

Here are some of the main issues.

Modern people will want to assure that the system is “scalable” by using ridiculous things like microservices.

Modern people will want to leverage the powerful new blockchain technology – something that is amazing for Bitcoin but thousands of times worse than anything else for corporate-controlled systems.

Traditional people will want everything centered on exclusively on DBMS, avoiding powerful tools like document-centered and memory storage to enhance the solution.

Smart people tend to be highly language-focused, instead of meta-data and abstraction oriented.

Nearly everyone will want a great set of requirements and a plan for how to meet them. The trouble is you can never know all the real-world requirements of a system you have yet to build! So an approach based on post-hoc design works best.

Recognize that "building" is the wrong metaphor for creating software; the reality is that software is mostly changed. A whole set of powerful methods for building high-quality software quickly with maximum speed emerges from this.

I hope Mr. Musk builds his new banking system. The efficiency and low cost of such a system would be great. I hope he takes the path of using simple, non-fashion-driven tools and a grow-the-baby, post-hoc-design method to build his solution from birth through adolescence into full functioning adulthood. I look forward to it!

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.

Are Cashless Payments a Good Thing?

Cashless payments have been around for quite a while in the form of checks and credit cards. But even while physical cards are being re-issued with chips, there is huge innovation going on to bypass checks and cards, and go all-electronic. There is Apple Pay, remote sensing, QR codes and more. Lots of people are excited about a new wave of consumer convenience, most of it based on credit card technology. It's all good!

Is it really ALL good? You never seem to hear about the downside of all this new technology. But there's actually lots of downside. Payments are a huge field. So I'm going to just point out a couple issues with credit cards, the basis of most cashless payments by consumers.

The Volumes

Let's start by getting a sense of how much money we're talking about. Look at this:


That's more than $5 TRILLION dollars in 2016 just in the US. How much is that? Well, for starters, it's more than the entire US Federal budget!! In fact, it's over a quarter of the US GDP.

Merchant Discount rates

This is an incredibly important subject -- of which most consumers are blissfully unaware. The concept is simple. Suppose you buy something from a merchant for $100. When you pay cash, the merchant ends up with ... $100! what a shocking concept! Well, it is shocking when you realize that when you pay with a credit card, the merchant will end up with less than $98, and often less than $97.

That may not sound like a big deal. But what if you're running on thin margins, like many merchants, and are under intense competitive pressure to cut prices, have sale prices, etc. You can be doing well to end up with an overall margin of 10% on your business. This means that collecting $97 instead of the full $100 amounts to a margin reduction of ... 30 percent!

Merchants grit their teeth and take it. They have no choice. The giants don't care. But next time you go to a small shop or farmer's market and see a sign that says "cash only, please," you'll understand.

This is a complicated subject, since the discount rate includes more than a dozen charges for various things, the largest of which is called "interchange." But it easy to boil it down. Consumers put about $5 trillion on their cards, which they have to pay -- undiscounted. The discount is about 2-3%, which is between $100 and $150 BILLION dollars. A year.

Some combination of issuing banks, acquiring banks and networks (like VISA) are splitting that money.

Credit card debt

Leading thinkers in payments love to talk about how consumer-centric they are, and how they're investing heavily to make using cards even more convenient for consumers. Aren't they just wonderful people?

The magic of credit cards is that when you want to buy something, and you use a card to buy it, you can get what you want even if you can't afford it. Great concept, huh?

Consumers in the US alone are burdened with about $1 TRILLION dollars of credit card debt. And it's not rich people who have the debt. It is overwhelmingly people with little to no net worth.

What's worse is what they pay. Today's average of interest rates is around 19%. It hasn't been less than 15% in quite a while. So that means that consumers have to pay well over $150 BILLION dollars a year just to keep the debt current, not counting paying it off. Almost all that money goes to the banks that issue the cards, though various lawyers and collection agencies get their cut. 

What cards are worth

None of the institutions involved with cards care a whit about whether the card is physical or electronic. What they care about is protecting and preserving their more than $250 BILLION dollars a year in revenue. How much is that? Here is an excerpt of a list of the world's governments' revenue:

As you can see, the revenue up for grabs with today's credit cards in the US is about the same as the revenues of the government of Sweden, 15th out of more than 190 countries. Card revenues are worth, well, quite a bit...

What this means

What all this means is simple: the companies involved are living high on this revenue, and will go to great lengths to protect it. They will spend big on fancy consumer features -- so long as those features are based on the card infrastructure.

Why this matters is that there are incredibly inexpensive and fast methods of transferring money that are not based on the card infrastructure. Some of these are in widespread use outside the US. In the US, the least expensive such infrastructure in wide use is ACH, which is a government-run electronic form of checks.

There is no technical barrier to upgrading this network to be near-instantaneous. Technically, it could have been near-instant 20 years ago! But no one involved felt like doing it somehow, and even today, it's getting faster at an incredibly slow pace.

There are a couple modern peer-to-peer payment systems that are based on this technology. While they're growing, they are tiny compared to card volumes, and the card insiders are working madly to control and contain this threat to their massive revenues.

Conclusion

I'm strongly in favor of consumer convenience. My goal here is simply to point out that the convenience comes at an astounding cost that is hidden from consumers who don't carry a balance on their cards, and is a constant temptation and on-going burden to those who can't afford debt, particularly the expensive kind you get with revolving credit cards.

Fintech: pay before, pay now, pay later

Cards, cards, cards – they all have the same shape and size, they all have a mag stripe on back, similar numbers and date and your name on the front. How different can they be?

I admit, they all have this in common: you can use them to get stuff. Instead of cash. That’s a lot to have in common! But the next level deeper, they can be so different, it’s amazing.

One of the biggest differences is when you pay for the stuff you get with the card. All cards fall into one of three buckets in terms of the “when do I pay” question. The buckets are:

• Pay before. Put money onto the card. Then you can spend it. No money on card, no stuff.
• Pay now. Use the card. Is there money in your account? You get your stuff. The money will be pulled within a day.
• Pay later. Use the card, if you haven’t used it “too much” recently. You’ll eventually have to pay, with interest if you take your time.

Pay before

In the industry, this is usually called a prepaid debit card. There are two categories of these.

The first kind is usually called a gift card. You can see these hanging on racks near the check-out counters of stores.

As you can see, each is worth a certain amount of money at a given retail store.

Some retailers have gotten pretty creative about their card designs, like this one that is supposed to be for millennials:

What if you just want to give money? Now you can do that. For example:

The second kind of pay-before card is usually called a prepaid debit card, but the key thing about it is that it’s rechargeable, which means you can put more money onto any time and use it for any purpose.

Prepaid debit cards normally have substantial fees associated with using them, because it’s the only way the companies doing the processing can make money. They’re not associated with a bank account, so they’re perfect for people who don’t have one.

Of course, technology being what it is, companies have evolved these categories -- some gift cards are now rechargeable.

The key thing to understand is that pre-paid debit cards are “debit cards” for technical purposes – but they’re NOT associated with a bank account. That’s what makes them easy to buy. Whether they have a bank account or not, people can use pre-paid debit cards anyplace cards are accepted.

Pay now

The industry calls this a debit card. It’s tied to a bank account. When you use the card, you debit the account, which is normally a checking account.

Depending on how you use the card, the actions and underlying technology can be quite different. If you just tell the system it’s a card, it will usually be treated as an off-line debit card, and the charge will be made to your account overnight. If you admit it’s a debit card, you’ll be asked to enter the PIN, just as though you were at an ATM. This is usually called PIN debit. The money is immediately removed from your account.

Pay later

Finally we get to the credit card. It’s a credit card because when you use it, someone is giving you credit for the amount you charge, and will expect you to pay the money back. You are welcome (sometimes encouraged) not to pay on time, because then you have to pay interest on the loan you’ve taken out. Unlike a home mortgage, which is “fixed” credit, the credit card is “revolving” credit, because the money going in and out is like going through a revolving door.

When you swipe a debit card, your bank account is checked to see if there’s enough money to cover the charge or the cash. When you swipe a credit card, your revolving credit line is checked to see if there’s enough credit to loan you enough money to cover the proposed purchase.

Summary

Using a card always involves money. But whether the money is already out of your pocket, comes out when you use the card, or whether it’s a bill you’ll eventually have to pay makes a large difference to you, which is reflected in a large difference in what happens behind the scenes.

Fintech: the world of cash cards

Cards. Love them or hate them, they are a key part of our lives in general, and of the fintech part of our lives in particular. If you're going to understand fintech, it's important to understand at least the basics of what goes on behind the scenes with cards. Not to mention knowing the proper terminology to use.

You spot a bank. You know you're getting low on cash. You stop and walk up to the machine. You pull out your wallet, and here comes your first choice: which card should I use? You probably don't think much about it, since there's probably a card you're used to using. But what makes your "cash card" special is that you know the four digit PIN code that stands between you and your cash.

But they're not all the same. The most basic and original kind of card is an ATM card. Here's an example:

Notice that the card doesn't say VISA or Mastercard on it. It's an ATM card, which is different! You put the card into the machine:

Then you enter your PIN code on the keypad. The ATM machine is actually connected, perhaps through a series of networks (more on them later) to your bank's computers. The first few of the card numbers identify your bank. Your bank gets the request and checks your balance. If you have enough money in your account, the bank immediately debits your balance, and sends an approval of the withdrawal request to the ATM machine. The machine then counts out your money and you take it:

You take your card and the transaction is complete, just as though you had been with a human teller.

The original ATM machines were connected to the same computers in roughly the same way as human tellers. Each bank had its own ATM machines, just as each one had its own branches.

Shortly after ATM's were deployed, the first interbank network was rolled out. One of the first was NYCE.

NYCE was developed and owned by a set of New York banks to enable a customer of any bank in the network to use any ATM that was also in the network. There are now over 300,000 ATM's in the NYCE network, and a whole lot more in the other networks that sprang up to compete with them.

If you pull out that bank card and try to pay for a meal in a restaurant with it, things aren't going to end well. But banks want you to use their cards! So after some time, the debit card was introduced. It looks nearly the same as an ATM card, but not quite. Here's mine, for example:

It has the same general appearance and numbering scheme as the ATM card pictured above, and the same "good thru" date. But it says VISA, and it says "DEBIT," which makes it different!

One way to think of it is a card with two hats on.

Hat number 1, the DEBIT hat: it's an ATM card. You put it in an ATM machine, enter your PIN, get your cash and away you go.

Hat number 2: the VISA hat: it's a "credit card." You hand it to the cashier at the store, it's swiped, and you walk away with your stuff. Except that, instead of being a separate account, the card is associated with your normal bank account, and the money comes directly out of your checking account. No talk of "minimum payments," it just comes out. Hope you've got enough there! Because it's not a "credit" card -- it's, sneakily, a kind of cash card!

That's enough about cards for one sitting. But I hope you can see a theme emerging: there are a number of systems designed and built for different purposes that can be accessed by cards that look and act much the same ... except the underlying technology is completely different.

 

Fintech: Whose rails are you using?

Like most specialist areas, fintech has its own vocabulary, highly meaningful to insiders but opaque to outsiders. The important issue of “whose rails are you running on” is one of those phrases.

Insider vocabulary

Some people learn to play a bit of baseball when they're young:

They learn basic vocabulary like bat, ball, base, hit, grounder, fly ball, catch, throw and out. That's already lots of words! But the pros and other people who are seriously into what the rest of us lightly call the "game" share a greatly extended set of concepts, words and phrases among themselves.

To take a couple simple examples, there is the “squeeze play” and the “infield fly rule.” Those are just a couple I happen to know. I'm sure there are whole piles of words and phrases that are way beyond me. It’s the use of that specialized vocabulary that distinguishes people who “know baseball” from those who merely watch or attend games.

Fintech is no different. There is some shared vocabulary among all the sectors of fintech. In addition, each specific fintech domain has its own set of vocabulary and concepts, arranged in layers according to how deep you are into the business or technology of the specific fintech sector.

Rails in fintech

When someone talks about "rails" in the payments sector of fintech, they're talking about the mechanics of how money gets from a sender to a receiver. It's about transportation.

It turns out that the image of "rails" is appropriate. Rails are what trains run on. They're big, elaborate, fairly expensive things that were developed a long time ago, and have been somewhat modernized. They constitute a system that is completely separate from and incompatible with the larger and more recent system of roads used for trucks and cars.

Even though railroads started decades before the modern road system, they are a valuable part of today's transportation system. They handle certain kinds of transportation far more efficiently than road-based trucks could.

Same thing with financial-system "rails." They are robust, reliable and cost-effective for the kinds of "cargo" they transport (financial transactions), and not about to be replaced any time soon.

Layers of competing technology

Fintech's technology of "rails" is not only separate from the "normal" way data moves around today, it's remarkably complicated. You may think it's pretty simple, just like I did. It's just adding and subtracting, and every once in a while you multiply to calculate interest; how bad can it be? Reality educated me; I was wrong, big time! And what's worse, there is more than one system of rails!

Let's take one rail system as an example: the ATM network, a.k.a. cash machines.

These are older than you might think. Here's what an early cash machine looked like in 1967:

It was just a railroad car; no network yet, and therefore no "rails." But a true networked version soon followed. It's not easy to find out the historic details, but it appears to have happened early in the 1970's:

The ATM network is just one set of rails. The credit card network has a rather different set, which is completely incompatible. Which is why, among other reasons, the choice of rails is important!

Conclusion

The "rails" used by financial transactions in fintech are deep, robust, incredibly complicated and separate down to the roots from most modern technology. The chance that a bunch of bright kids in a garage is going to come up with a bright idea and replace it next year is zero. And there's more than one rail system to use!

That's why one of the most important questions for payments in fintech is "whose rails are you using?" Even if you have no clue how those rails are built, and few people do, the answer is nonetheless important.

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.)

 

Fintech Innovation: the Drivers

What are the underlying principles driving innovation in Fintech? The same, identical, unchanged principles that drive innovation of tech in general. Nothing new! It's just applied to Fin.

We all know what those principles are. We've become used to them as wave after wave of improvement washes over the devices and services we use every day. In normal, physical things the changes are dramatic, going from literally using horse-power:

To steam-powered engines on rails:

To jet-powered planes that fly us in the sky:

Compared to last year, things get: faster, cheaper and better. That's it!

But when electronics and software are involved, as they are in most of fintech, the rate of change is even greater. That's why most of the disruptive changes we see in fintech are little more than fintech catching up with other sectors that have pioneered and are already using the technology. Like for example, "gee, people seem to like their smartphones. There are an awful lot of them out there. Maybe we could figure out a way to put the tech in fintech; maybe build an app or something?!"

What this amounts to is simple in principle, though often challenging in practice.

So how does faster, cheaper, smaller/better underlie innovation in fintech? The main ways include:

Replace physical objects large and small with digital

This reduces cost and speeds things up.

Devices instead of places

Don't go to a place (which is expensive to build/buy, maintain and staff), use your computing device. Call up the specific banking-like function you want on your device, and get your job done, right now. Consumers prefer it because it eliminates travel time, wait time and hassle. For example, money transfer with Abra:

Replace people with data and algorithms

This reduces cost and speeds things up. Consumers prefer it for the same reason they'll use an ATM even when human tellers are available. Don't make an appointment, get dressed up and see the loan officer at the bank; enter your data in the loan app and get an immediate decision with immediate access to the money. For example:

Summary:

• Use the device you have to get the job done.
• Eliminate the man in the middle; no people.
• Digital beginning, middle and end.

Result: faster, cheaper, better!

 

What is Fintech?

Fintech. It’s new. It’s big. Everyone wants a piece of it. Better jump into Fintech before it’s too late!

But what is Fintech? Fin? Like the thing that fish have? That Chinese people put in soup for special occasions?

No, it’s not the technology behind Shark’s Fin soup.

It’s “Financial” Technology.

Oh, that’s simple. Fintech is about money! I know what that is!

But since when is money new? And everyone has always wanted a piece of it. So “Fintech” can’t just be money, if it’s both “hot” and “new.”

And it’s not. “Fintech” is the name for a diverse collection of companies and products, each of which applies modern technology to some aspect of financial transactions.

Phew! That’s quite a mouthful! And kind of vague. So let’s dig in and see what’s really going on.

Fintech and real people

First of all, let’s be clear that the vast majority of people, normal people, not only don’t know what “fintech” is, they don’t need to know and don’t really want to know. Who feels the urgent need to replace their old “fin” with a fancy new “fintech,” after all?

Just think about phones. Maybe you didn’t envy the people who walked around with giant phones with antennae sticking out of them yakking away on the street.

But the second you saw someone pulling a ringing flip phone out of their pocket and answering the call, you probably felt the urge. And then when the iPhone came out? There were lines snaking out of Apple stores. Everyone knew what phones were and how they worked, and all you had to do was see one of the fancy new ones to want one.

“Fintech” is something investors, market analysts and corporate types in financial companies think and talk about. Regular people? Nope. It’s more like "energy sector” or “entertainment;” it’s a group term that covers lots of stuff that’s only vaguely related. For example, here’s a graphic that shows one group’s view of fintech:

And then when you dive into just one sector of Fintech, Bitcoin/Blockchain, here’s something that shows the intense interest of big corporate players in it:

Over 70 strategic investors have put real, old-fashioned money into these startups!

Conferences

Let's talk about conferences. Big ones. That, as a normal, everyday kind of person, you've probably never heard of.

Like Money20/20, an amazing conference that started just four years ago, whose subject is "disruptive ways in which consumers and businesses manage, spend and borrow money."

Another conference is smaller, but is all about people who invest in the kinds of small companies at Money20/20:

And who among us "normal" people might have even gotten close to the right answer to the question, "How much money was invested in Fintech companies last year?"

Perhaps now you're starting to get a sense of why Fintech is new ... and why it's hot, hot, HOT!!

 

What E-mail teaches us about Bitcoin and Block Chain

E-mail is widely used, and everyone knows what it is. Bitcoin is a hot new techno-bauble, and Bitcoin technologies like block chain are getting lots of attention and money. It turns out that e-mail has a great deal to teach us about Bitcoin and its technologies. Here’s the punch line: in spite of its ubiquity, practically no one understands how e-mail works, and this causes huge errors with practical consequences! By comparison, Bitcoin and its spawn are incredibly complicated;  most of the people who do understand e-mail have little chance of understanding Bitcoin. Think about the consequences of this, please.

Do You Know How E-mail works?

E-mail is simple, right? You login to your e-mail account, fill out the To and Subject fields, maybe add a couple people in the CC field, write your e-mail, and press send. Then some magic happens, and the e-mail shows up in the in-boxes of the people to whom you sent it. You can read your own e-mail by looking at the items in your in-box, and even go to your sent-mail folder and look at what you sent. It’s simple, wonderful and true! For the vast majority of the time, it’s fine to leave “then some magic happens” alone.

The trouble comes when trouble comes, i.e., when there’s some special circumstance that requires knowing something about how that “magic” in the middle works. That’s when it comes out that almost no one has a clue about what’s going on, even in something as simple and ubiquitous as e-mail.

The IRS e-mail case

There are lots of examples, but the issues involving e-mail at the IRS which have been in the news off and on for the last couple of years are a good case in point. Here’s the lead paragraph from Wikipedia on the subject:

Now, remember – I’m not talking about the merits of the issue on one side or the other. I’m solely talking about the knowledge exhibited of how e-mail works, and the practical consequences of that knowledge. Read this juicy lead from an AP story on the subject:

Here are the key points:

• In June 2011, Lois Lerner’s computer crashed.
• This resulted “in the loss of records”
• It was determined that the records on the hard drive, i.e., Lois Lerner's emails, were gone forever

I am aghast. Agog. At a loss for words. I’d like to be shocked at the “depth” of misunderstanding, but I think it’s more appropriate to be shocked at the “shallowness” of misunderstanding exhibited in this quote, and in the heads of all the IRS employees, FBI, Congressional staffers, the archivists, and all the journalists with their fancy degrees from fancy schools.

Here is the core concept that everyone involved on every side seems to agree on:

The e-mails Lois Lerner wrote are uniquely stored on the hard drive of her personal computer. If it is true that the hard drive is severely damaged, then the e-mails are “gone forever.”

The simple thing

Even from the simplistic view of how e-mail works, every e-mail is either a draft or is sent to someone. If it's been given an accurate address, it arrives. It's in the receiver's in-box, and perhaps eventually in their deleted mail folder. Since the issue involved e-mails not only received by Ms. Lerner, but ones sent by her, presumably to other IRS employees, there is an obvious strategy: do a search on the e-mail of every IRS employee to whom Ms. Lerner could have sent an e-mail, and see if she did send one. It's the magic of e-mail: the sender has a copy of what was sent, and the recipient has a copy of what was received. There are at least two copies: both sender and receiver have one!

Have you ever read that simple thought anywhere else? Neither have I.

The "deep" thing, requiring understanding of how it works

Now we get to the real point. An e-mail address has two main parts: the name, and the domain. The name is the part before the @ and the domain is the part after the @, for example [email protected]. Similarly, all e-mail systems have two main pieces of software involved: a client and a server. Software by Microsoft is widely used in governments and corporations. Outlook is the client software, which runs on the computer on which you read and write e-mails. Exchange is the server software, which runs in a data center somewhere. Exchange is a program with a database holding the e-mails, address books and calendars for a whole bunch of users. A domain like IRS.gov is implemented with many Exchange servers, each with the e-mails of a particular collection of IRS workers, typically a couple for each physical location.

When Ms. Lerner wrote an e-mail, she used her computer running an e-mail client such an Outlook. When she hit the Send button, the e-mail immediately went to her Exchange server, which filed it away. It then found the Exchange server(s) of the recipient(s) and passed the e-mail to it (them), which it turn sent it to the user's Outlook clients. Shortly after Ms. Lerner sent an e-mail to her colleague Mr. Lowe, it was stored in no less than four places, including a couple servers. In addition, assuming the government had at least moderately responsible Exchange administration, the e-mails were further copied to replicas, on and off-site, and in addition periodically backed up to yet another medium and location.

There are other e-mail clients and other e-mail servers. I have no information about what the IRS actually used. But this is how e-mail works! There are clients. There are servers, which serve a number of users/clients. When a human writes an e-mail, it goes from her client to her server to the recipient's server to the recipient's client. As as result, it should have made no difference whatsoever that Ms. Lerner's computer "crashed." It wouldn't matter if it suddenly grew wings and flew off to Tahiti to frolic in the waves. Any e-mails that Ms. Lerner wrote were securely stored on her e-mail server shared with other users and in a data center, and on multiple replicas, backups and disaster recovery sites.

The fact that Ms. Lerner's computer crashed and people supposedly spent time attempting to recover e-mails from it, and when they failed, declared them "lost forever," and the fact that everyone else involved, including journalists and commentators and experts of all sorts, accepted that as the state of affairs ("well, if her hard disk crashed, what can you do, ya know?"), demonstrates that none of them has a clue about how e-mail works. It's like not knowing that cars have engines. It's that bad.

What e-mails have to do with Bitcoin and Block Chain

Compared to many other computer technologies, e-mail is simple. Compared to many other computer technologies, Bitcoin is complex. Even worse, what's interesting about Bitcoin isn't Bitcoin the crypto-currency -- it's the block chain technology on which it's implemented. Block chain is getting all sorts of attention from financial technology people and investors. I won't review it here, but a brief look at the action will convince you it's frothy.

What if investors, financial industry executives and Bitcoin technology company leaders are as informed about block chain as everyone involved was/is about e-mail? What if they're making important decisions based on critical observations as sound as "well, the hard drive is kaput, so the e-mail is gone, and that's that?" If the understanding of important actors in the e-mail drama exhibit paper-thin understanding and wrong-headed conclusions, are we to understand that all the folks involved in Bitcoin and block chain are geniuses by comparison?

Place your bets, people. I know what I'm betting on.