Lessons from the Chinese model, with Richard Turrin
Five years ago, cash was involved in every fourth POS transaction in China. Today, it is involved in 1 in 14, and in two years, it is expected to be involved in just 1 in 33. The size and speed of that evaporation are undeniably impressive, but the most important lessons will be found by studying neither the size nor the speed at which those cash transactions evaporated but from studying where those transactions went.
In the West, cash transactions became debit card transactions and the big banks saw them all. In China, cash transactions went into the new mobile wallet ecosystems, and as the banks became blind, the Super Apps were born. Fed with transaction data at a scale never seen before, a new type of lender emerged, and in their growth, a thousand lessons might be found.
So in today's episode, I'm speaking to Richard Turrin, a Shanghai-based fintech, AI, and innovation consultant and best-selling author. We chat about his time with IBM Watson, his move to China, and how what they're doing there might inspire new ideas in the West.
Richard is the author of Cashless: China's Digital Currency Revolution and Innovation Lab Excellence both of which are available on, among other places, Amazon: https://www.amazon.com/stores/author/B07PQP33TC
But as you will hear, he is also writing for over 2,000 followers on Substack:https://substack.com/@richturrin
You can find Richard on LinkedIn, and possibly become his 50,000th follower! https://www.linkedin.com/in/turrin/
I am on LinkedIn, too, and open to new genuine connections - https://www.linkedin.com/in/brendanlegrange - please also follow the show's page while you're there.
Meanwhile, my action-adventure novels are on Amazon, some versions even for free, and my work with ConfirmU and our gamified psychometric scores is discussed at https://confirmu.com/ and on episode 24 of this show https://www.howtolendmoneytostrangers.show/episodes/episode-24
And finally, I'm also co-creating a new podcast called hAIghtened senses which will look at the intersection between human senses and technology, especially AI-powered technology. You can already start to follow it wherever you're listening to this one - there's only a trailer there at the moment, but we've recorded some of the early episodes and it's going to be a fun ride!
Keep well, Brendan
The full written transcript, with timestamps, is below:
Richard Turrin 0:00
But frankly, I've been doing nothing but computers and banks my entire life. So, yes, it's retail, but it's just another day with banks and computers.
We had to come up with new ideas. We had to come up with new products and people would tell us go to hell, we don't want them. There's no interest. You can't do that. And I'd sell it and they say, oh, you can do that, well, okay.
The entire purpose for an innovation lab is to take a hard to get group of people and to share their expertise within a larger organisation. And that's the exact same problem that AI faces. Everything I wrote in Innovation Lab Excellence is valid for AI deployment today.
Fundamentally, what AliPay did was to understand the use of big data and credit underwriting and the credit agencies that we have today are also making big data products for ready.
Brendan Le Grange 1:00
I was in Shenzhen, China, with my wife and my now late father in law, we had spent the day before visiting the army of terracotta warriors, one of those world famous tourist attractions that actually can meet heightened expectations. We were in the lobby of our hotel waiting to be picked up for a tour we were doing that day using motorbikes side cars to explore the surrounding countryside. And I had just read my confirmation email for the first time, and seen that it was cash only. I shouldn't have been that surprised because the vast majority of transactions in China were cash at the time.
I had to go outside find the nearest ATM and withdraw what was a comically large stack of notes. In Hong Kong, where we were living at the time, they were $500 $1,000 notes equivalent of 60, 120 US dollars. And so you could get transactions done, even in cash, pretty simply. But that was not the case in China. It was like carrying two hamburgers.
Luckily, I didn't have to go very far. And the tour was worth it. One of the most fun ways I've explored a country.
And about halfway through that trip, we stopped for lunch at a family house up in the hills. My wife dropped her earring in the long drop Lloyd the tour guide had to fish it out while her dad and I drink a beer. But we sat with Lloyd later at lunch, he'd washed his hands, and at some point or other we must have mentioned some of the cycle tours we've done in the past, because Lloyd told us about a route from Shanghai that followed, roughly speaking, the old grand canal to Suzhou. And we just pinned that bit of information in the back of our minds.
Fast forward about two years. And those of you who've listened to the show from near the beginning, may have already met my friend Romans Zilbering. I spoke about him in the episode about Moldova. He is a great credit risk strategist, a bit of friend but I think he'll be happy to admit not a natural tourist. So we were quite surprised when one day he asked us if we would organise a cycle tour with him. When he brought this up. We remembered that old story and said yes, of course we know our route.
I think that was the first time I went to Shanghai. And you can't help but be blown away by the visual impact of modernization and modernization at scale. I had planned the whole route on Google Maps. It was a technology I hadn't had access to when planning previous cycle tours. So I was very happy that this time we wouldn't get lost. Except I'd forgotten that inside the Great Firewall of China, there is no Google. So we had to revert to a paper map anyway, but all that did was underline how quickly the city was growing. In the couple of years since the mapmaker had been through big areas of empty land had been turned into homes for millions and millions of people.
It was impressive to see and in the context of the trip provided a great counter foil to some of the ancient places we visited over the next few days. But on the financial services side of things, we were still primarily using cash. fast forward another two or three years and I was back in Shanghai, this time speaking at a conference there and cash was gone. And I'm not just meaning within the hotel complex I could use my international credit card.
No, cash was gone.
Everybody was using their mobile phones and QR codes. It was changed at a pace that few countries can match. Welcome to How to Lend Money to Strangers with Brendan le Grange.
Richard Turrin, fintech, AI and innovation consultants and best selling author of Cashless and Innovation Lab Excellence, welcome to the show.
Richard Turrin 5:08
Thank you so much for having me, Brendan, and everyone out there in podcasts and video lands. Thank you for listening in.
Brendan Le Grange 5:15
Richard, you're currently living across Shanghai and Singapore working in and reporting on the rapidly evolving financial services landscape there. But your career is properly global, with some names that maybe aren't so familiar, but some names that are really familiar to those of us listening. So let's start there. I'm at the beginning of this path to where you are today.
Richard Turrin 5:39
Yeah, you know, it's really funny. So the answer is I was a career banker, and I was the kind of banker who worked with computer programmers and mathematicians, and I was one of those, what do they call them? The propeller heads, you know, I was an engineer studying Applied Mathematics and derivatives, you know, in the 90s. We're calling all of these technical people.
So I kind of laugh because when I was doing the combination of math, computers and banking, it was called structure products was called derivatives. Nobody dreamed of calling it fintech. But it really was because we're using the latest technologies to change how we looked at financial markets.
And people will say, well, it was wholesale, not retail. That's very true. But it was still a revolution. Because we could price bonds that didn't exist, we could look at risk and put prices on it that didn't exist before. And we could use analytical tools straight out of engineering straight out of physics to develop prices. And mostly it was good except for when it was really bad with the mortgage market. That's another issue though. But that's how I got into fintech.
Brendan Le Grange 6:49
So before FinTech existed, and before data science existed, but essentially doing both of those, and for a key moment of that at IBM. So yeah, we know the old saying that no one's ever been fired for buying IBM.
It is this core part of financial services and I guess, data science analytics FinTech ace, yeah, underpinned all of that, just to give some background.
Richard Turrin 7:14
Look, I'm coming to everybody from Shanghai, China, where I live. And I came to China after the global financial crisis, there were simply no jobs for people infrastructure products and derivatives, because that market exploded, that I eventually got a job working for IBM here because they needed someone who understood banking, banking software, and what's going on in the banking community in the United States, to bring some of that technology out here to China, where banks were at the time still building core banking systems, core risk management systems.
So I worked for IBM for five years, right at the point where innovation, FinTech exploded, and I happened to be transferred down to Singapore, where I headed their innovation teams working flat out on IBM's innovation floor, there were lots of different groups in there.
And basically what I did was work with bank innovation teams to get this work with them with Watson AI. Now, that's not like it's ancient history. But we're talking about 17 and 18. I think if my memory serves read my LinkedIn profile, telling me what the days are, right, but the point is, Watson was a great product, nothing wrong with it. But by comparison to the Gen AI that we're looking at today. Watson was really dumb.
But the point was trying to get bank innovation teams who were undergoing digital transformations to understand what the utility of AI was, what the utility of big data was, and how to help them to understand how to use these new resources in banking, my backgrounds in I guess, data science before we call it that, and the analytical teams working in the banks with all these sorts of tools that became AI and machine learning.
Brendan Le Grange 9:10
We were the ones most familiar with the sort of new technology, but we didn't have a new problem to solve. We just had the one we'd been working on all the time, and we solved a slightly better, we can bring in some unstructured data we couldn't deal with before. We've saved some time. But we were still building credit risk scorecards.
And you do need that moment to sit there say, wait a minute, this technology is not just an evolution of what the predecessors were doing. This can do all sorts of new things. That what new problems cannot solve?
Richard Turrin 9:39
Absolutely. I apologise. I'm interrupting. But that's the whole thing. So banks had plenty of data. And you had innovation labs, innovation teams coming in, and they're like, well, we can do this, this and this, if we have the data. And the next question is who knows how to access that data? There are always a bunch of guys in the bank who knew how to get it but what
Like you explained, they did it for one purpose. And now they were asked to access this data for all of these new purposes and in new ways. And, look, it's wonderful. It's beautiful. It's a revolution. It also stressed a lot of data teams who, you know, had one boss, one master, and then suddenly, they had five masters.
So you know, people who knew how to access the data, like you and your team became the hottest properties in the bank, I mean, broad strokes, but your analytical types often don't like change often like to have sort of a fairly structured world. And suddenly, you're saying, hey, we want to do something new and fresh. And it's a process.
Brendan Le Grange 10:39
Yeah, you need to think about culture and teams and all sorts. You can't just say, hey, go solve me an entirely new problem. He has the data and he has a new tool. There's a lot more involved.
But I first want to take one little step back, because I saw that among your speaking engagements you've done once in Mandarin, and I thought, well, maybe you'd studied Mandarin at university and made an intentional move to China's I was very intrigued to hear that actually, the Chinese move was not particularly planned.
So did you pick up your Chinese language skills living in Shanghai and working there? Or had you sort of learned the language before?
Richard Turrin 11:14
I had a couple of 100 words. I love languages. I'm a polyglot. I was born in the United States to an Italian family from the north. So we automatically you get German, bad German, very good Italian, obviously, native English. From there, you know, you work for a French bank. And then I'm like, okay, China. Next. Let's put another notch on my linguistic belt.
So to answer your question, yes, I learned the majority of it here in China, through private lessons, and through necessity, and I broke my Mandarin teacher's heart, who was still my Mandarin teacher. 12 years later, she's still my teacher, I call her my maymay, little sister, right? Because I've known her so long.
But I told her from the beginning, I don't want to learn characters, I have to talk to clients. And I'd give her these big long sheets of words, servers, datawarehouse, you know, and I'd give her all these technical terms. And she'd go translate them. And you know, and I, because I needed to do it at work. Look, if you make a living over here, and I've been here now for 14 years, you got to do it.
Brendan Le Grange 12:16
Well, a lot of people, myself included, I also lead on Cantonese been a little bit harder to learn. But no, I went to three lessons and gave up so it's all my fault. I sat giggling in the back of the class because I didn't know for accounting or doing colours. I've got such a bad ear for tones. But you're right. It's sort of the right way to approach it. But But I imagined you you'd see but looking at colleagues around or not, not everybody takes the trouble. So great to hear.
But sorry, yes, we interrupted the main topic, which were innovation labs. There's never ending debate do you build or do you buy in the FinTech world is as old as data science and fintech but an innovation lab, we see it rise and fall in popularity a little bit with the fashions. But working in that sort of capacity at IBM is probably the best place to learn.
So you went from having that experience to sharing that that knowledge with your first book, innovation, lab excellence, digital transformation from within? So do you want to talk a little bit about the book, but also, I guess, more broadly, what you learned, building those sort of teams.
Richard Turrin 13:19
There's some little bit of historical perspective, I had always worked, as I mentioned, with computers and banking and money, but the thing was, I was always innovating. I sat on a trading floor, you know, we had to come up with new ideas.
We had to come up with new products, and people would tell us go to hell, we don't want to, there's no interest. You can't do that. And I'd sell it and they say you can do that. Okay, well, okay, so the real motivation for me to write Innovation Lab excellence is while working for IBM's cognitive studios on the Lab Floor, my clients were all significantly younger than me, I was already Dare I say 55/ 57. Now that I'm older, my mind is too adelled to calculate. But my point is, I was already in my late 50s.
Alright. And I'm talking to younger people in labs, and they're telling me the same stories that I had 18 years ago, with, they don't want my product, they don't believe we can do this. They tell me they support innovation, then they shoot me in the back. Innovation Lab excellence was really a fast book for me to write. I wish I could write the third book as fast as I wrote that one, because it was the culmination of my career innovating in banks, and combined with the stories of the young people who are trying to innovate in banking, and most of them getting shot down.
Because most institutions, it's not unique to banks, it's you know, it's universal. We'll say, innovation is great. We want innovation and then the next day or you turn the page and that's too
Too much for today, you know, so that's kind of normal. It's a psychological hang up that people have about accepting innovation. So one of the parts that I talk about a lot in the book specifically, no bank, can hire a full complete AI team and then take a team, put it in this group, put them over there, no, you're lucky if you can hire a reasonable team, and you have to centrally locate them.
And you have to have them as a shared resource, which is exactly what innovation lab is. Everything I wrote in innovation, lab excellence is valid for AI deployment today, you asked a fundamental question. And it was one of the big points in my book, which is buy versus bill. And this is the same advice that I would give for an AI team today, you have to buy this technology, all but the very largest global banks like the JP Morgan's of the world, only a few of them are able to actually build their own technology.
So if you're looking at an innovation programme, or an AI programme, their job is to prove that this stuff can work. All right, their job is not to deliver ready to use code ready to use a AI that is ready for production. Because you really expect them to build their own large language model and know they prove it works.
And then you need to hire, particularly for the likes of AI, one of these larger firms is going to come in and hopefully have enough liability and insurance so that when your chat GPT style chatbot comes off the rails and give somebody the wrong answer. You can you can blame them with the losses.
Brendan Le Grange 16:41
Air Canada offers a new refund policy. I think it was a candidate got caught out with that. No, I'm so glad you brought that point up, because it definitely changes who should be thinking about innovation labs, I mean, when we look open AI, Google, they're going to be producing something so much faster and cheaper than me, I don't need it.
But actually, that's what you need the innovation team for, as you said, it's not to try and build Google, it's to say which of these two or three very complex tools actually works for us.
And this is like this, the flashy front end sales pitch, but somebody actually should understand how it works, what goes into it, what the constraints are, what the levers are, you need to pull. And those people alone are rare. I mean, as soon as you start looking at large language models, neural networks, any of these sorts of tools that are out there, even the relatively simpler older ones, I was talking to an AI engineer earlier.
There's a lot of complicated things he's doing in a model that is essentially drop data in and take data out.
Richard Turrin 17:41
Look at the disaster that Google just had with its Genesis text to image. I mean, they're Google. They're America's premier tech company. And you know, their images were highly insensitive. Is that the only word I can use? I don't know what I can get away with it. Yeah, I think just leave it at the images were insensitive, who checked this box?
Who looked at it and thought that it was okay that's what you're theoretically paying a big company for if you're a bank or a third party looking to use a large language models but anyway, yeah, buy please don't build
Brendan Le Grange 18:20
Yeah, and as I said, Innovation Lab excellence. The book is just as applicable today, easy to find on Amazon, I'm sure on other places as well.
But today, we're going to narrow our focus to your sort of home market and talk a little bit about China cashless China's digital currency revolution. It's not all lending. And so he talks about some topics though that are of great interest to people in the field.
So what is Cashless about and maybe give us some of the the feeling for the market that inspired that book.
Richard Turrin 18:53
Cashless is all about China's transition from a society that was predominantly cash based to one that was at least in the major cities, the first or third tier cities of China by 2018. or So had all gone effectively cashless, where they were using digital wallets on WeChat pay and Alipay.
It's the story of that transition.
And then it's the story of how China is gone beyond that.
And it explains what central bank digital currencies are, which is a hot topic right now because we're looking at a digital euro and a digital stirling programme. And we all know the United States is dead set against a central bank digital currency. It looks at how they're built, how it will be used. And fundamentally, it explains why central bank digital currency is far more than a way to simply buy a cup of coffee.
Because most people if they've had any experience in China, they visualise it perhaps as scanning the QR code and making the payment and they don't really think about the smart contracts that are associated with it, its ability to work without a signal without any wifi 5g 4g signal, it can work even with a dead battery in your phone, and how it promotes financial inclusion, particularly in rural villages.
That's where cashless goes, but relevant to your audience, I talk a lot about lending because WeChat pay and Ali pay once they became the standard de facto standard for digital payment. And once they had tremendous amounts of personal data, what we would call big data in the West, they probably only pay I'm gonna give it to Alipay - forgive me WeChat or Tencent, the mother company of WeChat, I love you too, but it was predominantly Alipay, that really invented Big Data loan underwriting.
And even as of some years ago, they had some 4,000+ parameters that they would look at from people's use of the ALI pay payment app. And they had machine learning models on them to basically underwrite loans. And that's the future, I think it's a great story, how they went from a highly successful payment system to going into a highly successful to successful that's word of mouth story later to successful loan generating platform and they generated loans with I wish I remember the stats, I wrote them down.
But when you look at card non payment in the West versus you know, Ali pay loans, it was like 100, or 1000 times less default rate, that's how good they are at it from the product point of view with with Alipay, you have this ability to learn from the customer's transactions, as you say, but also to be the fulfilling agent of the spin.
Brendan Le Grange 21:54
So you know, we were not just lending somebody some money that they're going to take away and do something with we are at the transaction, and we can embed it in that genuine Super App, you know, their struggle to recreate those outside of one or two markets around the world. But in the US and the UK struggled to create genuine super apps, but those parts of the transaction still exist today.
And they're very connectable, you might need to do some nice talking to partners to get get access to the stuff. But that environment can be recreated quite closely, if there's a will to look at it differently.
Richard Turrin 22:29
Absolutely. So I want to speak directly to everybody out there who thinks that's China, that's different. We can't do that here. Forget about Super apps, just leave that alone for a moment. They're great story. I talked about them for hours. But let's leave that aside. Fundamentally, what Ali pay did was to understand the use of big data and credit underwriting and the credit agencies that we have today are also making big data products or ready, they're gone beyond credit scoring.
And they're going into rental agreement scores, which are, admittedly by the credit score agencies, not just credit scoring, but use big data to go into Forgive me, are you a bad person? All right. So this exists, forgive this bit of sarcasm. If the US government can go to data brokers and buy tonnes of data on individual citizens and then use it it is not against the law for banks to go out and buy data from data brokers to flesh out a big data profile to enhance their lending activities.
Beyond the credit scores that are currently available. China is not particularly unique in the sense that what they did is perfectly attainable and applicable to the west. Now we're ready just to make people call in and say that guy is really wrong ready? China did this stuff a decade before the West? They've been on this since 2014. All right, they've got years now at looking at big data, instant payment, immediate loans, they are really good at it.
And rather than say that China isn't relevant, you shouldn't be looking at what China has done. Because that is like a crystal ball into the West's future.
Brendan Le Grange 24:29
Open banking is the one big easy channel to use to get a big chunk of this, but you can go beyond with the right consents, and just some transparency. And you may say, well, we'll stop short of where China goes. But the point is to explore those same ideas.
And the one I keep talking to people about is, if I use Apple Pay, that transaction goes through my credit card back into the normal system. It doesn't really change things. Whereas if I'm using a WeChat pay QR code, it's not going through my bank.
Now when we think about this western model that's heavily credit bureau based, take some rough numbers. Generally speaking, about 85% of people on the credit bureau have never missed a payment on Sunday. They're all our good customers. And the way we can create nuance between them is I can see which of these customers have spent $10,000 every month and repeated for 20 years. And which of these customers has spent $1,000 Once or twice, and I can say, Okay, I'm more or less confident based on this,
But that 85% 90% of people, if they spend, went away from credit cards, the majority of them are not using that credit card for credit. It's just the spending tool they repaying every month, there's an easy world to see where suddenly 80 or 90% of their data doesn't go to the credit bureau anymore.
And then what do you do with your FICO scores? If you're not thinking about where you're going there, if you think oh, this is just for first time borrowers, you could find yourself in a lot of trouble very quickly, because this sort of switch can happen really quickly. If someone doesn't right.
Richard Turrin 26:05
Look, China has credit rating bureaus as well. They are far underdeveloped. They were in 2014. Far underdeveloped. In fact, in my book, I talk about how they only had credit scores for a small percentage of the country because frankly, their credit score model was based on credit cards, and most of the population was poor. And most of the population never got a credit card.
So they only had credit information on essentially a wealthy group that had but we are now looking at account to account payments through the United States Feds now UK has had eight eight payments for years, you know, we can integrate payment data right into the credit systems, that's absolutely doable.
So truth bomb, your credit cards and what we call credit card rails, meaning the systems that carry the payments, and you accurately talk about how when you use Google pay Apple Pay, these are not digital payment wallets along the lines of WeChat. Pay or Ali pay, forget about the Super App part. They basically repackaged your credit card in digital form on your phone.
So you're still using credit card systems, then Credit Card Systems still charge merchants in the United States around 3.25 plus percent, I don't remember exactly. So we're paying these huge amounts in credit card fees. And we're maintaining a system that has been leapfrogged and surpassed by real time payments all throughout Asia, and that includes Australia, Singapore, China, India, with its fantastic UPI interface.
We're sitting here with credit cards. Yeah, they were great technology in 1990 1968, whatever. But the point is, we never evolve beyond them. And that's a real problem. But certainly, we could change or modify how credit ratings work in the West to accommodate account to account real time payments. In fact, they're doing it for fed now in the United States, because fed now, sadly, requires a bank account to use.
So the banks are kicking in the Fed now payment data up to the credit rating agencies. So they have a process that's essentially unchanged with what they've got now. But eight a payments in China in a UPI in India, they are essentially free or very near free. And they do not, under all circumstances require a bank account, which brings financial inclusion to an entire new group of people. Anyway, I think I digress.
Brendan Le Grange 28:48
No, no. I think it's very, very valid for the conversation. One last thing I wanted to cover though, while I've got the advantage of having you here is if we look at the Western world, that big development over the last sort of five years or so has been the sudden rise and drop back again of buy now pay later, if by now pay later was being built in China, or if the same problem perhaps was going to be solved in China. How would that maybe look different about that setup?
Richard Turrin 29:14
So by now pay later does exist, but it's a minor share of the loan market, whether you like or want to use WeChat pay or Ali pay credit systems, they provide you with a point here's the price. Here's the data point. I know what Alipay will charge me for this money and I know what WeChat will pay me for this money. Credit card utilisation rates in China, which were high interest loans just like they are in the West collapsed overnight because everybody cleared off their credit card balances and probably pay them off with WeChat and Ali pay loads as they were paying much less as you see I look at BNPL as a highly defective and regressive product.
There's nothing new about BNPL it is simply a throwback to 1920 instalment payments that you had at the company store. It's not exactly new lending has to go in the direction of we know more about you so that we determine whether you were worthy of credit or not, and what that credit should cost. Versus, okay, everybody gets it, and we know nothing about you.
And really BNPL success is really based in part on regulatory arbitrage. Nobody knew how to regulate this stuff. So it took off. But I was prepared for this question. And I don't want you to listen to this because I cut this out special for today.
And these numbers I am sure of because I will read them. This is the latest stats out of the United States regarding BNPL US market ready 56% of people who used BNPL encountered at least one problem while using the service. overspending 29%, missing a payment 18% difficulty returning products and getting a refund 18% BNPL isn't free because they're nice guys, they want you not to pay so they can make money on you.
And then the users of BNPL are our financially most vulnerable group, and are predominantly under educated. We want people to thrive in society and BNPL is not really the way to go. I mean, yes, I know it's successful. That doesn't mean I have to like it, it is the opposite of knowing about your customer. And we have that ability to know about our customers.
Brendan Le Grange 31:44
And we should be using it. One of the things to think about in the lending world about this knowing is the timescale as well, like the old model of lending was that I'm deciding today. And I'm assuming I'm not going to revisit this for six months minimum.
So I'm going to take what I know today and give you a credit limit.
And you know, it's such a weird lever to be pulling these days to just have this big, clunky stick to beat people with somebody's needs to be coming out with something that adapts a little bit better.
That moves people, there's a lot of ways to break this down that fit better if we forget everything about the system that's been built today. And one way to do that be inspired by other markets who've done it differently. I couldn't agree more. And I want to grab on to something that you said. And that was paraphrasing. If we're not stuck on the system that we built today, when I look at credit and payments in the West, and I compare them with credit and payments in China, and not just China all go to India.
Richard Turrin 32:46
Look, folks, and we're talking I'm gonna say this one's just really quickly, it's divergent Central Bank, digital currency, China and India are both going to have them in the next year or so 35% of the global population will have access to a central bank digital currency, meaning it's not unusual. And I grant they have new technology and they are not restrained by traditional lines of thought.
And because they are unconstrained because they have access to data. And I won't even say better data, but they are free of how they think. And don't be thinking that China or India, the other example are unregulated.
Oh, their regulations don't care care about personal data, or they allow for free use of this stuff. Are you kidding me? China has more severe data regulations, then anybody on the planet, India has severe data regulations. That's the way the dragon in Google and other big techs into the court system.
So it's not because they are somehow lawless third world countries, it's because they have adopted a different way of perceiving and understanding. And we are sort of blocked in submit. And that is a fundamental problem. And why we are falling behind.
Brendan Le Grange 34:04
There's so many more things that you speak about and write about, Richard. So I know you've got a website, www.richardturrin.com but if people want to benefit from from this sort of years of insight you've got in the region, one to read your works, read some of your other posts. Where should they go to connect with you to learn more about the work you're doing?
Richard Turrin 34:27
Sure, no problem. Look to anybody out there who severely disagrees with anything I say. Write me, I write everybody. You know, I'm happy. I'm on social. Here's how to get in touch with me.
My best writing of all is on substack if you're a fan of substack I have a weekly newsletter, but I actually write daily on substack. So @richardturrin on substack.com
I'm on LinkedIn. I'm on Twitter, send me a message on it.
Any one of those platforms, I'm proud to say I'm the number four global FinTech influencer on the analytic ranking system. So reach out to me LinkedIn, Twitter, and substack. My books are all on Amazon, Kobo and a number of other places. And if you disagree, tell me tell me why I'm wrong. And that's fine. And if you agree with me, and you samsara was a nice guy, please write me and tell him tell me I'm a nice guy. I love those letters even more, I have to say, but but I, you know, I, I pride myself, I'm on social media. And it's Zoo. People are not pleasant anymore.
But I pride myself in running absolutely clean, pleasant and aboveboard discussions that are kind to everyone, even those I disagree with?
Brendan Le Grange 35:46
Yeah, and of course, I mean, it's, there's a flavour of politics, we get to come into any West versus East sort of discussion. And there's nuance that gets in there.
But I think whether people agree that the Chinese model is the way to go or not. The fact is, it's working really well, in many situations. Even if you're not saying, Well, we're going to bring it all over. You can't help but learn Sunday.
Richard Turrin 36:10
Thank you so much for that, because that is really the point I try so hard to make in my book. And it's no surprise China, US China West relationships have plummeted. But the reality is number one, just because I live in China doesn't mean I've turned into a communist. I get that question from time to time. So please, you know, I'm a hardcore banker for 18 years. And that hasn't changed.
But the concept that oh, what happens is China is not applicable to the west. That is a fundamental error. They've tackled fundamental problems that we can expect to have some time in our own futures. So there's a lot there to learn from and a lot there that again, works like a crystal ball to our future.
Brendan Le Grange 36:54
Yes, certainly things to be fixed. Certainly, if you're looking to fix if you're looking to think differently, why not look at a big alternative.
So the show itself is audio. So not everyone will see the pictures but with the backdrop of Shanghai behind you, is one of my favourite images of modern China because it is so clearly a big growth, innovative space. And it's been a while since I've visited but you know, anybody who goes there once will see okay, if this stuff happening here, that is worth considering.
So yeah, thank you so much for your time
Richard Turrin 37:27
And thank you so much for having me. Thank you to everybody for listening in.
Brendan Le Grange 37:32
And thank you all for listening.
Please do look for and follow the show on your favourite podcast platform and share the updates widely on LinkedIn where lending nerds are found in our largest concentration. Plus, send me a connection request while you're there.
This show is written and recorded by myself Brendan le Grange in Brighton, England and edited by Fina Charleson of FC Productions.
Show music is by Iam_wake, and you can find show notes and written transcripts at www.HowtoLendMoneytoStrangers.show and I'll see you again next Thursday.