AI and Cryptocurrencies: How Artificial Intelligence Can Change Finance, Banking and Blockchain

Imagine that your financial app no longer just shows your account balance. Artificial intelligence notices an unusual payment, warns you that it is probably fraud, compares your spending with previous months and suggests where you could save. The next step could be that the same system, following pre-set rules, prepares a money transfer, a currency exchange or a transaction with a digital asset on its own. The question will then not only be “What does AI recommend to me?” but also “What will I allow it to do with my money?”

This is exactly where two technological worlds that have so far often developed separately are starting to meet. Artificial intelligence can analyse large amounts of information and increasingly also perform certain tasks on its own. Blockchain, in turn, makes it possible to transfer digital value, use smart contracts and build financial systems that can operate around the clock. If these two technologies are connected, services may emerge in which software not only evaluates something but then also carries out a specific financial operation.

This does not mean, however, that AI will replace banks or that cryptocurrencies will automatically take over the traditional financial system. A gradual blending of both worlds is more likely. Banks already use artificial intelligence today to detect fraud, analyse risk and communicate with clients. At the same time, technologies are emerging that allow AI agents to work with blockchain wallets, smart contracts or stablecoins. The future may therefore not be a battle of AI against banks, nor of cryptocurrencies against fiat currencies, but rather a search for where each technology makes the most sense.

AI is already changing traditional banking

When people talk about artificial intelligence today, many picture mainly chatbots or tools capable of generating text and images. In the financial sector, however, various forms of machine learning have been used for many years. Banks and other financial institutions use algorithms to analyse huge numbers of transactions and look for patterns that a human would find difficult to recognise.

One of the best-known areas is fraud detection. A banking system can continuously evaluate whether a particular payment matches the client’s usual behaviour. If a person normally pays by card in the Czech Republic and within a few minutes a large payment appears in another part of the world, the system may assess the transaction as risky. Similar technologies can also help with detecting money laundering, assessing credit risk, processing documents or customer support.

With the development of generative AI, the possibilities are expanding even further. A banking app does not have to just display numbers; it can also interpret them. Instead of informing the client that they spent CZK 42,000 in a month, the system can point out that their energy costs have risen by 25% compared to the long-term average, or that a service they have not used for several months has appeared among their regular payments. Banking can thus gradually change from a tool for displaying financial data into a system that actively helps clients understand that data.

From AI assistant to AI agent

Today’s AI mostly works as an advisor. A user can ask how much they spent last month, request an explanation of a specific financial product or have different options compared. Artificial intelligence provides the answer, but the decision itself and the subsequent operation are carried out by a human.

The next stage of development is represented by so-called AI agents. These are systems that can be given a specific goal, use other tools and independently perform multiple steps needed to achieve it. The difference is therefore not only in the quality of the answer. An agent can also gain the ability to act.

Imagine, for example, the instruction: “Every month after payday, keep a reserve of CZK 30,000 in the account, check the regular payments and split the remaining amount according to my pre-set rules.” Today’s AI can create a recommendation. A future agent equipped with the appropriate permissions could prepare some of the steps or even carry them out directly.

It is at this point that the combination of AI and blockchain starts to become interesting. If software can analyse a situation and at the same time has the technical ability to work with digital assets, a new type of financial tool emerges.

Why blockchain may be interesting for AI

The traditional financial system was created primarily for people, companies and financial institutions. We have bank accounts, payment cards, signing authorities, identification processes and other mechanisms designed for human users. Software, however, may have different needs.

Imagine an AI agent that needs to find the right data source within a few seconds, find out its price, pay a few cents, download the data and then use it for another task. For an operation like this, a traditional payment card or bank transfer may not always be the most efficient solution.

Blockchain offers several properties that may be interesting for autonomous software. It operates around the clock, a digital wallet can technically be controlled by software as well, transactions can be programmable and smart contracts can precisely define the rules under which funds are used. Blockchain systems also make it possible to work with very small amounts.

This does not mean that AI has to use cryptocurrencies. The banking sector is also building its own digital interfaces and APIs through which automated systems can work with traditional money. Blockchain, however, offers an interesting option: software can communicate directly with financial infrastructure built for programmable digital transactions.

An AI agent with its own wallet

The idea of AI having access to a wallet may seem dangerous at first glance. It does not have to mean, however, that the system gets unlimited control over all of the user’s funds. It is precisely blockchain and smart contracts that make it possible to set exact rules under which an agent can act.

The owner can, for example, specify that a single transaction must not exceed ten dollars, the daily limit is one hundred dollars and payments may only go to pre-approved services. Any higher amount would require human confirmation. AI can thus gain a certain economic autonomy without having absolute control over the money.

In this way, an agent can buy data, computing power, API access or other digital services. For a human, manually approving every payment worth a few cents would be impractical. For software, it can be a routine part of the task being performed.

Similar technologies are already emerging. One example is the x402 protocol, which builds on the idea of the internet code “402 Payment Required”. A server can tell an AI agent the price of the requested service, the agent makes the payment and then gains access to the requested data or functions. This is still a developing area and it is not certain how significant it will be in the future, but it shows how automated payments between software could work.

Stablecoins as a means of payment for AI

A volatile cryptocurrency may not be the ideal means of paying for digital services. If an AI agent needs to pay, for example, two cents for an API request, it is more practical to use an asset whose value does not change significantly against the currency in which the service is priced.

This is where stablecoins can gain importance. A stablecoin pegged to the US dollar combines a familiar unit of price with blockchain infrastructure. An agent can thus have, for example, a budget of 100 USDC and use individual parts of this amount according to pre-defined rules.

For AI, it does not matter whether a given asset is an interesting investment. What matters is whether it can reliably transfer value from one place to another. In some scenarios, a stablecoin can therefore function less as an investment cryptocurrency and more as a digital payment instrument.

From the perspective of an ordinary user, something else may be interesting. A person may not even know that a stablecoin is being used in the background. Just as today we usually do not think about all the systems that process a card payment, one day a user may simply enter: “Pay for this service.” And the app will choose the appropriate payment infrastructure on its own.

AI can make cryptocurrencies much simpler

One of the biggest obstacles to the wider use of cryptocurrencies is their technical complexity. A beginner quickly encounters terms such as seed phrase, blockchain address, gas, Layer 2, bridge, smart contract or various blockchain networks. Each of these terms has its technical justification, but together they can represent a high barrier to entry.

AI can insert another layer between the user and the technical infrastructure. A person does not need to know exactly which network to choose or how a bridge works. They can say, for example: “I want to send 50 USDC to this person via the cheapest supported route.” The system can compare the available options, point out the risks and explain how each option works.

Here, however, it is important to distinguish between a recommendation and the execution of a transaction. If AI only advises and the user then confirms everything themselves, there is still room for a check. If the system has access to private keys or the authority to sign a transaction on its own, its mistake can have immediate financial consequences.

The more we hide technical complexity, the more important it will be to check what is happening beneath the surface.

AI and smart contracts

Another area where both technologies naturally meet is the development and security of smart contracts. AI can help programmers explain existing code, create tests, look for suspicious spots or propose scenarios of a possible attack on a smart contract.

This can significantly speed up both development and security review. At the same time, however, the same rule applies as in ordinary programming: AI can produce faulty code. On an ordinary website, a bug may mean a button that does not work. In a smart contract that manages a large amount of digital assets, a bug can mean a financial loss.

An interesting example came from the Ethereum Foundation, whose security team used coordinated AI agents to search for bugs in code related to Ethereum’s infrastructure. The systems managed to find real problems, but they also generated outputs that seemed convincing even though they did not represent any real vulnerability.

This illustrates the dual nature of AI well. It can significantly increase the ability to find security problems, but on its own it is not a substitute for expert review.

AI as a new tool for blockchain analysis

Public blockchains contain an enormous amount of transaction data. Individual operations can be tracked, but with millions of transactions, manual analysis is practically impossible. AI and other analytical systems can look for patterns that a human would find difficult to recognise.

They can, for example, track how funds move between a larger number of addresses, search for suspicious transaction chains or identify addresses linked to already known scams. The same principle can be used in AML monitoring, counterparty risk assessment or analysing the behaviour of individual blockchain protocols.

In this respect, the traditional banking system and the blockchain world do not differ much. In both cases, it is about analysing enormous amounts of financial data and looking for unusual patterns.

The difference lies in the availability of data. A public blockchain can provide a very detailed transaction history, but at the same time a blockchain address does not automatically reveal the real identity of its user. AI can therefore be very effective at analysing behaviour, but it cannot always easily determine who is actually behind a particular address.

How AI can change everyday banking

For most people, the biggest change may not take place on a cryptocurrency exchange, but right in their mobile banking.

Today’s banking app usually shows the balance, incoming and outgoing payments or basic statistics. AI can change this experience by interpreting financial data.

Instead of simple information about a transaction, the app can say: “You have been paying for this service for twelve months, but you have not used it in the last three months.” Or it can point out: “Your energy costs this month are significantly higher than usual.” The next step may be a recommendation on how to deal with the situation.

A banking app can thus change from a passive list of transactions into an active financial assistant. This can be especially useful for people who do not find their way around finance very well. At the same time, however, it raises the question of how far we want financial institutions or their technology partners to analyse our behaviour.

Better financial services versus more data

AI works best when it has high-quality data. The more it knows about a client, the more accurate, for example, a risk estimate, a financial product recommendation or the detection of a suspicious payment can be.

This can bring concrete benefits. Traditional credit models sometimes work with relatively limited information. AI can analyse a broader history of financial behaviour and, for example, better assess a self-employed person who does not have a regular monthly salary but has shown a stable income over the long term.

The same principle, however, raises the question of privacy. How much information should a bank analyse? Should it know our shopping behaviour when deciding on a loan? How will it explain to a client why the model rejected them? And what if the algorithm uses correlations that work statistically but lead to an unfair result?

AI can thus make financial services more precise, but more data does not automatically mean fairer decision-making.

AI against AI: a new fight against financial fraud

One of the biggest paradoxes of artificial intelligence in finance is that the same technology can be used by both defenders and attackers.

Banks, exchanges and analytics companies use AI to detect suspicious transactions. Fraudsters, on the other hand, can use it to create more convincing scams.

Generative AI can create a fake email, imitate the style of a specific person, their voice, or create a deepfake video. A fraudster can thus create a fake investment advisor or impersonate a colleague, business partner or family member.

Cryptocurrencies can be attractive for such scams mainly because a blockchain transaction is usually difficult to reverse. If a victim sends funds to an address controlled by an attacker, there is not always an institution that could simply cancel the transaction.

AI can thus improve fraud detection while at the same time lowering the cost of creating fraud. In finance, a long-term technological race is therefore likely to emerge between systems that create scams and systems designed to detect them.

When seeing is no longer believing

Deepfake technology is also changing the way we verify identity.

In the past, a video call could be fairly strong evidence that we were communicating with a specific person. If we saw and heard someone, we had reason to believe it really was them.

Generative AI is gradually weakening this assumption.

If both image and voice can be realistically imitated, cryptographic verification may become more important. Instead of the question “Did I see this person on a video call?”, the more important question may be “Was this request really signed with an authorised key?”

This is exactly where blockchain and cryptography can gain new significance. Not necessarily as a way of investing in digital assets, but as infrastructure that can mathematically prove the origin of certain information or the authority to carry out a specific operation.

Of course, even a cryptographic signature is not an absolute solution. If an attacker obtains a private key, they can misuse it. Nevertheless, the way we prove trustworthiness in the digital world may be changing.

Can AI trade on its own?

Automated trading on financial markets has existed for a long time. Banks, funds and other institutions have been using algorithmic systems for many years. Generative AI, however, can make creating such strategies accessible even to people who cannot program.

A user can, for example, set a simple rule for regularly buying Bitcoin or a more complex system that responds to various market indicators.

Here, however, it is important to distinguish between the ability to automatically execute a strategy and the ability to reliably predict the future.

AI can quickly analyse enormous amounts of data. That does not mean, however, that it knows where the price of Bitcoin or a stock index will move next week. A model can work with faulty data, adapt to a past that will not repeat itself, or react to an unexpected event in a way its creator did not anticipate.

AI can be a very powerful analytical tool. It should not be mistaken for a machine that predicts the future.

What if everyone uses similar models

Another risk is not just the mistake of a single AI, but the possibility that many systems make a similar decision at the same time.

Imagine that a large number of banks, funds and automated trading systems use similar models and similar data. Significantly negative information appears on the market. The models evaluate it in a similar way and simultaneously start reducing risky positions.

Automated trading, of course, existed long before the current generation of AI. The new risk may lie in scale and concentration. If a large part of the market uses models from a few providers, a problem with one system can affect multiple institutions.

A similar question applies to cryptocurrencies. A blockchain may be decentralised, but the AI layer used on top of it may depend on a few centralised providers of models or cloud infrastructure.

This again recalls an important principle: decentralisation of one technological layer does not automatically mean decentralisation of the entire system.

An AI token is not automatically a quality AI project

When two popular areas come together, marketing naturally follows as well.

There are projects on the market that connect blockchain with computing power for AI, decentralised data, autonomous agents or other real technology. At the same time, however, a cryptocurrency may appear that uses the AI label mainly because it is a popular topic.

That is why it is important to distinguish between individual projects.

The name of a token or the claim that a project uses artificial intelligence says almost nothing about its real value. It is much more important to find out what problem it solves, whether it really needs blockchain for that, what function the token itself performs in the system and whether it has real use.

The same principle applied to previous cryptocurrency trends and will apply to AI as well.

A technological trend may be real, but that does not mean that every project that refers to it must be of high quality.

What the combination of AI and blockchain can bring

The greatest benefit may not lie in one revolutionary application. What may matter more is the gradual combination of several technologies.

AI can, for example, simplify the use of cryptocurrencies and hide part of the technical complexity of blockchain networks. It can analyse large numbers of transactions, flag suspicious activity or help developers review smart contracts. Autonomous agents, in turn, can use blockchain for small automated payments or access to digital services.

For the ordinary user, simplicity may be the most important thing. If AI can safely explain what happens during a transaction, compare the available options and warn of a potential mistake, it can significantly lower the barriers to entering the world of digital assets.

The most interesting use of AI may therefore not be speculating with cryptocurrencies. It may be precisely that complex technology becomes much less visible to the ordinary person.

Where the combination of AI and finance can fail

The greatest advantage of AI is at the same time the source of its greatest risk. The system can quickly assess a situation and react automatically. But if the assessment turns out to be wrong, it can just as quickly carry out an incorrect operation.

Large language models can produce convincing but factually incorrect answers. In a financial environment, such an error can have much more serious consequences than in an ordinary information search.

With blockchain transactions, the problem is even more pronounced, because many operations are difficult to reverse. An AI agent with overly broad permissions can, for example, send funds to the wrong address or use a faulty smart contract.

Another issue is privacy protection. A good financial assistant needs access to financial data. It is therefore important to know who processes the data, where it is stored and whether it can be used for other purposes.

With the growing autonomy of AI, a complex question of liability also arises. If the system carries out a wrong transaction, who bears the consequences? The user who gave it the permissions? The developer? The financial institution? The provider of the AI model itself? Answers to such questions will become increasingly important.

How much control should we hand over to AI

The basic principle can be simple: the greater the powers a system gains, the more important the control mechanisms must be.

If AI only explains a financial term, a possible mistake has relatively limited consequences. If it can transfer thousands of euros on its own, the situation is completely different.

That is why limits on the maximum amount, a list of permitted services, additional confirmation of larger operations or the ability to block access immediately can be important. The user should also know whether AI may only read financial data or whether it has the right to actually move funds.

The ideal goal may not be a system that is given absolute control.

For many people, an AI that prepares almost the entire operation, explains it and points out the risks, while the final decision still remains with the human, may be much more useful.

Will AI replace banks?

It is probably more useful to ask a different question: How will AI change the bank we already use?

Current developments mainly show the integration of AI into existing financial institutions. Banks use it for fraud detection, credit risk analysis, cybersecurity, compliance with regulatory rules or customer service.

The bank of the future may thus still work with fiat currencies, provide loans and operate within the regulated financial system. The difference may lie in how the client communicates with it.

Instead of searching for the right item in a menu, a person can say, for example: “I want to send 500 euros to my daughter and I don’t want to pay an unnecessarily high fee.” AI will find the available options, explain the differences and prepare the operation.

The person only checks and confirms it.

Will cryptocurrencies replace fiat currencies because of AI?

Here too, there is no reason to expect a simple scenario.

An AI agent can technically use euros in a bank account just as well as a stablecoin in a blockchain wallet, provided it has the appropriate access to both systems.

A more likely outcome may therefore be the existence of several parallel routes. Some operations will run through banking infrastructure and fiat currency, others through blockchain networks and stablecoins.

The user may not always know which infrastructure their app is using. What may be decisive is speed, cost, security, availability and the regulatory environment.

This is how part of the boundary between “traditional” and “crypto” finance may gradually disappear. For users, the service itself may matter more than the technology running beneath it.

What the financial AI of the future may look like

Imagine a small company that uses several digital services. Its AI agent can check invoices, compare the cost of cloud services, buy computing power only when it is actually needed, pay for APIs and alert the owner to unusual transactions.

Some operations may go through a traditional bank account. Some small digital payments may be easier to settle with a stablecoin. Meanwhile, a smart contract can make sure that the agent does not exceed its maximum daily budget.

The human remains the owner of the funds and sets the rules. AI handles routine operations.

Such a model may be more realistic than the idea that artificial intelligence will one day take over finance completely. It is not necessarily about removing the human from the system. Rather, it is about changing their role – from the executor of every single step to a person who sets the rules and oversees important decisions.

Conclusion

Artificial intelligence will probably not change only cryptocurrencies. It is already changing traditional banking today. Banks use it to detect fraud, analyse risk or serve clients, while the first systems are emerging in the blockchain environment in which AI agents can use wallets, smart contracts or stablecoins. The boundary between traditional and decentralised finance may thus gradually become less distinct.

The greatest opportunity of AI lies in its ability to analyse and automate complex financial systems and potentially make them accessible to a wider range of people. The greatest risk, however, stems from the same property: if we allow a system not only to advise but also to act, its mistake can have real financial consequences. Deepfakes, fraud, faulty data, unpredictable models or excessive concentration of control will therefore be just as important topics as speed and convenience.

The future may thus not be a question of “AI, banks or cryptocurrencies?”. What will matter more is how these technologies connect. A bank account, a blockchain wallet, a stablecoin and an AI agent may one day work side by side within a single service. And the key question will not only be what artificial intelligence can do. It will also be necessary to determine precisely what it may do with our money – and what it may not.

This text serves informational and educational purposes only and does not constitute investment advice. Crypto assets are volatile, and you may lose the entire amount invested.