Thank you, and thank you for inviting us, and it's lovely to be part of this audience, and I'll tell you a little bit about what I do and some of my colleagues do around A.I. in finance. And when I do this with my MBA students, I always begin by asking them what is finance about in one word? What do you think finance is about? If you had to describe it in one word and everyone who has not worked in finance says it's about money.
But everyone who is working or has worked in finance is something else. And guess what, how Brits risk finance is about risk, the everyday life of finance practitioners is really all about risk, is about measuring risk, about quantifying the risk and then about pricing the risk. For example, if I ask the bank for a loan, the bank then uses whatever information they have about me and they come up with a probability or a number that describes the likelihood that I would pay back.
And then based on that number, they can attach some kind of a decision whether to give me a loan or not, or in some cases, also a rate that goes with that loan depending on regulation and time place. And they can do that because of course, I'm one person and I will pay yes or no. But if there's lots and lots of people like me, that individual risk translate to some kind of distribution and it's like a casino. They can come up with a pricing that would compensate them for the risk that they take.
So this is really what what's happened in finance in most aspects of finance. Something like what I described. But this is really where technology and I can have a big impact because the bank in this case is trying to estimate the likelihood that I will pay back. So the bank doesn't know as much as I do. So if they could go inside my head, they will have a better idea of likelihood that they will pay back. But if they can go into this, they will have a much better idea.
I would pay back because this knows better than me the likelihood that I would pay back any law. OK. And I know it sounds like you're excited by that. Too scared by this. But this is this is this is what we've seen. And if you remember the everybody remember the scandal, which Facebook and the election, but it came from a project that started by Facebook, although they don't like to talk about it, called my personality.
It started as a Facebook project with the Cambridge people before they started off, where they looked at the correlation between Facebook likes, which at that time was a public of the publicly available information. And what we call personality is measured by the Big Five. And this is the standard way of measuring facade. And if you remember the result, they found that if you have enough likes, then the algorithm can predict your personality better than your best friends.
In fact, they came up with this number, which I think was 200, but I'm not sure that with 200 likes, the algorithm can predict with more accuracy than your partner could think about. You know, the person who lives with you, they can predict better. So clearly, the information is phenomenal and and this is both revealing we can stop here because you can see both the the opportunity and the challenges around algorithm making decisions for us and using the technology.
Now this using technology using information via the internet and other devices is not new. It's new in finance, but it's been used in other areas. So, for example, in the travel industry, you know, until eight, eight years ago, something like that, if you travel somewhere, you know, you could have a bad hotel, you could have a good hotel. There was lots of variability. There is still variability, but a lot less.
Yes, it's very unlikely that you'll go to a place and it will be completely, completely horrible because of TripAdvisor, because people share the information and the places know that. So they know that nobody will come if they get really, really bad trips. So the reviews on TripAdvisor, so that that sort of is a way of aggregating information and reducing risk.
And maybe even a better example of that is with the insurance industry. And so if you are young drivers or if you have children who are young drivers, you're all familiar with this thing called insurance and insured the box or the box, the different names for it. But it's essentially a device that you put in the car and it transmits large information about the driver all the time.
So if you think of the example I started with with the loan, it's pretty clear that this is a way of reducing the asymmetry. So you're saying, I really don't know, I'm the insurance company. I think young people are, you know, statistically bad drivers.
So, you know, I have to give you a really, really bad rate to compensate for the fact that I don't know anything about you and you are young, but you can say, Look, let me convince you that I'm a good driver and this is a credible way for me to use technology and transmit the information life all the time in a way to do that. And just if you know, if you're familiar with fintech, you know, there's lots of apps like that that that help people build credit history and so on.
In much the same way as what I just described to, you know, so this is a lot of the new stuff is known as fintech or financial technology, and it's relatively new. The reason it's new is because until sort of 2011 12, the regulator stopped any kind of use of technology in finance for because the regulators view was we are here to protect the public and anything that involves people lending money to each other or doing all kind of risky stuff is bad.
And that's what we would say. No. And that has changed after the financial crisis and after the government have instructed the FCA, the financial used to be the FSA, the regulators to work with the start-ups and help them come up with all these new things. And so we have this explosion of all this new digital banks and all these kind of applications that you may have heard of that are exactly using that using technology in finance and coming up with all kinds of new things.
Let me give you a couple of example and some of the much loved company Wonga, if you heard of them. So one guy, they're no longer in business. But the idea was to give loans what they now call payday loans. So to give very short a small amount of money for short amount of time for people charging quite high rates for that. And the idea is these are people who wouldn't be don't have credit cards, don't have any other ways of loaning money.
The banks wouldn't touch them. And so it is offering them something that they would use. But the key to that was to use this technology because they need the money typically now. And the decision has to be made very, very quickly. Normally on the smartphone, using the information they have.
Another example, a bit more big scale, but similar is Funding Circle, which is a company that started by two of our undergraduates from the business school from Keeble College and is now a publicly traded company. So you may have seen their adverts on television. They're quite big, but Funding Circle is a peer to peer lending, so we take money from us, the public and we lend it to small and medium enterprises, businesses, small shops, small businesses that are starting.
And again, you have this idea that there is a gap in the market because the banks after the financial crisis have stopped lending to these people. They need money. Some of them will, you know, not bad companies. Yes. And the public, on the other hand, is getting kind of close to zero interest rates on their account. So why wouldn't the public entities and thing kind of share some of the rewards from that? And so they use and they created a very, very successful base in business based on that.
There's many more like them, but hopefully you've heard of either of them now. Both of them, as I said, are offering the service in a place where there is a gap. So they're offering something that didn't exist before. That's a good thing. Both sides are happy. Roughly speaking with that being, both sides rely heavily on algorithms to make decisions. So in Wonga, it's you can't have a person. It's not worth your while having the person. So making a decision of a loan of £10.
But with funding secured, even though it's much bigger sums, it's still highly dependent on the algorithms and with funding circle. And again, as an example, there is algorithms and two sides of the market. So when the company, when a small, medium company and small is asking for a loan, it is the algorithm that essentially make the decision whether to give it the loan or not.
I think there's nothing particularly sophisticated about this algorithm, which I haven't seen it, but it's using the same information as the bank would use. And so to make the decision, but also on the other side, on the public side, we can't invest the money directly in the businesses that we like. What happened is they take our money and they split it between a minimum of 100 companies. OK, so if you put a thousand pounds, it goes to a minimum of 100 different companies.
And the idea is that you get diversification. And if one or two or even five systems fail, you will still get decent returns for for your buck. So it all makes sense, but you can immediately see that this is different kind of finance where all the decisions are made by the algorithms. So this algorithms in finances is very similar to what you got called gets in and and you know, it's got big advantages, obviously, because it allows us to do things we couldn't do before.
And as I said, both sides are happy with it, but it also comes up like you've just described, and that's good with lots of challenges. And the challenges in this case is how are we making sure that the algorithms do what they're supposed to do? And it's not because the algorithms are bad, it's because we don't really have control over that black box. And so that create all kinds of issues for the regulators that they wouldn't be familiar with before and how to deal with that.
So for example, and I'm not suggesting any of these companies are doing that, but you can see that there is pressure on them to match the demand and supply. So that is a two sided market. Yes. So you have to give loans and you have to have money invested in these loans from the public. And what happens if there is more people signing up to lend money than businesses asking for money?
So, you know, does that create pressure on them to maybe slightly change the button of the algorithm and say yes to more loans? Yeah. This is the kind of things you do you really need to think about. And in the case of one guy, I can say, because that's public information. That's exactly what happened. And the regulator for that got very, very, very upset with them because they felt that the buttons were changed and they weren't, you know, fully informed.
There's no suggestion of that with funding secured with any of the other companies, but that's it gives you a taste of what it's like. Now, there is one part of finance where algorithm trade three, there's one find in finance where A.I. and algorithms are not new and this is algorithmic trading.
And this is algorithmic trading and this is what we do in the in the army and also some of the colleagues, my colleagues in the business school looked into that now algorithmic trading, the reason that the so it started in the 80s. So for example, my the is man. And actually the full name is my age. Gender and age is initials of three statisticians that came from Cambridge.
Apparently, there's a university there and they and they came up with one of the first black boxes that were that were used in to trade. And the reason the regulator didn't really worry about hedge fund is because hedge funds takes money from very rich individuals and from very sophisticated investors, so they don't have to worry about it. And so we have a case study in A.I. in algorithms going from the 80s.
And it's a really interesting one because with algorithmic trading, there's been a lot of start-ups, a lot of funds, some of them super successful, like ages like Millennium, like some of the Renaissance, some of the big ones, but some that have failed. And so we can learn from these things and see how and when algorithms are used properly and when don't use that inappropriately.
And let me just two more minutes to finish on this. So. So in two minutes, I want to say that algorithmic trading, as you said, is from the 80s, but it's still growing and developing because there's new sources of data. For example, nowadays satellite, you can buy that data with satellite images and you can look at Compaq's of companies and estimate the demand from that or when the CEO of publicly traded companies speak to analysts.
There is now lots of boats that translate that into numbers and deduce from that or kind of sentiment analysis and so on, and if there is optimism or pessimism and then trade based on that. So all these things are in exists and actually a couple of quick one, this one colleague of us in the business school is working specifically on that. So it is so, so it's very dynamic. There's a lot, a lot going on. And as I said, there is a lot of examples to learn from.
And you know. Let me just give you maybe a couple of sentences how I think about it then is this it sounds a bit like the Matrix when there's good programmes and bad programmes. But I think there are good algorithms and bad algorithms and bad algorithms is like the guy who's now. I think he just the Americans decided not to put him in prison. This guy from Onslow who did this flash crash, the trader that uses his algorithm trading to trade really, really quickly.
And he basically moved the whole the American stock market and he is now. It was in trial. I think they decided not to put him in prison for various reasons, but. But that's clearly an algorithm just used badly because it increases the risks. But good programmes, good algorithms is when you use the information to reduce risks. If, for example, you kind of trade lots and lots of different markets, which would be very difficult for a person to do.
But in a way reduces the risk because you are placing bets in many, many more different baskets than you would otherwise. And as I said, this is a fascinating area, and I'm looking forward to hearing from you at the end and also collaborating with other people around university because I think it looks different, but it's very, very similar questions that we are facing. Thank you very much.