Can AI uplift entrepreneurs that traditional banks reject - Mercedes Bidart

人工智能能否扶持被传统银行拒绝的企业家——梅赛德斯·比达尔特

You're listening to TED Talks Daily where we bring you new ideas to spark your curiosity every day. I'm your host, Elise Hue. We hear it all the time: success isn't a one-size-fits-all type of thing. And yet for small business owners who may find success outside traditional metrics, securing things like a bank loan can be tough. In this talk, impact entrepreneur Mercedes Bidart shows how AI tools in Latin America are helping to change this by making the currency of trust in business visible and verifiable, opening doors to fairer finance.

欢迎收听TED Talks Daily，我们每天为您带来激发好奇的新思想。我是主持人Elise Hue。我们常听到：成功并非千篇一律。然而，对于那些在传统指标之外也能取得成功的小企业主而言，获得银行贷款等支持可能十分困难。在本演讲中，影响力企业家梅赛德斯·比达尔特展示了拉丁美洲的人工智能工具如何通过使商业中的信任货*变得可见、可验证，来帮助改变这一现状，从而打开更公平金融的大门。

I grew up in a family of small business owners in Argentina. My parents ran a quilt and carpet shop, so I witnessed firsthand how difficult it is to grow a business. Trust and support from their community were key in keeping the business alive.

我在阿根廷一个小企业主家庭长大。我的父母经营一家被褥地毯店，因此我亲眼目睹了企业成长的艰辛。来自社区的信任和支持是维持企业生存的关键。

But I decided not to continue with my family business. Instead, I studied political science. I was obsessed with how technology could support the growth of businesses like my parents'. And that curiosity led me to MIT, where in 2019 my master's thesis in AI and economic development got awarded funding to become a real-world pilot.

但我决定不接手家族生意，转而攻读政治学。我痴迷于技术如何能支持像我父母这样的企业发展。这份好奇心将我带到了麻省理工学院，2019年，我关于人工智能与经济发展的硕士论文获得了资助，得以成为一个现实世界的试点项目。

And that's how I ended up working in informal settlements in Colombia, in these neighborhoods where I did my research. You didn't need a credit card to buy lunch. It was enough for the shopkeeper to know who you were. If your mother had a good record with loans, if you said hello in the mornings, if you had a shop that was known by the neighbors, they would front you the rice, the sugarcane, the bread. The economy didn't run solely on cash. It ran on trust, that invisible currency that is built over time.

就这样，我最终在哥伦比亚的非正规聚居区工作，在这些社区里进行研究。在这里买午餐不需要信用卡。店主只要认识你就够了。如果你的母亲有良好的借款记录、如果你早上会打招呼、如果你有一家邻里皆知的店铺，他们就会赊给你大米、甘蔗、面包。经济的运行不仅仅依靠现金，更依靠信任——那种随时间建立起来的无形货*。

And I noticed something. Those same principles I saw growing up in Argentina were alive in Colombian businesses, too. In many Latin American neighborhoods, trust has always been the strongest currency, a good name. But here comes the contradiction. When this same person goes to a bank and asks for a loan to grow this business, they will be rejected. They will tell them: you don't have collateral, you don't have a financial history. There's no way we can prove who you are.

我注意到一个现象：我在阿根廷成长过程中看到的那些原则，在哥伦比亚的企业中也同样存在。在许多拉丁美洲社区，信任一直是最强大的货*，即“好名声”。但矛盾出现了：当这同一个人去银行申请贷款以发展业务时，他们会被拒绝。银行会说：你没有抵押品，没有信用记录，我们无法证明你是谁。

In many Latin American neighborhoods, this is the case, and in Latin America, half of our population is excluded from formal credit. After a decade working in the intersection of financial inclusion and urban development, I dedicated my life to answer one question: What if what makes you great, worthy in your neighborhood trust could also make you great worthy in the eyes of a bank? What if your word could be part of the risk assessment? What if we can scale the access to capital by making your potential measurable? What if trust could be measured with AI?

这在许多拉丁美洲社区是普遍现实。在拉丁美洲，一半的人口被排除在正规信贷体系之外。在普惠金融与城市发展的交叉领域工作十年后，我毕生致力于回答一个问题：如果令你在社区中获得信任和尊重的特质，也能让你在银行眼中变得可靠，会怎样？如果你的承诺能成为风险评估的一部分呢？如果我们能通过衡量你的潜力来扩大获得资本的渠道呢？如果信任能用人工智能来测量呢？

So before I tell you more, I want to share a little bit of how all this started. Since I was a child, I dreamed of changing the world, and that's why I studied political science. I thought I was going to do it through policy, but then I realized policy was not moving at the speed people needed to. So I turned to technology. Technology doesn't recognize any geographic boundary.

在深入之前，我想分享一下这一切是如何开始的。从小我就梦想改变世界，因此我学习政治学。原以为能通过政策实现，但后来发现政策变革的速度跟不上人们的需求。于是我转向了技术。技术不承认任何地理边界。

So at MIT, my classmates and I started working on a local project to define local marketplaces for communities, platforms where they can upload what they're selling and become visible in their community. We started visiting these businesses to help them upload more pictures of their products into the marketplace and become known and start selling more. And we noticed that they weren't growing their sales.

在麻省理工学院，我和同学们开始了一个本地项目，为社区构建本地市场平台，让他们可以上传销售的商品并在社区内获得可见度。我们开始走访这些企业，帮助他们上传更多产品图片到平台，以提高知名度、增加销量。但我们发现他们的销售额并未增长。

So when we asked them why, their answer was very simple: They didn't have enough money to buy more supplies. Even though they were running these businesses for years, they couldn't get more inventory. They couldn't get any access to working capital to buy more inventory. So we noticed something: We were not facing a visibility problem. We were facing a financial exclusion problem.

当我们询问原因时，他们的回答很简单：他们没有足够的钱购买更多原材料。尽管经营多年，他们却无法增加库存，无法获得任何营运资金来采购更多货物。于是我们意识到：我们面对的不是可见度问题，而是金融排斥问题。

And the deeper I went, the more I learned something that we usually don't say enough: Being poor is very expensive. Products cost more when you can just afford them in small quantities. If you can't buy a whole bottle of shampoo, you end up buying sachets. You can't buy groceries for the whole week, you end up buying by the day and you always end up paying more.

随着深入探究，我越来越明白一个我们通常强调不够的道理：贫穷的成本非常高昂。当你只能小量购买时，产品单价反而更高。如果你买不起整瓶洗发水，最终只能购买小袋装；无法购买一周的食品，只能按日购买，结果总是支付更多。

And when it comes to credit in the financial sector, the cost is even higher. When you don't have a credit history or bank account, your only option is to access the predatory lenders, the loan sharks, and they come at a brutal cost. They don't ask you for paperwork, but they could charge you 20% interest rate per week, even per day. And they are violent and abusive.

在金融领域的信贷方面，成本甚至更高。当你没有信用记录或银行账户时，唯一的选择就是求助于掠夺性放贷者、高利贷者，代价极其残酷。他们不要求文件材料，但可能收取每周甚至每天20%的利息，而且手段暴力和虐待。

So I will tell you the story of Maria. She's a Venezuelan migrant living in a low-income neighborhood in Colombia. She makes these beautiful handcrafted bags and gets custom orders from her clients. So before she sells and gets paid, she needs to make the order. So she needs to buy the materials to make that order happen.

让我讲讲玛丽亚的故事。她是一名居住在哥伦比亚低收入社区的委内瑞拉移民。她制作精美的手工包，并接收客户的定制订单。因此，在销售和收款之前，她需要完成订单，这就必须购买材料。

As Maria is a migrant, she doesn't have a bank account. She doesn't have any credit history. So her only option to buy those materials is to ask money from these predatory lenders that are really, really dangerous. Unfortunately, Maria in Latin America is not the exception. She's actually the rule. She's the rule in Latin America. She's one of millions of micro businesses like hers that are everywhere, from the corner shop to the restaurant to the beauty salon.

由于玛丽亚是移民，她没有银行账户，也没有任何信用记录。因此，她购买材料的唯一选择就是向那些非常危险的掠夺性放贷者借钱。不幸的是，在拉丁美洲，玛丽亚并非例外，而是普遍现象。她是拉丁美洲的常态。像她这样的微型企业数以百万计，遍布各个角落，从街角小店到餐厅再到美容院。

Actually, almost every business in Latin America is a micro business. 99% of our businesses are micro and they contribute one third of our GDP. But still they cannot even access one dollar from a bank. Why? Because they don't have the paperwork the financial system was built to require.

事实上，拉丁美洲几乎每家企业都是微型企业。我们99%的企业是微型企业，它们贡献了GDP的三分之一。但它们仍然无法从银行获得哪怕一美元贷款。为什么？因为它们没有金融体系要求提供的文件材料。

So Maria might not have a credit history. She might not have a bank account. But she has a phone, and there's where we saw the opportunity not to change who they are, but to change how they are seen. So when we started, there was no data about this economy and this segment of the population. We wanted to help. And you know, that's one of the main problems with AI: models can only predict what they have already seen.

玛丽亚可能没有信用记录，也可能没有银行账户。但她有一部手机，正是在这里我们看到了机遇：不是改变他们是谁，而是改变他们被看待的方式。我们起步时，关于这部分经济和人群没有任何数据。我们想提供帮助。而你知道，这是人工智能的主要问题之一：模型只能预测它们已经见过的情况。

So we understood that if we wanted to start helping this population, we needed to build a dataset ourselves. As this population we're talking about are informal entrepreneurs, there's no record, there's no data. So you become invisible to the system. In traditional banking, the way they give out a loan is usually that the risk officer goes to the house of the person, checks the business with their own eyes, talks with the neighbors, sees effectively that business exists and makes the decision based on their experience. That usually comes with bias. It's subjective and it's really slow.

因此我们明白，若要帮助这群人，我们必须自己构建数据集。由于我们谈论的是非正规企业家，没有记录，没有数据，所以他们对系统而言是隐形的。在传统银行业，发放贷款的方式通常是风险专员上门拜访，亲眼查看业务，与邻居交谈，确认业务真实存在，然后凭经验做出决定。这通常带有偏见，主观且非常缓慢。

So at that point when we started to build a dataset, we were actually building the local marketplaces where people were uploading the products of what they were selling. And we noticed that the images themselves were full of economic signals: we could see if there were customers in the background, if the product was handmade, if there was potential for that product or service to be sold in that neighborhood. So the data was there, but just not in the format that the banks were trained to read.

因此，当我们开始构建数据集时，我们实际上是在搭建本地市场平台，让人们上传他们销售的产品。我们注意到，图像本身充满了经济信号：我们可以看到背景中是否有顾客、产品是否手工制作、该产品或服务在社区是否有销售潜力。数据就在那里，只是不是银行习惯阅读的格式。

So when we started building the dataset, we started small, super small. We started giving out 10-dollar loans, just enough for the entrepreneurs to refill their inventory and enough for us to start growing the dataset. And we were very intentional about whom we were giving the loans to. Half of the people we were serving were women. Because if we want AI to be fair, then it needs to learn from everyone.

所以，我们从小处着手，从非常小的规模开始构建数据集。我们开始发放10美元贷款，刚好够企业家补充库存，也足够我们开始扩展数据集。我们在选择贷款对象时非常审慎，所服务的人群中一半是女性。因为如果我们希望人工智能公平，它就需要向所有人学习。

So people like Maria the artisan might not have a credit history, but she has a phone that is full of clues about her daily economy. She has a Facebook page where she uploads the products she's selling. She has text orders that she's receiving. She has had this phone for years. She has videos of the products on her phone. So we built a suite of AI-powered models that take this invisible data into a financial identity.

因此，像手工艺人玛丽亚这样的人可能没有信用记录，但她的手机里充满了关于她日常经济的线索。她有Facebook页面来上传销售的产品，她接收文字订单，这部手机已使用多年，手机里存有产品视频。于是我们构建了一套人工智能模型，将这些无形数据转化为金融身份。

This is all the data we are processing, but I will concentrate on three specific scores that are proprietary and that have been developed by us. One of the main scores we have is looking at text messages, short code text messages where we are getting bill payments, order confirmations, mobile recharges – any transactions that have been done in digital wallets or bank accounts. And by using an LLM model and machine learning, we can detect patterns of income, spending, and disposable available balance per month. It's a kind of open banking, but instead of using a bank account, we are using telecom data.

这是我们正在处理的所有数据，但我将重点介绍我们自主开发的三个专有评分。我们的一个主要评分是分析短信，特别是短代码短信，从中获取账单支付、订单确认、手机充值等任何通过数字钱包或银行账户完成的交易记录。通过使用大型语言模型和机器学习，我们可以检测月收入、支出和可用余额的模式。这类似于开放银行，但我们使用的是电信数据而非银行账户。

Another score we have developed is using videos. We replace that visit that usually the risk officer does to the houses of people. That is usually very expensive and takes a lot of time. We replace it by users sending a one-minute video of their business where they explain what they're doing. And using computer vision, we can get their stock, their inventory, their tone of voice, what they're saying about their business, their localization, the type of business and all the potential that it has. We are detecting their willingness to pay.

我们开发的另一个评分利用视频。我们取代了风险专员通常的上门拜访——那种方式成本高且耗时。我们让用户发送一分钟的商业视频来解释他们在做什么。利用计算机视觉，我们可以获取他们的库存、语调、关于业务的描述、地理位置、业务类型及其所有潜力。我们正在检测他们的支付意愿。

And lastly we developed one that connects to their social media. Right now most businesses, even if they are informal, are present online. They have a Facebook page or they have an Instagram. So when they apply for the loan they sign in to their social media and we can get their videos, their pictures. So we use again computer vision, the same we did for the other type of videos. But also we get the likes, the comments, the engagement. They have their profile bio and we detected that a business that has a really strong social presence and online presence has more probability to pay back.

最后，我们开发了一个连接到他们社交媒体的评分。如今大多数企业，即使是非正规的，也都有在线存在。他们有Facebook页面或Instagram账号。因此，当他们申请贷款时，可以登录社交媒体，我们就能获取他们的视频和图片。我们再次使用计算机视觉技术，与处理其他视频的方法相同。此外，我们还获取点赞、评论和互动数据。他们有个人简介，我们发现拥有强大社交媒体和在线影响力的企业还款概率更高。

So all these data flows into our models, and we detect patterns and signals that can tell us: Can this person be trusted with a loan if they never had one before? And after three years, we can go beyond just saying yes or no – in fact we do it in just seconds – we can also say how much they can repay, when and under what conditions? This is allowing us to simulate the interest rate, the number of installments; we can also detect for seasonal impact. So this is allowing us to offer credit that is actually supporting people's everyday needs and that is tailor-made for them. It's not just one financial product that we are trying to sell to everyone; it's actually understanding what do you need for your business.

所有这些数据流入我们的模型，我们检测能告诉我们答案的模式和信号：如果一个人从未贷过款，他们能否被信任？三年后，我们不仅能回答是或否——事实上我们只需几秒——还能说出他们能偿还多少、何时以及在什么条件下偿还。这使我们能够模拟利率、分期付款次数，还能检测季节性影响。因此，我们能够提供真正支持人们日常需求、为他们量身定制的信贷。这不仅仅是我们试图向所有人推销的一种金融产品，而是真正理解你的业务需要什么。

So we have validated this approach. After all these three years, we demonstrated that we can use this type of data to understand the informal sector. Our business and our models have reached an accuracy level of over 83%, which is at market standards. We have served more than twenty-six thousand entrepreneurs. Our models have been trained with more than one hundred and fifty thousand data samples of informal entrepreneurs with millions of data points.

我们已经验*了这种方法。经过这三年，我们证明了可以利用这类数据来理解非正规部门。我们的业务和模型已达到超过83%的准确率，符合市场标准。我们已经服务了超过2.6万名企业家。我们的模型已用超过15万个非正规企业家数据样本、数百万个数据点进行了训练。

But this is not just supporting the entrepreneur and their family. This is changing the financial system. What usually took years to be built – or maybe we don't have it at all – a credit history now can take just months. We're building a live financial monitor of the financial well-being that can be updated daily, so you don't need to wait years to be eligible for a loan. And this is allowing the informal sector to access loans from the formal banking system for the first time.

但这不仅仅是支持企业家及其家庭。这正在改变金融体系。通常需要多年（或根本不存在）才能建立的信用记录，现在可能只需几个月。我们正在构建一个每日更新的实时财务健康监测器，因此你无需等待多年才能获得贷款资格。这使得非正规部门首次能够从正规银行系统获得贷款。

Artificial intelligence is not magic. It's a tool, one that can help us process millions of data points no human risk officer could ever read, watch or analyze at scale. AI, of course, is improving efficiency, but if we design it with intention, it becomes more than efficiency. It becomes fair, and it allows us to see value where others were seeing risk. It's allowing us to see gold where others saw stones.

人工智能并非魔法。它是一种工具，能帮助我们处理数百万个数据点，这是任何人类风险专员都无法大规模阅读、观看或分析的。当然，人工智能正在提高效率，但如果我们有意识地设计它，它就不仅仅是效率。它变得公平，让我们在他人看到风险的地方看到价值，在他人看到石头的地方看到黄金。

And it's allowing us to offer services at scale while at the same time honoring the local knowledge, culture and context. It's allowing us the hyper-personalization of financial services. And to say yes to someone like Maria. To say yes to someone like my mom all those years ago when she started the business. And to millions of women entrepreneurs that we are pushing this economy forward – to say yes, not because of a bank statement, but because of millions of quiet signals that tells us that she shows up, she delivers, and she can be trusted.

它使我们能够大规模提供服务，同时尊重当地知识、文化和背景。它使我们能够实现金融服务的高度个性化。并对像玛丽亚这样的人说“是”，对像多年前我母亲创业时那样的人说“是”，对数以百万计推动经济发展的女性企业家说“是”——不是因为银行对账单，而是因为数百万个安静的信号告诉我们：她出现了，她兑现承诺，她值得信任。

That was Mercedes Bidart, speaking at TED AI in Vienna, Austria, in 2025.

以上是梅赛德斯·比达尔特在2025年奥地利维也纳TED AI大会上的演讲。