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?

“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.

Research by Choukhmane and co-authors showed that following AI recommendations can result in sizable saving buffers for virtually all individuals above age 30.

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.

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. 

How the study was conducted

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. 

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. 

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. 

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. 

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. 

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.

Breaking down the findings

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. 

1. AI encourages smart financial behavior.

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. 

“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.”

2. AI misses important nuances. Better prompts could help.

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. 

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?”

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.

“Regular people are not writing their prompts the way a finance professor is,” Choukhmane said.

3. AI advice varies depending on the user, which can lead to wealth gaps.

The authors found that LLMs’ advice differs depending on the prompter’s gender, financial literacy, and experience, leading to meaningful gaps in retirement wealth. 

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, 

• 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.
• 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. 

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. 

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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. 

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.

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. 

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. 

Takeaways for consumers 

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.

“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.

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. 

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.

Takeaways for business 

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.)

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.

“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.

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.”