The Opportunity with AI Driven Lending: Paul Gu
发布时间 2022-09-07 13:42:08 来源
You're listening to leaders in lending from Upstart, a podcast dedicated to helping consumer lenders grow their programs and improve their product offerings. Each week, hear decision makers in the finance industry offer insights into the future of the lending industry. Best practices around digital transformation, and more. Let's get into the show.
你正在收听Upstart的放贷领袖，这是一个致力于帮助消费者放贷人士增长其项目和提高产品优势的播客节目。每周，听金融行业的决策者提供关于放贷行业未来的深度见解。包括数字转型的最佳实践等。现在，让我们开始节目。
Welcome to Leaders in Lending. I'm your host, Jeff Keltner. This week, we're sharing with you a slightly older episode. My conversation with Upstart co-founder and head of product and machine learning, Paul Gooh. This is one of the first episodes we had.
欢迎来到领袖借贷。我是您的主持人杰夫·凯尔特纳。本周，我们与您分享一集稍早的节目。这是我与Upstart的联合创始人兼产品及机器学习负责人保罗·古的对话。这是我们第一次制作的节目之一。
Paul dives in great depth into the history of Upstart, how we chose to start the business and unsecured lending, and quite a bit on the application of artificial intelligence and machine learning to the overall lending process. Where Upstart is doing that today, how we do it, and where we see it going in the future.
保罗深入探讨了Upstart的历史，我们选择创业和无担保贷款的方式，以及人工智能和机器学习在整个贷款过程中的应用。他详细介绍了Upstart今天正在做什么，我们如何做到这一点，以及未来我们将如何发展。
If you're interested in any of those things, this is a great conversation. Without further ado, please enjoy this conversation with Paul Gooh.
如果你对这些事情有兴趣，这将是一场很棒的谈话。不多说了，现在请享受与Paul Gooh的谈话吧。
Paul, thanks for joining me today.
保罗，感谢今天和我一起参加。
Of course. Happy to be here.
当然。很高兴能来这里。
Yeah, so I want to start with when you and I first met, or really, when you got into Upstart, because you were an undergrad at Yale studying interesting things and decided that it was worthy of your time to drop out of school and found a company in the lending industry, which is maybe not what every teenager in their late teens thinks at the time. So walk me through a little bit what it was about this space that was interesting to you and you felt like something you had to go and do.
嗯，我想从我们第一次见面开始谈起，或者说，当你进入Upstart时，因为你当时是耶鲁大学的本科生，正在学习有趣的事情，并决定这值得你花时间退出学校，在贷款行业创办一家公司，这可能不是每个青少年在十几岁时都会想到的事情。所以请告诉我一些关于这个领域有趣的部分，并且你觉得它是你必须去做的事情。
Yeah, so when I was in school, I was studying computer science and economics, spent time sort of preparing myself for sort of life in a sort of quant hedge fund world. Spend some time there. And essentially what I realized was there were a huge number of very smart people applying novel technologies to solve an incredibly narrow problem, the problem of slightly inefficient securities prices on a variety of sort of traded securities. So that was great, but it wasn't obvious that that was solving important problems for a lot of sort of real normal people. And I thought if you could simply take the same techniques that were used there and apply them to a problem that would affect real people, you could do something that was very important.
嗯，当我还在学校的时候，我学习了计算机科学和经济学，花了很多时间准备自己进入量化对冲基金的世界。在那里待了一段时间后，我渐渐意识到，有大量非常聪明的人运用新颖的技术解决了非常狭窄的问题，即各种交易证券的价格略有不效率。这很好，但并不明显这样做解决了许多普通人面临的重要问题。我觉得，如果你能简单地将用于那里的技术应用到会影响普通人的问题上，你就可以做一些非常重要的事情。
So that was sort of the first half of the story. Then as it sort of I got to thinking about where that application might come from, the sort of most obvious place was in the sort of closest to Jason Z. Instead of incorporate finance moving over to personal finance. And the more I learned about it, the more I realized that huge numbers of people have very limited access to credit.
那就是故事的前半部分。后来当我开始思考申请可能来自哪里时，最显而易见的地方就是与Jason Z最近的地方，不是企业金融转移到个人金融。我学到的越多，就越发现有很多人对信贷的接触是非常有限的。
Essentially anyone who isn't born with money has a hard time getting access to money before they've built up a long rich credit history. And of course, that means for the vast majority of people, there are at least some limits to their ability to get credit when they need it most. And if we simply applied the techniques of machine learning and AI that had really been demonstrated to be incredibly powerful in other domains and applied them here, we could do something very important for people.
基本上，除了出生就有钱的人，其他人在建立长期的丰富信用历史之前，很难获得资金的访问。当然，这意味着对于大多数人而言，在最需要信贷的时候，他们的信用能力至少有些受限。如果我们简单地应用机器学习和人工智能的技术，这些技术在其他领域已被证明非常强大，应用到这里，我们可以为人们做一些非常重要的事情。
I love the mission that you guys started with directly around helping the consumers. I am curious when I look at the consumer lending space, there's lots of kinds of loans and the personal loan, the unsecured loan, what banks maybe have called a signature loan, it's kind of a small space overall in the consumer lending ecosystem. Why was that the place when you said, hey, we can use these techniques to improve access? Why was that the kind of loan as we were starting up, sorry, that she said, this is where we're going to focus our time versus some of the larger categories like mortgages or key locks or auto loans or something in that nature.
我喜欢你们所开展的使命，直接围绕帮助消费者。当我看到消费者贷款领域时，有许多种类的贷款和个人贷款、无抵押贷款，银行可能称之为签名贷款，总体来说这是一个比较小的消费者贷款生态系统。当你们说，嘿，我们可以使用这些技术来改善访问时，为什么选择这个领域？为什么在我们刚开始运营时，她说，这是我们要重点关注的地方，而不是一些更大的类别，如抵押贷款、钥匙锁或汽车贷款等？
Yeah, great question. Principally, two reasons.
是啊，好问题。主要有两个原因。
The first is if you want to demonstrate the efficacy of machine learning applied to something that's been done a very long time by many, many companies, many banks, you want to sort of apply in the place where it's going to matter the most and be the hardest.
第一个是，如果你想证明机器学习在很多公司、很多银行已经做了很长时间的事情上的效果，你需要将其应用在最需要最难的地方。
And that's an unsecured personal loans. If you think about every other asset class, car loans, home loans, they're backed by something, even credit cards are sort of backed by the further utility of being able to use your credit card in the future.
那就是一种无担保个人贷款。如果你考虑其他任何资产类别，如汽车贷款、房屋贷款，它们都有背书，即使信用卡也有进一步的效用，因为你可以在未来使用你的信用卡。
When you give someone $20,000 and say, please pay me back, you're really backed by nothing. And what that means is you really have to be very good at making a decision about who you're going to lend to and who you're not going to lend to or else you will very soon be out of business.
当你给某人$20,000并说请还给我时，你实际上没有任何抵押。这意味着你必须非常擅长决定你会借给谁，不借给谁，否则你很快就会破产。
And that meant that if we could do a better job of underwriting the risk here using AI, we would be able to generate incredible economic gains for both sort of the lender and the consumer. And so that's what we were able to do.
这意味着，如果我们能够使用人工智能更好地承保这里的风险，我们将能够为借款人和消费者带来惊人的经济收益。因此，这就是我们所能够做到的。
The second reason is that from the consumer's perspective, the unsecured personal loan by its very nature is the most flexible kind of loan. It can be used for any purpose. It's not something that you can only get sort of at these specific moments in your life when you're buying a car or a house.
第二个原因是，从消费者的角度来看，无担保个人贷款本质上是最灵活的贷款种类。它可以用于任何目的，不是只有在购买汽车或房屋等特定时段才能获得。
It's something that you really can get at any time for any reason. And in that sense, it was the broadest product, it's a natural starting place. And we always felt that if we did those first and second things, it would be much easier to go from personal loans to other types of loan products, which we're now doing, then the other way around where if you start with something really safe and really limited, it would be very hard to get out of that box and go to other spaces.
这个产品你无论何时何因都可以得到。因此，它是最广泛的，也是最自然的起点。我们始终坚信，如果我们先做这一、二件事情，就可以更容易地从个人贷款转向其他类型的贷款产品，而我们现在正在做的就是这样的东西。相反，如果你从一个非常安全而极其有限的产品开始，那么很难跳出这个框框去做其他的领域。
Interesting. I don't think starting with the riskiest space is the way many lenders approach the space, but I take your point that it's kind of where the technologies make the biggest difference.
有趣。我认为很多放贷者并不会选择从最具风险的领域开始，不过我明白你的意思，这也是技术带来最大变革的领域。
What do you think the biggest shift is? I mean, I know many lenders have talked about ML or AI and we talk about ML or AI a lot in terms of the context of how we underwrite credit. But talk to me a little bit about what you think the shift is from a traditional approach to lending to one that's really ML or AI driven and how you approach the question of identifying risk in a different way.
你认为最大的变革是什么？我的意思是，我知道许多贷款人谈论过机器学习或人工智能，并且在我们评估信贷方面的背景下，我们也经常谈论机器学习或人工智能。但是，请给我讲一下，你认为从传统的贷款方式转向真正的机器学习或人工智能驱动方式所带来的变革是什么，以及你如何以不同的方式来确定风险问题。
Yeah. But these words are often used as buzzwords and sometimes they cease to lose their meaning because of that. But when we say them, what we really mean is principally two things.
没错。但这些词常常被当作流行语使用，有时它们因此失去了意义。但当我们说这些词时，我们实际上主要想表达两个意思。
The first is that we've taken an approach of using an immense quantity of data in the problem. Unlike in traditional underwriting, where only a handful of data points are used, we use over a thousand variables. We use millions of rows of repayment data to actually train the model.
我们采用的第一种方法是，在这个问题上使用了大量的数据。与传统的核保不同，只使用了少量的数据点，我们使用了一千多个变量。我们使用了数百万行的还款数据来实际训练模型。
Those data points that we're using are a mix of both traditional and non-traditional data points. We really are sort of seeking the broadest possible set of data that can be used to drive signal about who's going to pay back low.
我们正在使用的那些数据点是传统和非传统数据点的混合。我们确实在寻求可能最广泛的数据集，以推动有关谁将偿还低的信号。
The second half is really about the way that we learn from that data. So traditionally, a person would go look at the data and try to draw insights from it or they might use a classical technique like a regression technique that essentially means fitting a straight line through the data.
第二部分其实是关于我们如何从数据中学习的。传统上，一个人会去查看数据并尝试从中得出见解，或者他们可能会使用一种经典技术，比如回归技术，这实际上意味着将一条直线拟合到数据中。
Now the problem is that while they're very easy to draw and understand, they don't fit the world all that well because most of what happens in the world is not straight lines. It depends on actual interactions between different variables, between the different factors that drive whether a person is going to pay back or not, when they're going to pay back, if they're going to prepay, when they're going to prepay.
现在的问题是，虽然它们非常容易画和理解，但它们并不很适合现实世界，因为世界上发生的大部分事情都不是直线。这取决于不同变量之间的实际交互，驱动一个人是否会还款、何时还款、是否会提前还款以及何时提前还款的不同因素。
And so to actually make precise individualized predictions at the month level, at the borrower level, what we need to do is we need to actually use sort of state-of-the-art machine learning algorithms. These are algorithms that actually are highly flexible, are able to sort of find patterns and sort of tens or hundreds of relationships in our variables interacting together and the relationships between them.
所以，要在月份和借款人层面上进行精确的个性化预测，我们需要使用最先进的机器学习算法。这些算法非常灵活，能够发现变量之间的模式和数十甚至数百个关系以及它们之间的相互作用。
And when we sort of combine that kind of big data approach with the sort of modern algorithm approach, that's really what we call AI lending.
当我们将那种大数据方法和现代算法方法结合起来时，就形成了我们所称的人工智能借贷。
Interesting.
有趣。
When you think about the uplift you've got, and we can talk about the actual increases in accuracy and how you see that, but then you talked about more data, both in terms of how you think of data as a spreadsheet, both in terms of more columns, more data points, per applicant, and more rows, more historical data look at.
当你考虑到你现在的提升时，我们可以谈论到实际准确率的提高以及你所看到的变化。但是你也提到了更多的数据，无论是对于数据如何呈现于电子表格之中，还是对于每个申请人而言的更多列和更多数据点，以及更多行，更多的历史数据来查看。
I know there's also the use of alternative data points and models. How do you think about those different components and how important each of them is or isn't to the improvement in accuracy? Is there one of those that's really pulling the bulk of the weight and driving the outcome as they're a split?
我知道还可以使用备选数据点和模型。你对这些不同的组成部分如何看待，以及它们对提高准确性的重要性各有多少？它们中有一个真正在发挥最大作用并推动结果吗，因为它们是一个分裂的整体？
I mean, how do you think about what's really driving the efficacy of the models' upsides building?
我是说，你怎么看待模型优势建设真正推动的因素是什么？
Yeah, so two answers there. The first is I would say these sort of different components work together in harmony, and that's not just sort of a generic talking point. It's actually really important because if you think about using a high powered machine learning algorithm, you tried to use it with only five variables. Well, it's not going to do anything more for you than the straight line because you've hardly given it enough data to work with.
嗯，有两个答案。首先，我会说这些不同的组件能够和谐地协同工作，这绝不仅仅是一个老生常谈的说法。这实际上非常重要，因为如果你想使用高功率的机器学习算法，但只提供了五个变量，那它对你的帮助几乎和直线一样，因为你提供了极少的数据供它使用。
It's not going to have enough to draw and to actually use the sort of powerful, complex, non-linear learning patterns that it's capable of. Similarly, if you were to take over a thousand variables and try to plug them into a straight line model, it wouldn't work. The model would also not be able to, it would tell you that 900 of the thousand variables are useless because it turns out that if you say, I'm only going to use this variable in a straight line way, independent completely of the other 909 variables, well, most of them actually aren't that useful.
它没有足够的威力、复杂和非线性学习模式来绘制和实际使用。同样，如果你试图将一千个变量插入一个直线模型中，那是行不通的。模型也不能告诉你，它会告诉你九百个变量是没用的，因为如果你说，我只会用这个变量来做直线运算，完全独立于其他九百零九个变量，很多实际上是不太有用的。
Most of the data is useful only in the context of certain other variables and usually in a way that's highly non-linear. So they'd really do go together. I would say though, if we had to sort of pry it apart and we do for certain of our products where we are sort of selling subsets of our model where someone can just access the algorithms on traditional credit data without necessarily going through all the alternative data, we sell that product because if you had to pick a single thing that was most important, you would go with the more powerful enhanced learning algorithms run on traditional credit data, that sort of first step gives you a sort of very significant boost to accuracy, but of course layering in that additional data takes you that much further.
大部分数据只有在特定的其他变量上下文中才有用，并且通常以高度非线性的方式呈现。所以它们真的应该结合在一起使用。不过，我认为如果我们必须分开使用它们，那在我们某些产品中，我们会销售我们模型的子集，这些子集只使用传统信用数据上的算法，而不需要使用所有的替代数据。我们销售这款产品是因为，如果你必须选择最重要的单一因素，你会选择在传统信用数据上运行的更强大的增强学习算法，这是第一步，它可以为您提供非常重要的准确性增益，但当然在加入其他数据后，它会让你走得更远。
Let me just dig in that for a little bit because I know you said traditional credit data, but many people think of traditional credit data as 10, 15 points of data off a credit file. What do you really mean when you say traditional credit data? Because my understanding is it's a little more sophisticated than just a credit score or a credit score and a couple of extra data points, but you're digging a little deeper than typical.
让我稍微探讨一下，因为我知道你说的传统信用数据，但很多人认为传统信用数据只是从信用档案中获得的10、15个数据点。当你说传统信用数据时，你真正在说什么呢？因为我理解它比仅仅是信用评分或信用评分和一些额外的数据点要复杂，你在探索的比平常更深入一些。
So when a lender pulls credit, they're really getting two different kinds of things, really three different kinds of things. The first thing, the most basic package is they are getting a record of every single transaction that a consumer has had that has to do with credit. Every single payment, mispayment, delinquency, application for credit, all of that is recorded on a person's credit history. The second thing they get is they get the credit bureaus interpretation of that data.
所以，当借款人拉取信用时，他们实际上得到了两种不同的东西，实际上是三种不同的东西。最基本的一件事是他们获得了每个消费者与信用有关的每一笔交易的记录。每一笔付款、违约、拖欠、申请信用等等都记录在个人的信用记录中。他们获得的第二件事是他们获得了信用局对这些数据的解释。
The credit bureaus say, well, let's summarize that this sort of vast history into a handful of data points. Most commonly they'll tell you, for example, this person has had three credit inquiries in the last six months or this person has been delinquent twice in the last two years. And they'll have a handful of common summary statistics about a person's credit report. Now the third is they'll actually pass that same information into a model that is built by sort of third party scoring agency, most commonly, FICO.
信用局说，他们将把这种波澜壮阔的历史总结成一些数据点。通常情况下，他们会告诉你，比如说这个人在过去的六个月内有三次信用查询，或者这个人在过去两年中有两次拖欠。他们还会有一些关于一个人信用报告的常见摘要统计数据。第三种情况是他们实际上将相同的信息传递给由第三方评分机构（最常见的是FICO）建立的模型。
And they'll output scores that say this score was based on some of these summary statistics, things like the number of inquiries in the last six months. And so you get these sort of three things, the sort of raw, complete history of a person's credit life, the summarized statistics from the credit bureau. And then finally, the sort of super summarized scores. And in sort of traditional credit scoring, a lot of the approach is to rely on the latter two categories, either the summarized statistics or even the sort of completely compressed credit scores.
他们会输出分数，显示这个分数是基于某些摘要统计数据得出的，例如过去六个月的查询次数等等。因此，您会得到三种东西，一个人信用生活的原始完整历史记录，从信用局中摘要的统计数据，以及最后的超级摘要分数。在传统信用评分方面，很多方法是依赖后两个类别，无论是摘要统计数据还是完全压缩的信用评分。
And when we talk about credit data at Upstart, we are talking about the full rich raw history, which is often highly unstructured, can be permuted in almost infinitely many ways. And so there's actually a lot of really interesting things that can be done using machine learning algorithms to figure out how to generate the most signal from this history in a way that doesn't just sort of completely overfit the data or completely cause your sort of servers to melt down because you're sort of trying to run something that is sort of too big of a computation. And that's where I think a lot of the interesting work we do with traditional credit data actually happens.
当我们在Upstart谈论信用数据时，我们谈论的是完整、丰富和非常混乱的原始历史，这些历史可以被排列成几乎无限多种方式。因此，使用机器学习算法找出如何从这些历史中生成最多信号的方法，这是真正有趣的事情。而这需要避免数据过度过拟合或导致服务器崩溃的方式来运行计算。在这方面，我们使用传统信用数据实际上做了很多有意义的工作。
So you talk about servers melting down and thought of that. We have servers in the Upstart that would melt down. I think they're all somewhere in the cloud, but not our servers. They're not our servers. They want to melt down Amazon servers and burn down a database.
你说了服务器会熔毁，我也想到了。我们在Upstart中有一些可能会熔毁的服务器。我想它们都在云端，但不是我们的服务器。它们想熔毁亚马逊的服务器，烧毁一个数据库。
But how do you think about the trends and technology that have enabled us? I mean, is there a reason from a technology point of view that these kinds of capabilities are coming to the fore now versus five or 10 years ago? There's something that's kind of shifted that's opened this up to be more accessible or possible today.
你怎么看待让我们取得成功的趋势和技术呢？我的意思是，从技术角度来看，现在这些能力成为了突出的特点，相对于五年或十年前，是否有原因？似乎有一些变化，使其变得更加可达或今天成为了可能。
Yeah, absolutely. I'd say the first big thing is compute availability. You know, when I say, and when I sort of jokingly said servers melting down, I of course didn't actually need them melting down. But I really meant was just sort of not having enough compute to do the work you want to do in this sort of space of time that you want to do it.
是的，完全正确。我认为第一件重要的事情是计算机的可用性。你知道，当我说“服务器崩溃”，我当然并不是真的希望它们崩溃。但我真正的意思是，在你想在这个时间空间内完成想做的工作时，可能没有足够的计算资源。
And even today in a world where you can infuri arbitrarily scale up the number of EC2 machines that you've got on the Amazon cloud, we still are actually constrained by the amount of compute we can access. And the way that manifests itself is when we run into compute problems, we start seeing run times that take longer and longer.
即使在今天，在这个你可以随意增加Amazon云上的EC2机器数量的世界里，我们仍然受到可访问的计算资源数量的限制。这种限制当我们遇到计算问题时就会显现出来，时常导致运行时间越来越长。
So for our full model training process, we've often in our history got into places where the complexity of the learning algorithms interacted with the sort of amount of compute and the efficiency of the sort of compute we had available to us. That we faced run processes spanning 24, 48, 72 hours for a single run of the model training.
所以，针对我们完整的模型训练过程，我们在历史上经常遇到的问题是，学习算法的复杂性与可用的计算机效率和计算机数量之间互相作用所致。这导致了我们需要花费24、48、72小时的时间来执行一次模型训练。
Now, you imagine that you do that once. It doesn't sound so bad, but actually you don't want to just do it once. What you really want to do is you want to find the optimal model, the sort of most predictive model among the universe possible models. And that means you want to search. And when you want to do search, you need to sort of go through many iterations of different models.
现在，你可以想象只做一次。这听起来不那么糟糕，但实际上你不仅仅想做一次。你真正想做的是找到最佳模型，即在可能的所有模型中最具预测性的模型。这意味着你需要进行搜索。要进行搜索，你需要通过许多不同模型的多次迭代来实现。
If each run is taking you 48 hours and you've got 1000 different runs to do, you're looking at a multi-year project to find a model. And so of course, that's not going to work very well.
如果每次运行需要 48 小时，而你需要做 1000 个不同的运行，那么你需要花费数年时间来找到一个模型。所以，显然这不是一个好的方法。
Now that's sort of the state of things today. And a lot of the investments we make are in improving the efficiency of those learning algorithms and figuring out how can we short cut the search so that instead of taking 48 hours, it only takes 24 hours. And instead of doing a thousand sort of searches, we only do have to do 20 searches. And suddenly you've got a problem that's much more tractable.
现在这就是今天情况的状况。我们很多的投资放在提升那些学习算法的效率和想出如何能够削减寻找的时间，使得它不需要花费48小时，而只需要24小时。而且我们也不必做一千次的搜索，只需要做20次搜索。这样一来，你就得到了一个更加能够解决的问题。
Now, if you rewind back 10 years, you're looking at a compute that's a fraction, a sort of almost an order of magnitude less than what was available today at the same cost. So it just sort of been much, much harder.
现在，如果你回溯10年，你会看到一台计算机，它只有今天同样成本的零头，差不多是数量级更少的。所以它变得更加困难了。
Of course, in parallel with the sort of improvement in the sort of underlying sort of infrastructure, there has been many advances in the actual algorithmic technology advances made sort of both in the kind of theoretical math and statistics side and also in the sort of implemented computer science side. And of course, we benefit from a lot of those advances and we're applying them to the sort of unique problems of lending.
当然，与基础设施的提升并行的是，在理论数学和统计学方面以及实施计算机科学方面，算法技术也有很多进步。我们从中获益匪浅，并将其应用于借贷业务的独特问题上。
Yeah, it's fascinating how those two things have interplayed the availability of compute and then the sophistication of algorithms available because it's a compute available to use them and they kind of feed each other a little bit. It's interesting.
哇，这两个东西是如何相互交织的，可供使用的计算能力和可用的算法的复杂性，因为有计算能力可用来使用它们，它们有点儿相互促进。很有趣。
Now, the other thing I know, I'm sorry, kind of focuses on is not just the credit underwriting, but actually the process simplification, right, the mission statement, it's effortless credit, right, enable effortless credit based on true risk. How do you think about the application of these kind of technologies to effortless? What does that mean and how do you go about reducing the effort of the friction in the lending process?
现在，另一件我知道的事情，抱歉，有点侧重于的不仅是信用核准，而是实际上的流程简化，对吧，它的使命宣言是轻松信用，基于真实风险实现轻松信用。您如何考虑将这些技术应用于轻松信用？这意味着什么？您如何减少贷款过程中的摩擦力？
Yeah, you know, I've started to really think of these not as two distinct things, but as part of one thing, which is the one hand, when you think about the term underwriting, really it is just everything you are doing to ascertain the risk of this loan. It doesn't matter whether you're doing it at the front of your process or the back of your process. It's all for the sake of one thing, which is you want to make sure that the person you're giving a loan to, the high chance of paying it back.
嗯，你知道，我已经开始真正地认为这不是两件不同的事情，而是一个事情的一部分。一方面，当你考虑到贷款核保这个术语时，其实就是你所做的一切来确定这笔贷款的风险。无论你是在流程的前面还是后面这样做，都是为了一个目的，那就是你想确保你给贷款的人有很高的还款机会。
And whether you're doing that by pulling their credit report or by verifying their income, it's sort of all for the same purpose. It's all underwriting.
无论是通过查看他们的信用报告还是核实他们的收入，都是为了达到同样的目的。它都是核保。
Now, the flip side of that is there are, and always have been ways to gather more information about people that create a lot of cost and friction. And in some sense, there's almost a perfect trade-off between these things. You could decide to follow a person around every day for their whole life, and you probably have a pretty high chance of figuring out the sort of person who's going to pay you back, even without any fancy AI, right?
那么，另一方面，总是有收集更多关于人们信息的方式，这些方式会带来许多费用和阻碍。在某种程度上，这两者之间几乎存在完美的权衡。你可以决定每天跟踪一个人一生，甚至不需要任何高级人工智能，你也很有可能知道这个人是不是值得信任的人。
But of course, that's going to be incredibly costly and probably not a lot of— Big brother as much. Not a lot of consumers want to sign up for that service, right? So you're going to kind of scare everybody away. And essentially, that's what we've seen.
当然，那将非常昂贵，而且可能没有太多人会使用。像大兄弟那样。不太多的消费者想要注册这项服务，对吧？所以你会吓走所有人。基本上，这就是我们所看到的。
There's an incredible trade-off curve where the more work you ask the user to do, the more they go away, and oftentimes those conversion falls are very substantial. You're talking about on the order of you ask someone for a document. You can expect maybe 20% fewer people to make it through your process. And if you're sort of a traditional lender that sometimes asks for several documents, you can just sort of do the math on that and look at what kind of conversion loss you're looking at.
有一个令人难以置信的权衡曲线，如果你要求用户付出更多的努力，他们就会流失，有时候这种转换损失非常大。举个例子，如果你要求某人提供一个文件，可能会有大约20%的人无法完成流程。如果你是传统的贷款机构，有时需要提供多个文件，你可以算一下那种转换损失的数量。
And so for us, we think of it as there is this efficient frontier of trade-offs between the information quality you gather and the amount of work that you put in front of your user. And the sort of real magic of our AI system is that we can really achieve much higher levels of this frontier.
对于我们来说，我们认为这是一个有效的贸易折衷前沿，处于信息质量和用户面前投入的工作量之间。而我们AI系统的真正魔力在于我们能够实现更高层次的前沿。
And we can choose between much higher combinations of information quality and ease of process for the consumer. Where for the same level of work, we can get much more information from the consumer. Or for the same level of information, we can do it with much less work to the consumer.
我们可以在信息质量和消费者过程的高度组合之间做出选择。在同样的工作量下，我们可以从消费者那里获取更多的信息；或者在同样的信息量下，我们可以减少消费者的工作量。
That's manifested itself today in our combination of extremely high underwriting accuracy, where we often see multiples of the accuracy that traditional sort of credit models can achieve. And at the same time, we're doing that while over 70% of people that get loans with us are getting those loans in a fully automated instant way, meaning there's no documentation that they had to upload, you know, sort of human touch on the upstart side, no waiting around, no leaving the session, just sort of all one smooth instant digital experience.
今天，我们表现出极高的核保准确性，通常看到我们的准确性是传统信用模型所能达到的准确性的数倍。同时，我们实现了超过70%的贷款客户可以以全自动即时方式获得贷款，这意味着他们不需要上传任何文件，没有任何与Upstart相关的人员，无需等待或离开会话，只需要一个完整的平稳数字体验。
Yeah, I want to talk about that instant for a second, because I've talked to a lot of lenders and particularly in the context of online loans and particularly in the context of, you know, walk up or net new traffic, which my understanding is the vast majority of upstart loans are going to first time borrowers, people without an established relationship that the movement to online with walk up results in very high levels of fraud.
嗯，我想讨论一下那个瞬间，因为我和很多贷款人交谈过，特别是在网上贷款的情况下，特别是在潜在新客户或新流量的情况下。我的理解是，绝大多数Upstart贷款将面向首次借款人，即与贷款人尚未建立关系的人。转向网上贷款后，潜在新客户增加，但也伴随着非常高水平的欺诈。
So as you talk about 70% no documentation, no kind of manual review, how do you think about preventing high rates of fraud from entering the systems that's happening? Because that's kind of, I think, an astounding stat for a walk up traffic business in an online context versus what most lenders have typically seen.
当您谈论无文件、无手动审核的70%时，如何考虑防止高比例的欺诈进入系统中呢？因为在线交易业务中，这是一种令人震惊的统计数据，与大多数贷款人通常看到的不同。
Yeah, I mean, the vast majority all well over 90% of business that we do is for sort of first time customers in the whole upstart ecosystem and for a particular bank partner of ours. And of course, there are, there are a lot of fraud attempts in that system.
嗯，我的意思是，我们所做的绝大部分生意都是为了那些第一次来到整个初创生态系统以及我们的特定银行合作伙伴的客户。当然，在这个系统中有很多欺诈企图。
And so I think one of the challenges with moving from kind of traditional retail lending over to online is you just have this sort of huge exposure to fraud risk. And we've seen many different kinds of fraud risk.
因此，我认为从传统的零售放贷转向在线放贷面临的挑战之一就是面临巨大的欺诈风险。我们已经看到了许多不同种类的欺诈风险。
Of course, sort of first party fraud or someone pretends to be, you know, or a third party fraud where someone pretends to be someone else's first party where someone just has no intention of repaying. There's sort of internet specific behaviors of one of the most common ones is called loan stacking, which didn't really make sense in the kind of analog world where, you know, it would be a lot of work to go to a whole bunch of different banks and try to get loans.
当然，有一种称为第一方欺诈的欺诈行为，或者有人假装成第一方，还有一种称为第三方欺诈的欺诈行为，其中有人假装成别人的第一方，而另一些人则根本没有还款的意图。在互联网上有一些特定的行为，比如最常见的一种就叫做贷款叠加，这在模拟世界中并不太合理，因为你要去不同的银行申请贷款还需要很多的工作。
But online it turns out you can open five tabs and be on five different lenders websites around the same time. And you can get loans with all of them before any of them find out from each other or from the credit bureaus that anything's happened.
在线上，你可以打开五个标签页，在同一时间内访问五个不同的借贷机构网站。你可以在任何人之前，包括从彼此或信用局中得知任何信息的情况下，从它们每个人那里获得贷款。
And so you really have to sort of prevent against all these different kinds of risks. And that's sort of a lot of the special sauce of the system that we've built up is we've built dedicated ML models targeting each of these kinds of risks.
因此，你必须要防范所有这些不同类型的风险。而我们系统中的一大特色就是我们建立了专门针对每种风险的机器学习模型。
We've built them into the right points in the flow where what we really have is sort of a thing of it as kind of a sort of decision tree of at the right sort of moments we're asking the models to sort of past judgment on the probability of certain risks.
我们已将它们构建到流程的正确节点，以便在某种程度上将其视为一种决策树，在正确的时刻要求模型对某些风险的概率进行评估。
And depending on that risk level, we can then take different actions, mitigate that risk. If we success, we do it. Great. If we don't, then there's sort of this question again of whether it would be worth it to sort of take another action. And so at each step, you're sort of asking a series of kind of mathematical questions.
根据风险等级的不同，我们可以采取不同的行动来减轻风险。如果我们成功了，那太好了。如果我们失败了，那么又会有这样一个问题：是否值得采取另一种行动。因此，在每一步中，你都在问一系列数学问题。
And if the answer is the risk is high enough, then you need to sort of take the next action in a series of actions and kind of building that system over the years. We really developed the expertise to keep fraud to really minimal rates.
如果风险足够高，那么你需要采取一系列行动中的下一步，并在多年的时间内逐渐建立起这种系统。我们真正发展了专业知识，将欺诈降到了最低程度。
We're talking tens of basis points of sort of fraud across the platform, which again, in the context of almost completely kind of first time unknown online, fully automated sort of loans is, as we think, a really powerful statistic.
我们在谈论平台上几个基点的欺诈现象，这在几乎完全是第一次未知的在线、完全自动的贷款环境中，是一个非常强有力的统计数字，我们认为。
Interesting. How do you think about what happened during, I mean, the last 12 months have been kind of unprecedented time, both from a macroeconomic environment point of view and from an unemployment, potentially fraud drivers point of view. How have you thought about your model's ability to perform through this kind of unprecedented economic experience, kind of disruption to daily life? And what are the results you've seen? Anything you've learned that you think would be interesting to the listeners about what you've seen in your experience over the last 12, 13 months through this kind of very strange environment.
有趣的。你认为过去12个月发生的事情如何？我的意思是，从宏观经济环境和失业、潜在欺诈驱动因素的角度来看，这是一段前所未有的时间。你如何考虑你的模型在这种前所未有的经济经历和日常生活中的干扰中的表现能力？你看到了什么样的结果？你认为有什么值得听众注意的经验教训，关于你在过去12到13个月的经历中看到的非常奇怪的环境的事情？
Yeah, extremely strange environment. Lending, of course, we always talk a lot about the economic cycle. When times are good, everybody seems to be lending. Sometimes are bad, no one wants to be lending, and when times are good, everyone's just sort of worried about when times are bad. And one of the things we've always believed is that there's sort of this itself, this kind of fascination with the economic cycle and the sort of focus of when the bad part of the cycle is coming, is itself a symptom of sort of an approach to lending that is not particularly robust or predictive.
是啊，这个环境很奇怪。当然，我们总是谈论经济周期。当时机好的时候，每个人似乎都在借贷。当时机不好的时候，没有人想要借贷，当时机好的时候，每个人都在担心坏时机什么时候会到来。我们始终相信的一件事情是，对经济周期的这种着迷和对坏时机的关注本身就是不太稳健或不具有预测性的放贷方法的症状。
It means that lenders aren't really able to predict very well the sort of individual consumers level of risk. They're really just doing a saying, well, overall times are good now, and we assume right now we're going to get a 5% default rate. But when times are bad, that 5 is going to change to 10. And it's sort of just a kind of like almost like throwing paint against the wall approach and sort of when the seasons change, like everything goes from good to bad. Our belief has always been that really the variation between individual borrowers far outweighs the variation between macroeconomic periods. And it's very clear that's the case when you actually zoom into the data and you see that even in the toughest economic climates, the vast majority of people in sort of low risk buckets still end up repaying their loans. The most interesting question is actually which individuals are actually in those low risk buckets and not sort of which period are you in.
这意味着贷方无法准确预测不同个人消费者的风险水平。他们只是估计当前时期总体状况良好，我们假设目前违约率为5％。但是当情况不好时，这个数字就会变成10。这种做法就像随便丢颜料一样，而且当季节变化时，一切都从好到坏。我们始终认为，个人借款人之间的差别远远超过了宏观经济时期之间的差别。当你真正深入数据并查看在最困难的经济环境中，大多数低风险人群最终仍然还清贷款，这一点就非常清楚了。最有趣的问题实际上是哪些个人属于低风险人群，而不是你处于哪个时期。
So now going back to this past year, of course, was sort of a very interesting time not only was it a turn in the economic cycle, but a very unusual one. And essentially what we saw was that we saw certainly a lot of changes to consumer behavior. But in aggregate, we actually saw that the sort of loan performance of the sort of entire ecosystem across all our bank partners, all our sort of capital markets partners, the returns that they were expecting to earn, they pretty much across the board consistently achieved or beat over the course of the last year in spite of pandemic.
现在让我们回到过去一年，当然，这是一个非常有趣的时期，不仅是经济周期的转折点，而且是一个非常不寻常的时期。基本上，我们看到了消费者行为的许多变化。但总体来说，我们实际上看到的是，在我们所有银行合作伙伴、所有资本市场合作伙伴的整个生态系统中，他们预计赚取的回报，他们几乎都在过去一年中持续实现或超越了，尽管我们遭受了疫情的打击。
Now having said that, I think a lot of that does have to do with some of the sort of unique circumstances that happened here, which is another piece of what we've sort of really built into our model. And that's about being able to respond to the sort of macarate, economic changes on really a dime. I mean, when unemployment rates started going up in Q2 of 2020, that was a time where it was really important to be able to respond very quickly and precisely to those sort of changes in unemployment rates. And you wanted to be able to know like in which sectors, occupations, et cetera, there was extra unemployment risks that you needed to be modeling in and how to model it relative to traditional norms. And you wanted your model to handle that because the alternative was sort of getting a group of people to sit around the table and kind of like do some back-of-the-bomb load math and then just sort of have to like take a blunt cut to your credit box.
说了这么多，我觉得其中很多都与这里发生的特殊情况有关，这是我们在模型构建中非常注重的一点。我们必须能够迅速、精准地应对宏观经济的变化，当2020年Q2的失业率开始上升时，这非常重要。你想知道哪些行业、职业等存在额外的失业风险，以及如何将其模型化并反映到传统规范之上。你希望你的模型能够处理这一切，因为另一种选择就是让一群人坐在桌旁进行一些粗略的计算，然后就要削减你的信用额度了。
And that sort of really just means that you are either going to still take on too much risk or you're going to essentially stop running your business during this challenging time. Well, what we found was we were able to continue approving a significant number of people for loans and have those people pay back because they were the right people to lend to. On the sort of reverse side, as things started normalizing, again, you want that system that can respond very dynamically to updating macroeconomic data. That's sort of a system that we built and it sort of worked incredibly well over the last year.
这句话的意思是，这就意味着你要么继续承担太多风险，要么在这个困难时期停止经营你的业务。我们发现我们能够继续为大量人批准贷款，并让这些人还款，因为他们是合适的借款人。另一方面，随着事情开始恢复正常，你需要一个能够非常动态地响应更新的宏观经济数据的系统。这是我们建立的一种系统，在过去的一年中非常成功。
Is that system predicting macroeconomic conditions or how do you think about what you're doing to bring macroeconomics into your risk profile? I get that the individual is greater, but you're still talking about ingesting some sort of macroeconomic status into your decisions. How does that work?
那个系统是预测宏观经济状况，还是您如何考虑将宏观经济纳入风险档案中的做法？我明白个人更重要，但您仍在谈论将某种宏观经济状况纳入决策过程中。那是如何运作的？
Yeah, so we were not in the business of predicting macro. That is sort of a whole business in of itself. We're very happy to be. We're happy to be. Yeah. We're super happy to be market neutral with respect to macroeconomic.
嗯，所以我们不从事宏观预测的业务。那是一个完整的业务。我们非常高兴能够这样做。我们很开心。是的。我们在宏观经济方面市场中性，感到非常高兴。
So what we want to do is we want to take market consensus. And so there are a variety of sources for market consensus statistics, both things that have already happened and things that are expected to happen in the near future. And we take those and we simply plug them into a system that's been fit against historical metrics, meaning we've taken things like historical charge off rates on consumer credit across different asset classes, historical levels of unemployment across different industries and occupations.
所以我们想要做的是获取市场共识。有许多来源可获得市场共识统计数据，包括已经发生的和预计在不久的将来会发生的事情。我们将这些数据简单地输入到一个已经根据历史指标进行匹配的系统中，这意味着我们已经考虑了不同资产类别的消费者信贷违约率、不同行业和职业的历史失业率水平。
And we've built models that connect these things to the way that they would impact our particular categories of loans. And so we've essentially built that into our model so that as soon as the latest unemployment numbers are released on a weekly basis, on a monthly basis, where the latest sort of monthly projections are released from various consensus groups of economists. Those numbers just essentially just get fed into the model, update the macroeconomic assumptions and then you're properly calibrated to the sort of market consensus on macroeconomic numbers.
我们建立了模型，将这些事物与它们对我们特定的贷款类别的影响联系起来。所以，我们已经将这些内容纳入我们的模型中，这意味着一旦每周发布最新的失业率数据，或者每月发布来自各种一致性经济学家组织的最新投影，这些数据就会自动输入模型中，更新宏观经济假设，然后我们就能够准确地校准市场对宏观经济数据的共识。
That's a really interesting approach. I like that. So I want to ask one of the you use the phrase bank partners a couple times. Just wanted to dig into the way you think about working with banks versus a lot of the fintechs that are out there. I know pursuing chargers, buying banks, becoming banks, what is the business model for upstart in terms of working with banks or the bank partners that you talked about?
这种方法真的很有趣，我喜欢。所以我想问你们其中一个人几次使用了“银行合作伙伴”这个短语。我想深入了解你在与银行合作方面的想法，与目前很多金融科技公司的情况相比。我知道追求收费、收购银行、成为银行，对于upstart而言，在与银行或你们所提到的银行合作伙伴合作方面，业务模式是什么？
Yeah, we're a technology company through and through. We've been doing this a long time and I think every time we've had an opportunity to sort of think about which path in the road we want to go down, it's been very clear for us that we want to be more and more of a technology company and that's what we're good at. We're not good at being a bank. We're not good at any of the things that a bank is good at.
是的，我们是一家纯粹的科技公司。我们已经从事这项工作很长时间了，每当我们有机会思考我们想要走哪条路时，对我们来说非常清楚，我们想成为越来越多的科技公司，这是我们擅长的。我们不擅长做银行，也不擅长银行擅长的任何事情。
But we are very good at building technology and we believe very strongly that the way that technology is moving, especially in the field of AI, it's going to be a sort of technology field where you sort of need aggregation and returns the scale that's true across data scale. It's true across the R&D investment scale. It just doesn't really make sense for every single bank or every single business in a certain industry to really be developing their own AI engines and tools and data sets.
但我们非常擅长建造技术，并且非常坚信，尤其是在人工智能领域，技术的发展将成为一种技术领域，在这种领域中，您需要对数据规模进行汇集和回报。这对于数据规模和研发投资规模都是正确的。对于每个银行或某个特定行业的每个企业来说，确实没有开发自己的AI引擎、工具和数据集的真正意义。
It's just not going to be scaled enough to be as good as one that is essentially shared across many in that industry. And so that's what we aim to be. We aim to be the common technology provider and builder. And we think actually banks are good at a lot of things. Those things sometimes don't include building technology, but they do include having great customer relationships spanning many products, having the sort of customer trust and loyalty and sort of brand understanding that comes with the sort of retail presence comes again with having those different products has that sort of relationship with with checking accounts, banking accounts, all of those things are things that that we don't do. We don't want to get into doing.
我们的目标是成为行业技术的共同提供者和建造者，因为我们知道单一公司的规模是不够影响的。我们认为银行擅长很多事情，但构建技术可能不是他们的长项。然而，银行在拥有多种产品和良好的客户关系方面表现出色，他们享有客户的信任和忠诚，也拥有广泛的零售业务和这些产品的亲密关系，如支票账户、银行账户等。我们并不想涉足这些领域。
But we do want to support and enable our bank partners to be very, very good at lending. And we think that piece of it needs a strong component of AI. And that's what we bring to the table.
我们确实希望支持并帮助我们的银行合作伙伴在贷款方面做得非常好。我们认为这其中需要强有力的人工智能组成部分。这就是我们所带来的。
Got it. I appreciate your thoughts. It's been an interesting conversation, Paul. I think hopefully interesting for the listeners in terms of what upsides doing that's unique in the space.
明白了。我很感谢您的想法。与您的交谈非常有趣，Paul。我认为在独特空间所做的事情有希望引起听众的兴趣。
I have three questions that I usually ask guests at the end of the podcast, but I'm going to add a fourth for you that you don't know about. So here you go.
我通常在播客结束时向嘉宾提出三个问题，但现在我要为你加上第四个问题，你不知道的。所以，让我问一下。
You know, so you came out of college, well, literally out of college because you hadn't graduated when you started an upstart and you've been in the basically the consumer lending space for a decade now, more or less, what's been the number one thing that surprised you about the way the space is operated since you when you kind of came in from an outsider with I'm going to guess pretty limited preconceived notions about the industry was.
你知道的，你从大学出来了，实际上是从大学里出来的，因为你在开始一个新公司时还没有毕业，你已经在消费借贷领域待了大约十年了，那么有关空间运作的第一件让你惊讶的事情是什么呢？你作为一个从外面进来，对这个行业没有太多先入为主的观念的人，看到了什么？
What's been the biggest surprise? Yeah, for sure, I think the biggest surprise is going to, I mean, I expected there to be a lot more people trying to build what we're building. I mean, it just seemed to me like such an incredible opportunity to create value for lenders and an incredible opportunity, a great value for consumers.
最大的惊讶是什么？是的，当然，我认为最大的惊讶是，我预计会有更多的人试图建立我们正在建设的东西。对我来说，这似乎是一个极好的机会，可以为放贷者创造价值，也可以为消费者创造巨大的价值。
It almost is like bang your head on the table. Obviously, this is probably the best application for AI that you could have period. I mean, best in terms of like it's sort of this combination of all the kind of pieces are there, like the sort of high volumes of data, the high economic opportunity, the sort of real time decision making.
这就像是把头猛撞在桌子上一样。很明显，这可能是你能有的最佳人工智能应用了。我的意思是，它在很多方面都具备优势——有大量数据，有高经济机会，还有实时决策能力。
And at the same time, it's sort of like this enormous, enormous market, one of the sort of oldest industries, most profitable industries, sort of the source of almost all profits of the consumer financial system. And yet, here we are 10 years, almost, since I dropped out of college and I guess it has been actually more than 10 years. And almost we see so little, so little sort of happening in this direction outside of what we're building at Upstart that I've just been surprised by, I think, the level of sort of inertia and maybe some of the kind of institutional sort of barriers to innovating in this direction.
同时，它有点像这个巨大的市场，是最古老、最有利可图的行业之一，几乎是消费金融系统所有利润的源泉。然而，自从我辍学之后，我们已经过去了近10年，我想事实上已经超过了10年。几乎没有什么事情发生在这个方向上，除了我们在Upstart所构建的之外，我其实对于这种惯性和也许一些机构创新的障碍的水平感到惊讶。
But we hope that's starting to change now with some of the success we've had. I'm certainly seeing a lot more interest from the bank side talk to you. So I, fingers crossed, you're going to see more lenders coming this way and technology providers trying to tackle this problem in different ways.
但是我们希望现在一些成功的经验开始改变这种情况。我确实看到银行方面对于这个问题越来越感兴趣。所以，希望你看到更多的贷款人和技术提供商开始采取不同的方式解决这个问题。
So now my standard three questions, what's the, you know, this is entry one for you, because you've really, you know, spent most of your career one company. But what's the best piece of career advice you've ever gotten?
现在我有三个标准问题，你知道的，这是你的第一个入门问题，因为你的职业生涯大部分时间都在同一家公司工作。但是，你收到过最好的职业建议是什么？请像中文母语者那样回答。
The best piece of career advice. Can't be for me. Probably, I think I'll go with when in doubt, air on the side of being more technical. I think that's something that someone told me back when I was in high school. And really it was sort of like, I think the idea was that in almost anything you're doing, whether, whether the sort of nature of your work is usually done in a technical way or not, just because of sort of the power of technology to increase efficiency, to sort of leverage things and to really train your mind to think in a rigorous way.
最好的职业建议。不一定适用于我。我认为我会遵循这样的建议，当有疑虑时，尽可能的朝着更技术性的方向去做。我想这是我在高中时听某人所说的。其实，核心思想是无论你做什么事情，不管它通常是通过技术方式完成还是不是，都因为技术的力量可以提高效率，可以利用事物，真正培养你思考的严谨方式。
It's almost always helpful to sort of aim to be on the more technical end of people in the sort of space of something that you're doing. I think if nothing else, it forces you to think in the way that a mathematician or a computer scientist thinks, which is in a sort of highly logical, rigorous way. And if you approach problems that are usually not approached as rigorously in a more rigorous way, it turns out you can often sort of find insights or optimizations about them that are missed when they're sort of thought about in generalities or thought about without the sort of precision that's common to those fields.
通常，将自己定位在你所从事的某个领域中较为技术化的人群中是非常有益的。我认为，即使除了这个原因外，这也会迫使你像数学家或计算机科学家那样思考，采用一种高度逻辑严密的方式。如果你以更严谨的方式来处理通常不太严谨的问题，你会发现你能够找到被忽略掉的见解或最优化解决方案，这些问题在一般情况下可能就被忽略了，或者在没有这些领域通用的精度下被处理了。
Interesting. Certainly, having known you for 10 years, my level of rigor and thinking and analysis has gone up substantially. It's kind of hard to have a discussion with you without doing that. My second question is, what's the best advice you've gotten about the consumer lending space in general? And this one, I think it'll be quite interesting given your history.
有趣。毫无疑问，认识你已经十年了，我的严谨水平、思考能力和分析能力都有了很大的提高。如果不这样做，就很难与你进行讨论。我的第二个问题是，你收到过有关消费贷款领域的最佳建议是什么？考虑到你的历史，这个问题很有趣。
Yeah. The best piece of advice we've gotten about the consumer lending industry. Have you ignored all the advice that you got and just charted your own path for some time? No, no. Certainly, and in our years, we have spent a lot of time reinventing the wheel on many things. I mean, for example, we didn't know about direct mail when we started.
是的。我们得到的有关消费信贷行业的最好建议。你忽略了所有的建议，自己探索了一段时间的路吗？不，不是的。当然，在我们多年的经营中，我们花了很多时间在很多事情上重新发明轮子。我的意思是，例如当我们开始时我们并不知道直接邮件。
And for many years, we just thought there's no way that direct mail can be a thing that is done in the 21st century as sort of a successful mode of advertising. But it very obviously is a sort of successful way that advertising and consumer credit is done. And the sort of best advice has been something along the lines of, don't be clever and innovative in stuff that you don't need to be clever and innovative about. When we started, we really tried to sort of make everything about what we did different.
很多年来，我们一直认为直邮作为成功的广告推广方式在21世纪已经行不通了，就好像是一种不可能的事情。但是事实上，它很明显是一种成功的广告推广和消费信用的方式。而最好的建议是：在你不需要聪明和创新的地方不要刻意追求。当我们刚开始的时候，我们真的尝试让我们所做的一切都与众不同。
And that meant all the sort of different terms of the loan, the way we structured, servicing fees, and sort of every last part of it, we sort of tried to like rethink. And most of those were like minor, minor optimizations.
那就意味着我们重新考虑了所有与借款有关的不同条件，包括我们的结构方式、服务费用，以及每一个具体的细节。我们试图重新思考它的每一个部分，而多数只是微不足道的小优化。
And of course, sort of the core thing we were doing was like a really big optimization. But because of all the sort of small different things we did, we found ourselves constantly tangled up in kind of the greater like ecosystem sort of has a set of norms of how they operate. You know, when you think about rating agencies and sort of regulators and sort of all the sort of different things that they're used to.
当然，我们所做的核心事情是一个非常大的优化。但由于我们做了许多不同小事，我们发现自己不断陷入更大的生态系统中，这个生态系统有一套操作规范。你知道，当你考虑到评级机构、监管机构和其他一些不同的东西时。
And you kind of want to isolate your sort of innovation down to the things you want people to focus on and not get sort of spend all your time hung up on the sort of innovations that don't really move the needle, but do sort of create a lot of confusion and sort of headaches for people. And I think we did a lot more of that early on and a lot less now.
你有点想把你的创新独立出来，让人们聚焦在你想要的事情上，不要把时间都花在那些不会真正推动变革，但会带来很多混乱和烦恼的创新上。我认为我们在早期做了更多的这种事情，现在则少了很多。
Sounds like finding that efficient curve you were talking about between effort and accuracy and collecting data. Maybe you didn't quite have that right. And the product organization and the early days.
听起来像是在寻找你谈论过的那个在努力和准确性之间有效的曲线，并收集数据。也许你没有完全理解。以及产品组织和早期阶段。
And then my last question is always, what's your bold prediction for the future? Can be lending, FinTech, Life in general, what's your bold prediction? You seem like a guy who makes bold predictions. So give me a nice bold prediction I can bring you back and hold you to.
然后我最后一个问题一直是，你对未来有什么大胆的预测？可以是贷款、金融科技、生活总体，你的大胆预测是什么？你看起来像一个会做大胆预测的人。所以给我一个好的大胆预测，我可以带回来并向你解释。
A bold prediction. I usually see you lacking for bold Paul or predictions, frankly. You don't have to give me odds if you don't want, but I'll take them. Prediction without odds is not much of a prediction. That goes to that rigorous thinking point you made earlier.
一个大胆的预测。坦率地说，我平时看到你缺乏大胆的保罗或预测。如果你不想给我赔率，那也没关系，但我会接受的。没有赔率的预测就不是太有意义的预测了。这就回到了你之前提到的严谨思考的观点。
I didn't think this would be the question to stump you. I don't think I have a good answer that I want to commit to.
我没想到这个问题会把你难倒。我不觉得我有一个好的答案，我想要确定下来。
No answer, no bold predictions. All right. You need a mathematical model to make our predictions and we don't have one on the fly. I'll take it.
不回答，不会大胆预测。好吧，我们需要一个数学模型来作出预测，而我们并没有一个即刻可用的模型。我明白了。
Well Paul, I appreciate your joining us. I think it's a very interesting discussion on application of AI across the lending life cycle. Concepts around underwriting and particularly like to your point on macroeconomic where the difference in individual risk is much greater than the variance among individuals during an economic stress period. That was a really interesting point. So thanks for joining us. Appreciate your make of the time.
保罗，谢谢你能加入我们。我认为关于人工智能在借贷生命周期中的应用非常有趣。特别是在核保方面，我很喜欢你提到的宏观经济概念，在经济压力时期，个人风险的差异远大于个人之间的差异。这真是一个非常有意思的观点。所以感谢你加入我们，感谢你抽出时间。
OK, awesome. Thanks so much.
好的，太棒了。非常感谢。
Upstart partners with banks and credit unions to help grow their consumer loan portfolios and deliver a modern all digital lending experience. As the average consumer becomes more digitally savvy, it only makes sense that their bank does too.
Upstart与银行和信用联盟合作，帮助让他们的消费贷款组合增长，并提供现代化的全数字化贷款体验。随着普通消费者变得越来越数字化，银行也应该跟上。
Upstart AI lending platform uses sophisticated machine learning models to more accurately identify risk and approve more applicants than traditional credit models with fraud rates mere zero. Upstart's all digital experience reduces manual processing for banks and offers a simple and convenient experience for consumers.
Upstart AI借贷平台使用高级机器学习模型来比传统信用模型更准确地识别风险并批准更多申请人，欺诈率几乎为零。Upstart的全数字化体验减少了银行的手动处理，并为消费者提供了简单方便的体验。
Whether you're looking to grow and enhance your existing personal and auto lending programs or you're just getting started, upstart can help. Upstart offers an end to end solution that can help you find more credit worthy borrowers within your risk profile with all digital underwriting, onboarding, loan closing and servicing. It's all possible with upstart in your corner.
无论您是想要扩大和增强您现有的个人和汽车贷款计划，还是刚刚开始，upstart都可以帮助您。 upstart 提供了一个端到端的解决方案，可以帮助您在您的风险档案中找到更多信用有价值的借款人，包括数字化核保、上线、贷款关闭和服务。有了 upstart的支持，这一切都是可能的。
Learn more about finding new borrowers, enhancing your credit decision process and growing your business by visiting upstart.com slash 4-banks. That's upstart.com slash 4-banks.
了解如何找到新的借款人、加强您的信用决策流程并且增加您的业务，请访问 upstart.com/4-banks。就是 upstart.com/4-banks。
You've been listening to leaders in lending from Upstart. Make sure you never miss an episode. Subscribe to leaders in lending in your favorite podcast player. Using Apple Podcasts, leave us a quick rating by tapping the number of stars you think the show deserves. Thanks for listening. Until next time.
你一直在听Upstart的贷款领袖们。一定要不错过任何一集哦。记得在你最喜欢的播客播放器上订阅贷款领袖，比如苹果播客，点击你认为这个节目星级的数量来给我们评分。感谢你的收听，下次再见。