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?人们正越来越多地转向人工智能寻求财务建议，但遵循这些建议真的能改善他们的财务状况吗？

“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.麻省理工学院斯隆管理学院金融学助理教授、一篇衡量和分析大语言模型财务建议质量的新论文合著者塔哈·舒克曼（Taha Choukhmane）表示：“一半的美国人称他们正在使用人工智能获取财务建议，但我们对他们收到的建议类型以及他们是否采纳这些建议知之甚少。”

Research by Choukhmane and co-authors showed that following AI recommendations can result in sizable saving buffers for virtually all individuals above age 30.舒克曼及其合著者进行的研究表明，对于几乎所有 30 岁以上的人来说，遵循人工智能的建议可以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.人工智能始终建议人们在工作期间进行储蓄，在退休后支取储蓄，大量投资于多元化的股票基金，并在 45 岁后降低股票敞口。然而，人工智能聊天机器人在应对失业等突发状况时表现欠佳，且往往任由投资组合随波逐流，而非主动进行再平衡。

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词（prompt）时，大语言模型（LLM）提供的财务建议质量有所提高，但人工智能仍经常表现出主动投资组合再平衡不足的问题。

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. 研究人员建立了一个模型，反映了人们的收入、工作、投资和税收在生命周期中通常是如何演变的，这为衡量什么是“好的”财务决策提供了一个基准。

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. 随后，他们邀请了 1,000 名成年人作为样本，让他们编写自己的提示词，向 GPT-5.2、GPT-5.6 或 Gemini 3 Flash 寻求消费和投资建议。

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 岁至 89 岁的人群随着时间推移遵循这些建议会发生什么，即反复向人工智能询问这些类型的问题，并遵循其在消费、储蓄和投资方面的建议。

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，他们使用包含完整财务信息和明确假设的、撰写规范的学术性提示词重复了这一练习。这些更详细的提示词包含了有关个人年龄、工作状况、收入和储蓄余额的信息，以及关于经济环境的假设。

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（如果普通人遵循他们给出的提示词所得到的人工智能建议会发生什么）与人们在没有人工智能帮助下已经采取的财务行为进行了对比。他们还将模拟建议与使用学术性提示词的结果进行了比较。

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，帮助用户克服传统人类财务顾问所带来的高昂成本、偏见和利益冲突。

Breaking down the findings研究结果解析

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. 总的来说，研究人员发现大语言模型随时间推移提供的财务建议质量良好，且在以学术方式提问时效果更佳，同时这些模型也各有利弊。

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. 无论提示词是由普通用户还是学术人员编写，大语言模型的建议都比学者们预期的要好。它引导人们增加储蓄，提高对股票市场的参与度，并促进了资产的多元化配置和与年龄相符的风险承担。

“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，”舒克曼说，“特别是当你阅读人们提出的问题时，人工智能的建议能够符合学术界公认的良好财务原则，这并非理所当然。”

2. AI misses important nuances. Better prompts could help.2. 人工智能忽略了重要的细微差别。更好的提示词或许能有所帮助。

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. 大语言模型的建议在良好财务规划的微妙之处上有所欠缺。它倾向于依赖简单的经验法则来处理储蓄和消费，且在情况发生变化时无法做出充分调整。例如，它建议失业者过度削减开支，即使他们已经有了积蓄。

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。一个典型的提示词可能是：“我应该如何开始投资，初始资金 50 美元，之后每月固定投入 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“学术性”提示词时，大语言模型的表现更好。例如，学术性提示词可能会要求聊天机器人假设正常的预期寿命、生活开支、退休年龄、就业风险和收入风险，并假设当前的美国税法和社会保障规则不会发生变化。

“Regular people are not writing their prompts the way a finance professor is,” Choukhmane said.“普通人编写提示词的方式与金融学教授并不一样，”舒克曼说。

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，大语言模型的建议会根据提问者的性别、金融素养和经验而有所不同，从而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、金融素养较高用户或有 AI 使用经验者所编写的提示词而给出的建议，在接近退休时产生的财富多出约 5%。具体而言：

• 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.针对男性和金融素养较高个人编写的提示词，大语言模型建议了更高的股票配置。在整个生命周期中，这种投资建议的差异导致女性和金融素养较低的用户在 60 岁时的财富减少了约 50,000 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. 针对此前未使用过人工智能进行财务咨询的个人编写的提示词，大语言模型建议了较低的储蓄率。遵循这些建议使他们 60 岁时的财富比有 AI 使用经验的人少了近 100,000 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. 舒克曼指出，这些差异来自两个方面。首先，不同的用户会提出不同类型的问题，并且经常涉及不同的话题。他说，例如，女性在提示词中更倾向于使用“家庭”、“杂货”和“支付”等词汇，而男性则使用“策略”、“加密货币”和“增长”等词汇。

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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，即使基本问题相同，模型也可能给出不同的建议。以性别为例，财富结果中约三分之二的性别差距可归因于男女编写提示词方式的差异，而其余三分之一则来自模型在识别到提示词来自女性而非男性时所做的建议调整。

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.舒克曼表示，后一种模式可能反映了大语言模型针对不同性别在偏好或境遇上的差异所做的合理推断（理想情况下，模型应向用户明确说明这一点），也可能反映了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. 舒克曼说，人工智能财务建议面临的挑战在于缺乏明确的基准。只有当针对不同人群应如何调整建议达成共识框架时，大语言模型才能朝着正确的方向进步。他表示，对此仍抱有希望。

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，舒克曼提醒，并非所有建议上的差异都是有问题的。例如，“我们希望（大语言模型）具有不同的偏向，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 给消费者的启示

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.除了留意偏见并暂时要求人工智能防范偏见外，成功的关键在于更聪明的提示词。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词，能够改善消费和储蓄建议，并减少基础性的经验法则式回答。

“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.“我认为真正的挑战在于，我们如何确保人工智能财务建议能够惠及那些金融素养水平不高、且无法完美编写提示词的人？”舒克曼说。对于有兴趣使用人工智能获取财务建议的人，一个建议是：将人工智能作为建立财务认知的工具，而不是仅仅盲目遵循其建议。

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. 舒克曼认为，人工智能可以作为人类财务顾问（你可能一年只会见两次面）的良好补充，因为它可以帮助你实时落实顾问提供的建议。

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.对于那些没有资金聘请人类财务顾问的人来说，人工智能是一种低成本获取建议的好方法。“许多能从财务建议中受益的人，恰恰是那些资源匮乏的人，”他说。

Takeaways for business 给企业的启示

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了解其产品的方式。研究发现，大语言模型经常推荐受访者自己并未提及的特定账户类型、金融产品和提供商。（例如，Vanguard 投资产品出现在 6% 的人工智能回复中，iShares 产品出现在 3.4% 的回复中，尽管只有不到 0.4% 的提示词提到了这两家公司。）

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.舒克曼表示，这表明人工智能建议可能会改变人们发现和比较金融产品的方式。对于金融公司而言，吸引客户关注的关键可能不再仅仅取决于传统的营销或搜索可见度，而更多取决于消费者寻求建议时，大语言模型如何描述其产品。

“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.《人工智能财务建议：供给、需求与生命周期影响》（AI Financial Advice: Supply, Demand, and Life Cycle Implications）一文获得了 2026 年瑞士金融研究所杰出论文奖，由麻省理工学院斯隆管理学院的塔哈·舒克曼（Taha Choukhmane）、林卫东（Weidong Lin）、Matthew Akuzawa 以及斯坦福大学商学院的 Tim de Silva 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·舒克曼是麻省理工学院斯隆管理学院的金融学助理教授。舒克曼曾获得 TIAA 研究所颁发的 2025 年保罗·A·萨缪尔森终身财务保障杰出学术写作奖。他的获奖论文《家庭决策的效率：来自美国家庭退休储蓄的证据》（Efficiency in Household Decision-Making: Evidence From the Retirement Savings of US Couples）是与美国财政部的 Lucas Goodman 和耶鲁大学的 Cormac O’Dea 合著的。