AI financial advice is surprisingly good — especially if you ask the right questions

People are increasingly turning to artificial intelligence for financial advice, but will following it improve their financial standing?越来越多的人开始向人工智能寻求财务建议，但遵循这些建议是否真的能改善他们的财务状况呢？

“Half of Americans say they are using AI to get financial advice, but we know very little about what kind of advice they’re getting and whether they’re acting on it,” said Taha Choukhmane, an assistant professor of finance at the MIT Sloan School of Management and co-author of a new paper that measures and analyzes the quality of financial advice given by large language models.“半数美国人表示他们正在使用人工智能获取财务建议，但我们对他们获得的是什么样的建议以及他们是否采纳这些建议知之甚少，”麻省理工学院斯隆管理学院金融学助理教授、一篇衡量和分析大型语言模型所提供财务建议质量的新论文的合著者塔哈·舒赫曼说道。

Research by Choukhmane and co-authors showed that following AI recommendations can result in sizable saving buffers for virtually all individuals above age 30.舒赫曼及其合著者的研究表明，遵循人工智能的建议可以为几乎所有30岁以上的人带来可观的储蓄缓冲。

AI consistently advised people to save during their working years, draw down savings in retirement, invest heavily in diversified stock funds, and reduce stock exposure after age 45. However, AI chatbots were less successful in adjusting to shocks like unemployment, and they allowed portfolios to drift rather than actively rebalancing them.人工智能一致建议人们在工作期间储蓄，退休时提取储蓄，大量投资于多元化的股票基金，并在45岁后减少股票敞口。然而，AI聊天机器人在适应失业等冲击方面表现不佳，它们允许投资组合漂移，而不是主动重新平衡。

The quality of financial advice given by LLMs improved when the researchers introduced more structured prompts, but the AI still often generated too little active portfolio rebalancing. 当研究人员引入更结构化的提示时，大型语言模型给出的财务建议质量有所提高，但AI仍然经常生成过少的主动投资组合再平衡。

How the study was conducted研究是如何进行的

The researchers built a model reflecting how people’s incomes, jobs, investments, and taxes typically evolve over their lives, which gave them a benchmark for what “good” financial decisions look like. 研究人员构建了一个模型，反映人们的收入、工作、投资和税收在人生中通常如何演变，这为他们提供了一个衡量“好”财务决策的基准。

Then they asked a sample of 1,000 adults to write their own prompts seeking spending and investing advice from GPT-5.2, GPT-5.6, or Gemini 3 Flash. 然后，他们请1000名成年人样本编写自己的提示，向GPT-5.2、GPT-5.6或Gemini 3 Flash寻求支出和投资建议。

Next, they simulated what would happen if people from 22 to 89 years of age followed that advice over time, repeatedly asking AI these same types of questions and following its advice on spending, saving, and investing. 接下来，他们模拟了如果22岁到89岁的人长期遵循这些建议会发生什么，反复向AI提出相同类型的问题，并遵循其关于支出、储蓄和投资的建议。

Finally, they repeated the exercise using well-written academic prompts that included full financial information and clear assumptions. These more-detailed prompts included information on the individual’s age, job status, income, and savings balances, along with assumptions about the economic environment. 最后，他们使用精心编写的学术提示重复了该实验，这些提示包含完整的财务信息和清晰的假设。这些更详细的提示包括个人的年龄、工作状况、收入和储蓄余额，以及对经济环境的假设。

The authors compared the simulated advice (what would happen if regular people followed the AI recommendations from the prompts they gave) to what people were already doing financially without the help of AI. They also compared the simulated advice to the academic prompt. 作者将模拟建议（即普通人如果遵循他们提供的提示中的AI推荐会发生什么）与人们在没有AI帮助的情况下已经在进行的财务活动进行了比较。他们还将模拟建议与学术提示进行了比较。

The results showed that LLMs can offer an affordable, widely accessible source of financial guidance that can help users overcome the significant costs, biases, and conflicts of interest associated with traditional human financial advisors.结果表明，大型语言模型可以提供一种负担得起且广泛可及的财务指导来源，帮助用户克服与传统人类财务顾问相关的巨大成本、偏见和利益冲突。

Breaking down the findings研究结果详解

Overall, the researchers found that the financial advice given by LLMs over time is good but gets better when the questions are asked in an academic fashion, and that the models have strengths and weaknesses. 总体而言，研究人员发现，大型语言模型长期给出的财务建议是好的，但当问题以学术方式提出时，建议会变得更好，而且这些模型有优点也有缺点。

1. AI encourages smart financial behavior.1. 人工智能鼓励明智的财务行为。

LLM advice was better than the scholars expected, regardless of whether the prompts were written by regular users or by academics. It steered people toward higher savings, increased participation in the stock market, and promoted well-diversified allocations and age-appropriate risk-taking. 无论提示是由普通用户还是学者编写，大型语言模型的建议都比学者们预期的要好。它引导人们提高储蓄，增加股市参与，并促进多元化配置和与年龄相适应的风险承担。

“We were somewhat surprised by how good the advice was,” Choukhmane said. “Especially when you read the kind of questions people asked, it was not a given that the advice would line up with what academics think are good financial principles.”“我们对建议的质量感到有些惊讶，”舒赫曼说。“尤其是当你读到人们提出的各类问题时，这些建议能与学者们认为的良好财务原则保持一致，并非理所当然。”

2. AI misses important nuances. Better prompts could help.2. 人工智能忽略了重要的细微差别。更好的提示可以有所帮助。

The LLMs’ advice fell short on more subtle aspects of good financial planning. It tended to rely on simple rules of thumb for saving and spending and didn’t adjust well enough when circumstances changed. For example, it advised people who had experienced a job loss to cut spending too sharply, even when they had savings. 大型语言模型的建议在良好财务规划的更微妙方面有所欠缺。它倾向于依赖简单的储蓄和支出经验法则，并且在情况变化时没有足够好地调整。例如，它建议经历过失业的人大幅削减支出，即使他们有储蓄。

The way people ask questions is part of the problem. A typical prompt might read: “Where should I invest starting with $50 and consistently adding $25 a month after?”人们提问的方式是问题的一部分。一个典型的提示可能是：“我应该从哪里开始投资，初始投入50美元，之后每月持续增加25美元？”

When a more detailed, structured “academic” prompt was used, the LLM performed better. For example, an academic prompt might tell the chatbot to assume normal life expectancy, living expenditures, retirement age, employment risk, and income risk, and to assume that current U.S. tax law and Social Security rules will not change.当使用更详细、结构化的“学术”提示时，大型语言模型表现得更好。例如，一个学术提示可能会告诉聊天机器人假设正常的预期寿命、生活支出、退休年龄、就业风险和收入风险，并假设美国当前的税法和社保规则不会改变。

“Regular people are not writing their prompts the way a finance professor is,” Choukhmane said.“普通人不会像金融学教授那样编写提示，”舒赫曼说。

3. AI advice varies depending on the user, which can lead to wealth gaps.3. 人工智能的建议因用户而异，这可能导致财富差距。

The authors found that LLMs’ advice differs depending on the prompter’s gender, financial literacy, and experience, leading to meaningful gaps in retirement wealth. 作者发现，大型语言模型的建议因提示者的性别、金融素养和经验而异，导致退休财富出现显著差距。

Following the advice in response to prompts written by men, more financially literate users, or those with prior AI experience generated about 5% more wealth close to retirement. Specifically, 遵循针对男性、金融素养更高的用户或有AI使用经验的用户所编写提示的建议，在临近退休时产生了约5%更多的财富。具体来说，

• The LLM recommended higher equity allocations in response to prompts written by men and by individuals with high financial literacy. Over the life cycle, such differences in investment advice compounded into roughly $50,000 (4%) lower wealth at age 60 for women and for less financially literate users.大型语言模型对男性和金融素养高的个人编写的提示推荐了更高的股票配置。在生命周期中，这种投资建议的差异累积起来，导致女性和金融素养较低的用户在60岁时财富减少约5万美元（4%）。
• The LLM recommended lower saving rates in response to prompts written by individuals who had not previously used AI for financial advice. Following the advice left them with almost $100,000 (6%) less wealth at age 60 than individuals with prior AI experience. 大型语言模型对以前未使用过AI获取财务建议的个人编写的提示推荐了较低的储蓄率。遵循这些建议后，他们在60岁时的财富比有AI使用经验的个人少了近10万美元（6%）。

These differences come from two sources, Choukhmane said. First, different users asked different kinds of questions and often brought up different topics. Women, for example, were more likely to use words such as “family,” “grocery,” and “pay” in their prompts, while men used words like “strategy,” “crypto,” and “growth,” he said. 这些差异来自两个来源，舒赫曼说。首先，不同的用户提出不同类型的问题，并且经常涉及不同的话题。例如，女性更可能在提示中使用“家庭”、“杂货”和“支付”等词，而男性则使用“策略”、“加密货币”和“增长”等词，他说。

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Second, the model may give different advice even when the underlying question is the same. In the case of gender, about two-thirds of the gender gap in wealth outcomes could be attributed to differences in how men and women wrote their prompts, while the remaining third came from the model changing its advice when the same prompt was labeled as coming from a woman rather than a man. 其次，即使潜在的问题相同，模型也可能给出不同的建议。在性别方面，财富结果中的性别差距约有三分之二可归因于男性和女性编写提示方式的差异，其余三分之一来自模型在相同提示被标记为来自女性而非男性时改变建议。

That latter pattern could reflect the LLM making reasonable inferences about how preferences or circumstances vary by gender — which, ideally, the model could make explicit to users, Choukhmane said — or it could reflect biases learned from training data.后一种模式可能反映大型语言模型对偏好或情况如何因性别而异的合理推断——理想情况下，模型可以向用户明确说明这一点，舒赫曼说——或者它可能反映从训练数据中习得的偏见。

The challenge with AI financial advice is that there are no clear benchmarks, Choukhmane said. Only when there is an accepted framework for how advice should vary with demographics will LLMs be capable of progressing in the right direction. He said he remains hopeful that will happen. 舒赫曼说，AI财务建议面临的挑战是没有明确的基准。只有当一个公认的框架能够说明建议应如何随人口特征变化时，大型语言模型才能朝着正确的方向前进。他说，他仍然希望这种情况能够实现。

In addition, it’s important to remember that not all variation in advice is problematic, Choukhmane said. For example, “we want [the LLM] to have different bias because men and women are different and have different life expectancy and income risk,” he said. 此外，重要的是要记住，并非所有建议的差异都是有问题的，舒赫曼说。例如，“我们希望大型语言模型有不同的偏向，因为男性和女性是不同的，他们的预期寿命和收入风险也不同，”他说。

Takeaways for consumers 给消费者的要点

Beyond being mindful of bias, and, for the time being, asking LLMs to guard against it, success boils down to smarter prompts. Prompts grounded in life-cycle planning, portfolio theory, and real-world financial assumptions improved advice on spending and saving and cut down on basic, rule-of-thumb answers.除了注意偏见，并在目前要求大型语言模型防范偏见之外，成功归结为更聪明的提示。基于生命周期规划、投资组合理论和现实世界财务假设的提示改善了关于支出和储蓄的建议，并减少了基本的经验法则式回答。

“I think the real challenge is, how do we make sure that AI financial advice delivers for people who don’t have [a] level of financial literacy and who don’t write prompts perfectly?” Choukhmane said. One idea for people interested in using AI for financial advice is to start by using AI as a tool for building financial understanding rather than simply following its advice.“我认为真正的挑战是，我们如何确保AI财务建议能为那些金融素养不高、无法完美编写提示的人带来价值？”舒赫曼说。对于那些有兴趣使用AI获取财务建议的人，一个想法是首先将AI作为建立财务理解能力的工具，而不是简单地遵循其建议。

AI can serve as a good complement to working with a financial advisor — someone you might meet with twice a year — because it can help you implement the advice they give you in real time, Choukhmane said. 舒赫曼说，AI可以很好地补充与财务顾问（可能每半年见一次）的合作，因为它可以帮助你实时实施他们给你的建议。

And for people who don’t have the money to work with a human financial advisor, AI is a good way to get advice inexpensively. “A lot of the people who would benefit from financial advice are precisely the people who don’t have a lot of resources,” he said.对于没有钱聘请人工财务顾问的人来说，AI是获得廉价建议的好方法。“许多本应从财务建议中获益的人恰恰是那些没有多少资源的人，”他说。

Takeaways for business 给企业的要点

As consumers increasingly turn to LLMs for financial advice, providers may need to rethink how customers learn about their products. The study found that LLMs often recommended specific account types, financial products, and providers that respondents themselves did not mention. (For example, Vanguard investment products appeared in 6% of LLM responses, and iShares products appeared in 3.4%, even though fewer than 0.4% of prompts mentioned either company.)随着消费者越来越多地转向大型语言模型寻求财务建议，服务提供商可能需要重新思考客户如何了解他们的产品。研究发现，大型语言模型经常推荐受访者自己未提及的特定账户类型、金融产品和服务提供商。（例如，Vanguard投资产品出现在6%的LLM回复中，iShares产品出现在3.4%的回复中，尽管不到0.4%的提示提到这两家公司。）

That suggests that AI advice might be changing how people find and compare financial products, Choukhmane said. For financial firms, attracting customers’ attention may depend less on traditional marketing or search visibility and more on whether and how their products are described by LLMs when consumers seek advice.舒赫曼说，这表明AI建议可能正在改变人们查找和比较金融产品的方式。对于金融机构来说，吸引客户的注意力可能不再那么依赖于传统营销或搜索可见性，而更多地取决于当消费者寻求建议时，大型语言模型是否以及如何描述他们的产品。

“AI Financial Advice: Supply, Demand, and Life Cycle Implications,” which won the Swiss Finance Institute Outstanding Paper Award 2026, was written by Taha Choukhmane, Weidong Lin, and Matthew Akuzawa from MIT Sloan and by Tim de Silva from the Stanford Graduate School of Business.荣获2026年瑞士金融学院杰出论文奖的《AI财务建议：供给、需求与生命周期影响》由麻省理工学院斯隆管理学院的塔哈·舒赫曼、林卫东和马修·阿库扎瓦，以及斯坦福商学院的蒂姆·德·席尔瓦共同撰写。

Taha Choukhmane is an assistant professor of finance at the MIT Sloan School of Management. Choukhmane received the 2025 TIAA Paul A. Samuelson Award for Outstanding Scholarly Writing on Lifelong Financial Security from the TIAA Institute. His winning paper, co-authored with Lucas Goodman of the U.S. Department of the Treasury and Yale University’s Cormac O’Dea, is “Efficiency in Household Decision-Making: Evidence From the Retirement Savings of US Couples.”塔哈·舒赫曼是麻省理工学院斯隆管理学院的金融学助理教授。舒赫曼获得了TIAA研究所颁发的2025年TIAA保罗·A·萨缪尔森终身财务安全杰出学术写作奖。他获奖的论文是与美国财政部的卢卡斯·古德曼和耶鲁大学的科马克·奥迪亚合著的《家庭决策效率：来自美国夫妇退休储蓄的证据》。