Do people trust AI on financial decisions? We found it really depends on who they are

When it comes to investing and planning your financial future, are you more willing to trust a person or a computer?

This isn’t a hypothetical question any more.

Big banks and investment firms are using artificial intelligence (AI) to help make financial predictions and give advice to clients.

Morgan Stanley uses AI^[1] to mitigate the potential biases of its financial analysts when it comes to stock market predictions. And one of the world’s biggest investment banks, Goldman Sachs, recently announced it was trialling the use of AI to help write computer code^[2], though the bank declined to say which division it was being used in. Other companies are using AI to predict which stocks might go up or down.

But do people actually trust these AI advisers with their money?

Our new research^[3] examines this question. We found it really depends on who you are and your prior knowledge of AI and how it works.

Trust differences

To examine the question of trust when it comes to using AI for investment, we asked 3,600 people in the United States to imagine they were getting advice about the stock market.

In these imagined scenarios, some people got advice from human experts. Others got advice from AI. And some got advice from humans working together with AI.

In general, people were less likely to follow advice if they knew AI was involved in making it. They seemed to trust the human experts more.

But the distrust of AI wasn’t universal. Some groups of people were more open to AI advice than others.

For example, women were more likely to trust AI advice than men (by 7.5%). People who knew more about AI were more willing to listen to the advice it provided (by 10.1%). And politics mattered – people who supported the Democratic Party were more open to AI advice than others (by 7.3%).

We also found people were more likely to trust simpler AI methods.

When we told our research participants the AI was using something called “ordinary least squares^[4]” (a basic mathematics technique in which a straight line is used to estimate the relationship between two variables), they were more likely to trust it than when we said it was using “deep learning” (a more complex AI method).

This might be because people tend to trust things they understand. Much like how a person might trust a simple calculator more than a complex scientific instrument they have never seen before.

Trust in the future of finance

As AI becomes more common in the financial world, companies will need to find ways to improve levels of trust.

This might involve teaching people more about how the AI systems work, being clear about when and how AI is being used, and finding the right balance between human experts and AI.

Furthermore, we need to tailor how AI advice is presented to different groups of people and show how well AI performs over time compared to human experts.

The future of finance might involve a lot more AI, but only if people learn to trust it. It’s a bit like learning to trust self-driving cars. The technology might be great, but if people don’t feel comfortable using it, it won’t catch on.

Our research shows that building this trust isn’t just about making better AI. It’s about understanding how people think and feel about AI. It’s about bridging the gap between what AI can do and what people believe it can do.

As we move forward, we’ll need to keep studying how people react to AI in finance. We’ll need to find ways to make AI not just a powerful tool, but a trusted advisor that people feel comfortable relying on for important financial decisions.

The world of finance is changing fast, and AI is a big part of that change. But in the end, it’s still people who decide where to put their money. Understanding how to build trust between humans and AI will be key to shaping the future of finance.

References

1. ^{^} Morgan Stanley uses AI (www.morganstanley.com)
2. ^{^} AI to help write computer code (www.cnbc.com)
3. ^{^} new research (papers.ssrn.com)
4. ^{^} ordinary least squares (soc.utah.edu)