The Opportunity with AI Driven Lending w/ Paul Gu
发布时间 2021-05-04 09:00:00 来源
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 in sort of tens or hundreds of relationships in our variables interacting together in the relationships between them.
为了在借款人层面上，以月为单位实际制定精确的个性化预测，我们需要使用先进的机器学习算法。这些算法非常灵活，能够在我们的变量之间寻找模式，以及寻找它们之间的联系，这些变量之间存在几十甚至上百的关系。
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.
你正在聆听来自Upstart的领袖们的借贷播客，致力于帮助消费者贷款业务增长和改善产品优化。每周，听取来自金融行业的决策者对未来借贷行业的见解，包括数字化转型的最佳实践等内容。接下来，让我们开始节目吧。
Hi and welcome to leaders in lending. I'm your host, Jeff Keltner, and I'm joined today by Paul Gooh. Paul is the co-founder and head of product at upstart. So I've known Paul for quite a while. Paul, thanks for joining me today. Of course, happy to be here.
嗨，欢迎来到领先的贷款公司。我是您的主持人Jeff Keltner，今天我和Paul Gooh一起连线。Paul是Upstart公司的联合创始人和产品负责人。我和Paul认识已经很长时间了。Paul，谢谢你今天能和我连线。当然，很高兴能在这里。
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 was something you had to go and do.
嗯，所以我想要从我们初次相遇的时候开始说起，或者说，从你加入Upstart开始说起。当时你还是耶鲁的本科生，在学习有趣的事物。你决定放弃学业，创建一家贷款行业的公司，这或许并不是每个十几岁的青少年所能想到的。所以，可以跟我详细地讲解一下，是什么激发了你对这个领域的兴趣，让你觉得有必要去做这样一件事情？
Yeah, so when I was in school, I was studying computer science and economics spent time preparing myself for life in a quant hedge fund world. It spent 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 traded securities. And that was great, but it wasn't obvious that that was solving important problems for a lot 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，当我在学校时，我学习计算机科学和经济学，花时间准备自己进入量化对冲基金世界的生活。我在那里呆了一段时间。基本上，我意识到有很多非常聪明的人应用新技术来解决一个非常狭窄的问题，即各种交易证券的稍微低效的价格问题。这很好，但并不明显这解决了许多真实普通人的重要问题。我认为，如果你可以简单地采用相同的技术并将它们应用于会影响真正人们的问题上，那么你可以做出非常重要的事情。
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 adjacency, instead of in corporate 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, 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.
那就是故事的前半段了。然后，当我开始思考这个应用可能来自哪里的时候，显而易见的位置是最接近的邻近地区，而不是从企业财务转移到个人财务。我越了解这个问题，就越意识到许多人非常难以获得信贷，实际上除非他们拥有丰厚的信用历史，否则任何没有出生有钱的人都难以获得贷款。当然，这意味着对于绝大部分人，他们的能力至少受到了某些限制，不能在他们最需要贷款的时候获得信用。如果我们简单地应用已经被证明在其他领域非常强大的机器学习和人工智能技术，并将它们应用于这里，我们就能为人类做出非常重要的事情。
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, start 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?
我喜欢你们一开始的使命，它直接服务于消费者。当我看到消费贷款领域时，我很好奇有很多种类的贷款。个人贷款，无抵押贷款，可能是银行称之为个人信用贷款，总体上它是消费者贷款生态系统中一个较小的领域。当你们说，嘿，我们可以使用这些技术来改善访问时，为什么选择了这个领域？为什么在刚刚开始时，你们说，这是我们要把时间集中在这个领域，而不是像房屋贷款、抵押贷款、车贷等更大的类别？
Yeah, great question. Principally two reasons, 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 it in the place where it's going to matter the most and be the hardest. 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. 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. 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.
嗯，很好的问题。基本上有两个原因，第一个原因是，如果您想展示机器学习应用于很多公司、很多银行长期使用的东西的有效性，您需要在最重要的和最困难的地方应用它。这就是非担保个人贷款。如果您考虑其他任何资产类别，如汽车贷款、房屋贷款，它们都有支持。即使信用卡也是在未来使用信用卡的其他效用的支持下支持的。当您给某人20000美元并说，请还给我，您真的没有任何支撑。这意味着您必须非常擅长决定您将借给谁，不借给谁，否则您很快就会破产。这意味着如果我们能够使用人工智能更好地评估这里的风险，我们将能够为借方和消费者创造难以置信的经济收益。这就是我们所能够做的。
The second reason is that from the consumer's perspective, the unsecured personal loan, Bides 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. It's something that you really can get at any time for any reason. And in that sense, it sort of was the broadest product.
第二个原因是从消费者的角度来说，无担保个人贷款自然而然地成为了最灵活的贷款方式。它可以用于任何目的，不仅仅是你在购买汽车或房子等特定时刻所能得到的。它真的是一种随时可以申请，无论什么原因都可以获得的产品。从这个意义上讲，它是最广泛的产品。
It's sort of 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 than 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.
这是一个很自然的起点。我们一直认为，如果我们先做这些第一和第二件事，那么从个人贷款到其他贷款产品会更容易，而不是反过来，如果你从非常安全和非常有限的东西开始，那么很难走出那个框框，去到其他领域。
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.
有意思。我认为很少有贷款人会从风险最大的领域开始探索这个领域。但是我明白你的意思，科技确实对这个领域影响最大。
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，我知道许多贷方已经谈论了机器学习或人工智能。我们在如何审批信用的背景下经常谈论机器学习或人工智能。但是，请跟我谈一下你认为传统贷款方式向实际上由机器学习或人工智能驱动的贷款方式的变革以及你如何以不同的方式去识别风险的问题。
Yeah. So 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.
是的。所以，这些词经常被用作流行语，有时由于这个原因它们失去了意义。但当我们使用它们时，我们实际上想表达主要两个意思。
The first is that we've taken an approach of using an immense quantity of data in the problem. So unlike in traditional underwriting, where only a handful of data points are used, we use over 1,000 variables. We use millions of rows of repayment data to actually train the model. And those data points that we're using are a mix of both traditional and non-traditional data points.
首先，我们采用了大量数据的方法来解决问题。与传统的核保方式不同的是，我们使用了超过1,000个变量，而不仅仅是少数数据点。我们使用数百万行还款数据来实际训练模型。我们使用的这些数据点是传统和非传统数据点的混合。
We really are seeking the broadest possible set of data that can be used to drive signal about who's going to pay back low. And the second half is really about the way that we learn from that data.
我们真的正在寻求尽可能广泛的数据集，以推动关于谁会低息还款的信号。而第二部分则是关于我们如何从这些数据中学习的方式。
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. 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.
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经典技术，比如回归分析技术，这实际上意味着通过数据适配一条直线。现在，问题是虽然它们很容易绘制和理解，但它们并不非常适用于世界，因为世界上发生的大多数事情都不是直线。
It depends on actual interactions between different variables between the different factors that drive whether a person's 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. 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 state-of-the-art machine learning algorithms.
这要取决于各种不同因素之间的实际交互作用，包括驱动一个人是否会还款、何时会还款、是否会提前还款以及何时会提前还款。为了在借款人层面、月度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。
These are algorithms that actually are highly flexible, are able to find patterns in tens or hundreds of relationships in our variables interacting together in the relationships between them. And when we combine that big data approach with the modern algorithm approach, that's really what we call AI lending.
这些算法实际上非常灵活，能够找到在众多变量相互作用的关系中的模式，这些变量之间有几十个或几百个关联。当我们将这种大数据方法与现代算法方法相结合，这就是我们所说的AI借贷。
Interesting. When you think about the uplift you've got, and we can talk about the actual increases in accuracy in how you see that, but when you talked about more data, both in terms of how you always 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. I know there's also the use of alternative data points and models.
有趣。当你考虑到你所获得的提升时，我们可以谈论实际的准确性增加是如何体现的，但当你提到更多的数据时，无论是更多的列，每个申请人更多的数据点，还是更多的历史数据查看，你总是会想到数据就像电子表格。我知道我们也可以使用其他数据点和模型。
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 split? I mean, how do you think about what's really driving the efficacy of the models' upsides building?
你如何看待这些不同的组成部分以及每个部分对提高准确性的重要性？其中有一个部分真正承担了大部分的工作，推动了结果吗？我的意思是，你如何思考模型建立的有效性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可以协调配合工作。这不仅是一个通用的口头陈述，实际上非常重要，因为如果你考虑使用一种高效的机器学习算法，而只使用五个变量，那么它对你来说将没有多少益处，因为你几乎没有提供足够的数据供其工作。
It's not going to have enough to draw and to actually use this sort of powerful, complex nonlinear learning patterns that it's capable of. Similarly, if you were to take over 1,000 variables and try to plug them into a straight line model, it wouldn't work.
这个模型不够强大，不能实际运用到它所能支持的复杂的非线性学习模式中。同样地，如果你试图将超过1000个变量直接放入一个直线模型中，也是不可能的。
The model would also not be able to tell you that 900 of the 1,000 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.
这个模型也不能告诉你，有900个变量毫无用处。因为实际上，如果你说，我只会直线地使用这个变量，与其他909个变量完全独立。大多数这些变量实际上并不是很有用。
Most of the data is useful only in the context of certain other variables, and usually in a way that's highly nonlinear. 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.
大部分数据只有在特定变量的情境下才有用，而且通常是高度非线性的。因此它们确实是相辅相成的。不过，如果必须分开使用，则对于我们某些产品而言是必须的。我们在这些产品中出售模型的子集，某些人可以仅访问传统信用数据上的算法，而无需使用所有的替代数据。
We sell that product because if you had to pick a single thing that was most important, you would go with the more powerful and 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.
我们销售这个产品是因为如果你必须选择一件最重要的事情，那就是使用更强大和增强的学习算法，并运行传统的信用数据。这样的第一步可以给你一个非常显著的准确性提升，但当然，叠加额外的数据可以带你更远。
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 in a couple of extra data points, but you're digging a little deeper than typical.
让我稍微深入一下，因为我知道你说的传统信用数据，很多人认为传统信用数据只是从信用档案中提取10至15个数据点。当你说传统信用数据时，你真正的意思是什么？因为我了解到，这方面可能更为复杂，不仅仅是信用评分或几个额外数据点，你可能要更深入挖掘。
Yeah, 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.
嗯，所以当一个贷方检查信用时，他们实际上会得到两种不同的东西，确切地说是三种不同的东西。第一件事，最基本的东西是他们会得到一个消费者与信用有关的每一个交易的记录。每一个付款、违约、拖欠、申请信用等，所有这些都记录在一个人的信用历史中。
The second thing they get is they get the credit euros interpretation of that data. The credit bureau will 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. Now have a handful of sort of common summary statistics about a person's credit report.
他们得到的第二件事情是他们得到了信用欧元对这些数据的解释。信用局会说，我们把这个庞大的历史总结成几个数据点。最常见的是他们会告诉你，例如，这个人在过去六个月中有三次信用查询，或者这个人在过去两年中两次拖欠款项。现在我们有了一些关于一个人信用报告的共同总结统计信息。
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. 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.
现在第三个步骤是他们实际上会将同样的信息传输到由第三方评分机构（最常见的是FICO）构建的模型中，然后输出得分，这些得分基于一些汇总统计数据，例如过去六个月内的查询次数。
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.
所以，你可以得到三种信息，第一种是一个人信用生活的详细历史记录，第二种是从信用局得到的统计信息总结，最后是极为概括的信用评分。在传统的信用评分中，很多方法都是依靠后两种类别，即统计信息总结或者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.
当我们在Upstart谈论信用数据时，我们是指完整、丰富、原始的历史记录，这些记录通常不太结构化，可以按照几乎无限的方式进行排列组合。因此，使用机器学习算法实际上可以做很多非常有趣的事情，以找出如何从这些历史数据中生成最多的信号，而不完全适应数据，或者完全导致服务器崩溃，因为你正试图运行一些计算量过大的东西。
And that's where I think a lot of the interesting work we do with traditional credit data actually happens.
我认为许多有趣的传统信用数据工作实际上就在那里发生了。
So you talk about servers melting down, I hadn't thought of that. We have servers in the Upstart that would melt down.
所以您说服务器会崩溃，我没有想到那个。我们在Upstart中有一些会崩溃的服务器。
I think they're all somewhere in the cloud. But they're not our servers. They're not our servers. They want to melt down Amazon servers and burn down a database.
我想它们都在云里的某个地方，但它们不是我们的服务器。它们不是我们的服务器。它们想要熔化亚马逊的服务器，烧掉一个数据库。
But how do you think about the trends in 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?
“你怎么看待科技趋势给我们带来的变化呢？我是说，从科技角度来看，这些能力现在与五到十年前相比有什么原因呢？”
There's something that's kind of shifted that's open this up to be more accessible or possible today.
有些事情已经改变了，让这件事更容易或今天更可能实现。
Yeah, absolutely. I'd say the first big thing is compute availability. When I say, and when I sort of jokingly said, servers melting down, I of course didn't actually need them melting. Yeah, 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，绝对没错。我认为第一个大问题是计算机可用性。当我说“服务器崩溃”，并且我自己开玩笑时，当然我并不真的希望它们崩溃。我实际上的意思是你可能没有足够的计算资源来在你期望的时间范围内完成你想做的工作。
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.
即便在今天，在一个可以随意扩大亚马逊云上拥有的EC2机器数的世界里，我们实际上仍然受到我们可以访问的计算资源数量的限制。这种限制会在我们遇到计算问题时表现出来，我们开始看到运行时间变得越来越长。
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, meant we faced run processes spanning 24, 48, 72 hours for a single run of the model training.
我们的完整模型训练过程经常会遇到一个问题，就是学习算法的复杂性与我们可用计算的数量和效率相互作用，这导致我们需要使用 24、48 或 72 小时的时间来运行一个模型的训练。
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 universal 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.
现在你想象一下，你只做一次还好，但实际上你不想只做一次。你真正想做的是找到最佳模型，也就是在所有可能的模型中最具预测性的模型。这意味着你需要进行搜索。而当你想进行搜索时，你需要经历许多不同模型的迭代过程。
If each run is taking you 48 hours and you've got 1,000 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. 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 shortcut the search so that instead of taking 48 hours, it only takes 24 hours and instead of doing 1,000 sort of searches, we only do have to do 20 searches. And suddenly you've got a problem that's much more tractable.
如果每个运行需要48小时，而你需要完成1,000个不同的运行，那么你需要花费几年的时间来寻找一个模型。当然，这样做的效果不会很好。现在，这就是今天的情况，我们所做的很多投资都是为了提高学习算法的效率，找出如何快速搜寻的方法，这样就可以将48小时缩短为24小时，同时搜索的次数也可以从1,000次减少到20次。这样，你就可以更轻松地解决问题了。
Now if you rewind back 10 years, you're looking at a compute that's a fraction, 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. 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.
如果你回溯十年前，你会发现那时候的计算机相当于今天同样价格下可获得的计算机的零头，几乎少了一个数量级。所以事情变得困难得多了。当然，与基础设施的改进相平行的是，算法技术上也有很多进步，包括在理论数学和统计学方面以及在实施计算机科学方面都有所提高。
And of course, we benefit from a lot of those advances and we're applying them to the sort of unique problems of lending. 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.
当然，我们从许多这些进步中受益，并将它们应用于贷款的独特问题。是的，那两件事是如何应用计算机和算法的可用性相互作用的，因为有可用的计算机来使用它们，它们有点互相促进。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.
另一件我知道的事情，抱歉，有点重点不只是在信用批准上，而且实际上是关注流程简化，对，使命宣言就是"轻松获得信用"，是基于真正风险的轻松获得信用。
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 and the lending process?
你对这些技术在减少工作难度方面的应用有什么看法？这是什么意思，你如何减少摩擦和借款流程的努力？
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.
是的，你知道，我开始真正认为这两个不是两个截然不同的东西，而是属于同一个东西的一部分，也就是一方面，当你考虑到保险的时候，实际上它就是你所做的所有事情，以确定这笔贷款的风险。
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 they're giving a loan to the high chance of paying it back. 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.
无论是在流程的前端还是后端进行，都无所谓。这都是为了一个共同目标，那就是确保贷款对象有很高的还款机会。无论是通过查看他们的信用报告还是核实他们的收入，这都是为了同一个目的，也就是核保。
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.
现在，相反的一面是，一直以来都有一些收集人们更多信息的方法，这些方法会带来高昂的费用和阻力。在某种程度上，这两者之间几乎有完美的权衡。
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 that the sort of person is going to pay you back, even without any fancy AI, right? 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?
你可以决定每天跟随一个人一生，即使没有任何高级人工智能，你也很有可能想出那种人会回报你，对吧？但是，当然，这将非常昂贵，也许并不多见。类似大哥哥的服务，不会有很多消费者想要注册，对吧？
So you're going to kind of scare everybody away. And essentially, that's what we've seen. 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.
所以，你会有点把每个人都吓跑。基本上，这就是我们所看到的。有一条惊人的权衡曲线，即你要求用户做的工作越多，他们就越会离开。往往这些转化率的下降非常显著。
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 are 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.
你说的是当你向某人要文件时的情况。你可能会预期有20%的人流程无法完成。如果你是一个传统的放贷者，有时会要求几份文件，你可以算一下，并看看你将面临的转化率损失。
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, meaning we can choose between much higher combinations of information quality and ease of process for the consumer.
所以对我们来说，我们认为存在这种高效的界限，即收集信息质量和为用户提供的工作量之间的权衡。我们AI系统的真正魔力在于，我们可以实现更高水平的这个界限，这意味着我们可以在信息质量和消费者的流程易用性之间选择更高的组合。
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. 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. But 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, no sort of human touch on the upstart side, no waiting around, no leaving the session, just sort of all one smooth instant digital experience.
我们能够从消费者那里得到更多的信息，而不需要做更多的工作，或者在相同的信息水平上，我们可以减轻消费者的负担，做更少的工作。这表现在我们极高的承保准确率上，我们通常能看到传统的信用模型所能达到准确度的倍数。但同时，我们70%以上的贷款借款人可以通过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、不需要向Upstart提供任何信息，完全由数字化体验来完成。
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 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. 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? So 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.
啊，我想谈一下那种瞬间，因为我已经和很多放贷人交谈了，特别是在网上贷款和突然出现的新客户交易中，我了解到大多数新贷款都是第一次借款人，即没有建立关系的人们，走向网上市场会导致非常高的欺诈率。所以当你谈到70%的没有文件和手动审核的情况时，你如何考虑避免高比率的欺诈进入系统呢？这对于网上市场中的突然出现的客户业务来说，是一种惊人的统计数字，而这与大多数放贷人通常看到的情况不同。
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 a lot of fraud attempts in that system. 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.
嗯，我的意思是，我们所做的绝大部分业务已经超过了90%的新客户，这是整个新兴生态系统以及我们的一个特定银行合作伙伴的情况。当然，在这个系统中有很多欺诈企图。因此，我认为从传统零售放贷转向在线放贷的挑战之一就在于，您将面临巨大的欺诈风险。
And we've seen many different kinds of fraud risk. There's of course sort of first party fraud where someone pretends to be, you know, or a third party fraud where someone pretends to be someone else. This first party where someone just has no intention of repainting. There's sort of internet specific behaviors. 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. But online it turns out you can open five tabs and be on five different lenders websites around the same time. And you could get loans with all of them before any of them find out from each other or from the credit bureaus that anything's happened.
我们看到了许多不同类型的欺诈风险。其中，当然有第一方欺诈，即有人假装自己是某人，也有第三方欺诈，即有人假装是别人。第一方欺诈是指有人根本没有归还意图。还有一些特定于互联网的行为。最常见的之一是叫做贷款叠加，在模拟世界中并不太合理，因为你需要去很多不同的银行并尝试获取贷款。但是在线上，你可以同时打开五个标签，并进入五家不同的借贷网站。并且在它们相互之间或通过信用局之前获得所有贷款。
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. 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 pass judgment on the probability of certain risks.
所以你真的必须防范所有这些不同种类的风险。这就是我们所建立的系统的一些特殊之处，我们已经建立了专门针对每种风险的机器学习模型。我们将它们构建到流程的正确节点中，在那里，我们真正拥有的是一种决策树，也就是在正确的时刻，我们要求这些模型对某些风险发生的概率进行判断。
And depending on that risk level, we can then take different actions, to mitigate that risk. If we successfully 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. 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 broad to really minimal rates. 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 we think a really powerful statistic.
根据风险水平的不同，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。如果我们没有成功，那么问题又出现了，是否值得再采取行动。每一步，我们都在问一系列数学问题。如果答案是风险足够高，那么你需要采取一系列行动中的下一步，逐步构建这个系统，多年来，我们真正发展了专业知识，将欺诈率保持在非常低的基础点水平。我们说的是平台上欺诈率只有十个基础点左右，这在几乎完全是第一次未知在线、全自动贷款的情况下，我们认为是一个非常强有力的统计数字。
Interesting. How do you think about what happened during, I mean, the last 12 months have been a 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 one of the results you've seen are there, anything you've learned that you think would be interesting to listeners about what you've seen in your experience over the last 12, 13 months through this kind of very strange environment.
很有趣。你觉得过去12个月发生的事情怎么样？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，这是一种前所未有的时期。你怎么看你的模型能否在这种前所未有的经济体验和对日常生活的干扰中表现出色？你看到了哪些结果？在过去的12、13个月中，通过这种非常奇怪的环境，你所学到的东西有没有什么有趣的地方可以与听众分享？
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, times are bad, no one wants to be lending, and sort of when times are good, everyone's just sort of worried about when times are bad.
嗯，非常奇怪的环境。当然，我们总是谈论经济周期，当时机好的时候，每个人似乎都在借款，时机不好的时候，没有人想借款，当时机好的时候，每个人都担心当时机不好的时候。
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.
我们一直认为的一件事，就是对经济周期的痴迷以及对经济低谷的关注，其实是一种贷款方式不够强健、缺乏预测性所导致的症状。
It means that lenders aren't really able to predict very well the sort of individual consumers level of risk. What they're really just doing is saying, well, overall times are good now, and we assume right now we're gonna get a 5% default rate, but when times are bad, that five is gonna change to 10, and it's sort of just a kind of 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.
这意味着贷方不能太好地预测个人消费者的风险水平。他们真正做的只是说，总体来说现在时机很好，我们假设现在会有5％的违约率，但当时机不好时，这个五会变成十，这种方法有点像把油漆往墙上扔，而当季节变化时，一切从好变坏，我们一直认为，个人借款人的差异远远超过宏观经济周期之间的差异，当你实际查看数据时，很清楚这就是情况，即使在最艰难的经济环境下，低风险组别的绝大多数人最终仍能偿还他们的贷款。
The most interesting question is actually, which individuals are actually in those low risk buckets and not sort of which period are you in. 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.
最有趣的问题实际上是哪些人处于低风险区间，而不是你处于哪个时期。现在回到去年，当然，那是一个非常有趣的时期，不仅是经济周期的转折点，而且也非常不寻常。实质上我们看到的是，虽然消费者的行为发生了很多改变，但总体来说，我们实际上看到了整个生态系统的贷款绩效，包括我们所有的银行合作伙伴和资本市场合作伙伴，他们预期赚取的回报，在过去的一年中几乎全部能够持续实现或超越，尽管受到了疫情的影响。
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 macroeconomic 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.
现在，说到这一点，我想很多这方面的问题确实与这里发生的一些独特情况有关，这也是我们真正建立在我们模型中的另一个因素，即能够迅速响应宏观经济变化。我的意思是，当2020年第二季度失业率开始上升时，能够非常快速、准确地做出反应非常重要，你希望能够知道哪些行业、职业等存在额外的失业风险需要进行建模，以及如何相对于传统规范进行建模。
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-on-low math, and then just sort of have to like take a blunt cut to your credit box, and that sort of really just means that you are either gonna still take on too much risk or you're gonna essentially stop running your business during this challenging time.
你想让你的模型处理这个问题，因为另外一种选择就是让一群人坐在桌子旁边进行一些低效的计算，然后只能钝化你的信用记录，这实际上意味着你要么承担过多的风险，要么在这个具有挑战性的时期停止经营你的业务。
Well, we found as 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 sort of a system that we built and it sort of worked incredibly well over the last year.
嗯，我们发现，我们能够继续批准大量人的贷款，并因为他们是合适的借款人而得到偿还。在状况开始正常化的反面，您希望系统能够非常动态地响应更新的宏观经济数据，这样的一个系统我们建立了，并且在过去的一年中表现出色。
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's greater, but you're still talking about ingesting some sort of macroeconomic status into your decisions. How does that work?
那个系统是在预测宏观经济状况吗？你是如何考虑将宏观经济纳入风险配置的？我知道个人因素更重要，但你还是要考虑吸收某种宏观经济状况，并将其纳入决策中。这是怎么实现的呢？
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 with you. All the different industry, yeah. That's the whole difference. We're super happy to be market neutral with respect to macroeconomics, so what we want to do is 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，我们不是在预测宏观经济方面的业务。这本身就是一个完整的行业。我们对你们很满意。各种不同的行业，是的。这就是全部的区别。我们非常高兴能够市场中性地面对宏观经济，因此我们想要做的就是采取市场共识，有很多市场共识统计数据的来源，包括已经发生和预计在不久的将来会发生的事情，我们把它们简单地插入到已经适应历史指标的系统中，这意味着我们已经采取了不同资产类别中消费者信贷历史拖欠率、不同行业和职业的历史失业率等历史数据。
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.
我们已经建立模型，将这些事物与它们对我们特定贷款类别的影响联系起来。我们把这个模型实际上建立起来了，以便在每周公布最新的失业率数值、每月公布的最新预测投射数值（从各种共识经济学家组成的团体中得出）时，我们就能立即将这些数据纳入我们的模型中。
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. That's a really interesting approach.
这些数字基本上只是输入到模型中，然后更新宏观经济假设，这样你就能够适当地校准市场对宏观经济数字的共识。这是一个非常有趣的方法。
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.
我喜欢这个想法。所以我想问一下，你几次使用了短语“银行合作伙伴”，我想深入了解你是如何思考与银行合作的方式与许多现有的金融科技公司不同。我知道你们正在追求充电器、购买银行和成为银行。
What is the business model for upstart in terms of working with banks or the bank partners that you talked about? Yeah, what were 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.
谈论与银行或银行伙伴合作的商业模式，upstart是怎样的呢？是的，我们是一家纯粹的科技公司。我们做这个已经有很长时间了，并且在每个机会去思考我们想要走哪条路时，很明显的是我们想要成为越来越多的一家科技公司，因为这是我们擅长的。
We're not good at being a bank. We're not good at any of the things that a bank is good at, 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 to scale.
我们不擅长做银行的事情。我们不擅长银行擅长的任何事情，但我们非常擅长建造技术，而且非常坚信技术的发展，尤其是在人工智能领域，将成为需要聚合和规模回报的技术领域。
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.
这在数据规模上是确实的。在研发投资规模上也是确实的。对于每一家银行或某个行业中每一家企业真的去开发自己的AI引擎、工具和数据集来说，这并没有太多意义。
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.
“仅仅把规模扩大并不足够好，像在这个行业中共同分享的那个就更优秀。因此，这就是我们的目标。我们的目标是成为共同的技术提供商和构建者。”
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 customer trust and loyalty and brand understanding that comes with the retail presence comes again with having those different products has that sort of relationship with checking accounts, banking accounts, all of those things are things that we don't do, we don't want to get into doing.
我们认为银行在很多方面是做得很好的。有时候他们的强项不在于建设技术，但他们在拥有跨足多种产品的出色客户关系、获得客户的信任和忠诚度以及品牌理解等零售业的存在所带来的好处。他们与支票账户、银行账户等不同产品建立了那种关系，这是我们不会去做、也不想去做的。
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.
我们希望支持和帮助我们的银行合作伙伴在贷款方面变得非常非常好。我们认为这方面需要强大的人工智能组件，这就是我们为此带来的。
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. And 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.
明白了。感谢你的想法。这次交谈很有趣，Paul。我认为对于听众来说，讨论这个领域的独特益处是非常有趣的。在播客结束时，我通常会问嘉宾三个问题，但对于你，我会增加第四个问题，这你不知道。
So here you go. So you came out of college, literally out of college because you hadn't graduated when you started at 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 when you kind of came in from an outsider with, I'm going to guess pretty limited preconceived notions about the industry was.
所以你毕业了，真正毕业了，因为你在Upstart开始时还没有毕业。你已经在消费贷款领域工作了大约十年了。自从你从一个门外汉进入这个行业以来，最让你惊讶的一件事是什么？我猜你对这个行业的先入为主的观念相当有限。
What's been the biggest surprise? Yeah, for sure, I think the biggest surprise is going, 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 to create value for consumers.
最大的惊喜是什么？是的，肯定的，我认为最大的惊喜是，我本来预计有很多人在努力建立我们正在建立的东西。我觉得这是一个难得的机会，可以为贷方和消费者创造价值。
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.
这句话差不多就像你把脑袋撞在桌子上一样。很明显，这可能是你能拥有的AI的最佳应用。我的意思是，最好的在于，它结合了所有的要素，例如高量的数据、高经济机会以及实时决策制定等等。
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.
同时，这就像是一个巨大的市场，是最古老、最赚钱的行业之一，几乎是消费金融系统所有利润的来源。然而，我们现在已经过去了十年，离我退学也快了。
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 a surprise 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.
我猜实际上已经超过十年了。我们几乎没有看到任何类似的事情在我们在Upstart建造的之外发生，我很惊讶于这个方向的惯性水平，也许还有一些机构的创新障碍。
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.
但我们希望现在开始有所改变，因为我们已经取得了一些成功。我肯定看到了更多银行方面对你的兴趣。所以，我希望你会看到更多的贷款人朝着这个方向走来，而技术提供商也试图用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 on one company. But what's the best piece of career advice you've ever gotten? The best piece of career advice. Can't be from me. Probably, I think I'll go with when in doubt, air on the side of being more technical.
现在我的标准三个问题。你知道的，这对你来说是第一个入门问题，因为你真的大部分职业生涯都在一家公司。但最好的职业建议是什么？最好的职业建议。不能是我给的。可能，我认为当有疑问时，要在更技术的一面上倾向。
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, 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 that you're doing.
我觉得那是高中时有人跟我说的。其实，我觉得他们的想法是不管你做什么，无论工作的性质是不是通常以技术方式完成的，由于科技的力量能提高效率、利用事物，而且真正训练你的脑子思考严谨的方式，所以在自己所做的领域里，瞄准偏技术的人通常都是有帮助的。
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. And just say, 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.
我认为，至少它迫使你像数学家或计算机科学家那样思考，以一种高度逻辑、严谨的方式。如果你用更严谨的方式来解决通常不会被严谨看待的问题，你会发现往往能找到被忽略的见解和优化方法，这些方法在一般情况下或没有那些领域共有的精确性下思考时都是被错过的。可以肯定的是，在认识你10年后，我的严谨思维水平和分析能力都大大提高了。没有这样的思考，与你谈论问题会比较困难。
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. 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.
我的第二个问题是，你对消费信贷领域有关的最佳建议是什么？考虑到你的历史，我认为这个问题可能非常有趣。是的，我们得到的有关消费贷款行业的最佳建议。你是否忽略了所有收到的建议，而是自己开创了一条路？不，当然没有。在我们的经历中，我们花费了很多时间在许多事情上重新发明轮子。
I mean, for example, we didn't know about direct mail when we started and for many years, we just thought, 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 in consumer credit is done. I think 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.
我的意思是，例如当我们开始的时候我们并不知道直邮，而且很多年我们只是认为直邮不可能是21世纪进行成功广告的一种方式，但很明显它就是消费信用广告的一种成功方式。我认为最好的建议是，不要在不必要的事情上聪明和创新。
When we started, we really tried to sort of make everything about what we did different. 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 rethink. And most of those were like minor, minor optimizations. 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.
当我们开始时，我们真正尝试让我们所做的一切有所不同。这意味着贷款的不同术语、我们所构建的方式、服务费和每一个小部分，我们都试着重新思考了一下。其中大部分只是细微的优化。当然，我们所做的核心事情是一个非常大的优化。但是由于我们所做的这些小不同，我们发现自己不断纠缠于整个生态系统的一些规范中。
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, 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 a lot less now.
你知道，当你考虑评级机构和监管机构以及它们所用的各种不同的事物时，你会想要将你的创新孤立到你想让人们关注的事物上，而不是花费所有时间陷入那些不真正推动发展但会给人们带来许多混乱和头痛创新领域。我认为我们早期更多地做了这些，而现在做得少了很多。
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 in the product organization in the early days. 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?
听起来你在谈论努力和准确度之间的高效曲线，以及收集数据。也许在产品组织的早期阶段，你并没有完全正确地掌握这一点。最后一个问题始终是，你对未来有什么大胆预测？可以是借贷、金融科技、生活一般，你的大胆预测是什么？
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. 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.
你看起来像一个敢作敢为的人。给我一个好的大胆预测，让我能回来跟你确认。一个大胆的预测。 我通常看到你缺乏大胆的保罗或预测，说实话。如果不想给我赔率也没关系，但我会接受。没有赔率的预测不算太有意义。
That goes to that rigorous thinking point you made earlier. 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. No answer, no bold predictions. All right, we need a mathematical model to make our predictions and we don't have one on the fly.
那就回到你之前所提到的严谨思考的问题。我没想到这个问题会让你难住。我没有一个想承诺的好答案。没有答案，没有大胆的预测。好的，我们需要一个数学模型来做出预测，但我们没有临时的。
I'll take it.
我会买下它。
Well, Paul, I appreciate your joining us. I think it's a very interesting discussion on the application of AI across the lending lifecycle. The 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. I appreciate your make of the time.
嗯，保罗，我很感激你加入我们。我认为这是关于人工智能在贷款生命周期中应用的非常有趣的讨论。我们在谈论核保方面的概念，特别是像你所说的关于宏观经济方面的，因为在经济压力期间，个体风险差异要比个人之间的差异大得多。这是一个非常有趣的观点，因此感谢你的加入。我很感激你抽出时间参加我们的讨论。
Okay, 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's AI lending platform uses sophisticated machine learning models to more accurately identify risk and approve more applicants than traditional credit models with fraud rates near zero. Upstart's all digital experience reduces manual processing for banks and offers a simple and convenient experience for consumers.
Upstart与银行和信用社合作，帮助他们增加消费者贷款组合，并提供现代的全数字化借贷体验。随着普通消费者的数码化技能越来越强，他们的银行也必须跟上。Upstart的AI借贷平台使用先进的机器学习模型，比传统的信用模型更准确地识别风险，并批准更多的申请人，欺诈率接近于零。Upstart的全数字体验减少了银行的手动处理，并为消费者提供简单方便的体验。
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 into-in solution that can help you find more credit where the borrowers within your risk profile with all digital underwriting, onboarding, loan closing and servicing. It's all possible with upstart in your quarter. Learn more about finding new borrowers enhancing your credit decision process and growing your business by visiting upstart.com slash four-banks. That's upstart.com slash four-banks.
无论您是想要增长和改进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提供一种综合解决方案，可以通过全数字化的承保，入职，贷款结案和服务来帮助您在风险范围内寻找更多有信贷价值的借款人。只要有Upstart在您的支援，这一切都是可能的。通过访问upstart.com/four-banks了解更多有关寻找新的借款人，改善您的信贷决策过程以及发展业务的信息。那是upstart.com/four-banks。
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你一直在倾听来自Upstart的贷款领袖。要确保您不错过任何一集，请在您最喜爱的播客播放器上订阅贷款领袖。使用Apple Podcasts，通过轻触您认为该节目应得的星星数量来快速评分。
Thanks for listening. Until next time.
谢谢你的聆听，下次再见。