Around 70% of Americans and Europeans currently use either a computer or a mobile phone as their primary means of interacting with their banks, and that number is certain to rise as generations of digital natives become the primary market for financial services. As people grow increasingly comfortable with the role of technology in their lives, we’re likely to outsource a lot more of our repetitive work to artificial intelligences. Even in relatively sensitive areas like finance, AI is proving to be a fast learner and a helpful assistant, and the financial instruments of the future are likely to come with a strong AI component.

AI is a frequently-misunderstood technology, but the machine learning that is the most common manifestation of AI today has a very simple idea at its core: machines are fantastic at finding patterns in large amounts of data. This talent is driving some of the biggest new finance trends, such as robo-advising and chatbots.


Robo-advisors, fortunately, don’t look much like the Terminator or HAL-9000. They’re just lines of computer code that take in information about users (your investment needs) and markets (available assets), process that information, and return a good investment strategy (risky? conservative? in-between?).
Their main product is long-term buy-and-hold strategies, not super-fast algorithmic day trading, though most of them do automatically update users’ portfolios as the market changes. With low overhead and minimal human management needs, they’re a cheap, easy option for investors, and with more advanced AI technology their financial plans will only get more personalized and flexible.


Robo-advising isn’t this technology’s only consumer-facing fintech application, though: if you’ve ever interacted with a chatbot, odds are good that you’ve met an AI. Natural language processing technologies are making robots a lot better at understanding humans, and they can often deliver helpful responses much more quickly. Finances can get confusing, so having 24/7 robotic financial advisors will be a big help.

For example, the FINMATEX chatbot is designed to help users choose the best banks and financial products for their needs. The chatbot has access to large amounts of information about credit cards, bank accounts, savings instruments, and more, and it can help to both educate people about their available options and steer them towards the best solutions. It can even look up the nearest bank branches for those situations where a brick-and-mortar location is necessary.

Other applications

Some companies are also putting AI to work as a way to assess risk, using them to calculate things like credit scores and insurance rates. People who don’t have a credit history might be able to get a loan by letting an AI take a look at their social media profile, or an insurance company might use satellite data to assess property value. Other companies and banks are developing AI that helps users find their optimal instruments for spending and saving money—important tools in a world full of too many choices. We have more data than ever before in history, and machine learning is the key to making it work for us. Whether it’s scanning for signs of fraud or dispensing customized financial advice, AI is set to become a new norm in finance.

Over the next three to five years, AIs with a focus on financial expertise will continue to develop and present new opportunities at both the small-scale personal finance level and the large-scale investment level. AIs are already capable of providing good service at a lower cost, but they’re improving quickly, and soon may be able to cheaply compete with experts. Many futurologists believe that by the 2030s, most professional fields will be using some form of narrow AI, or artificial intelligence that specializes in a certain type of operation. By the 2050s, though, it’s likely that we’ll see the first general AI, capable of processing pretty much any type of input, understanding it, and reacting to it without human guidance.