When the Algorithm Decides for You: Why Banks Block Transactions and Who Is Responsible for AI Decisions

Sometimes digital reality feels almost absurd.

You make a completely ordinary purchase in an online store. The payment fails. The bank blocks the transaction.

You contact support and ask why the payment was blocked. The answer is simple. The algorithm made the decision. You try to understand how the algorithm works and what criteria it uses, but no one can explain it.

This situation is no longer rare.

Today, more and more decisions in digital services are made by automated systems. At the same time, when a user tries to understand why a decision was made, the answer often sounds the same. The system made the decision.

Why banks use algorithms and what is happening in the market

Why banks use algorithms

Banks are required to prevent fraud and suspicious transactions. In Russia, this obligation is shaped by financial regulations, anti-money laundering laws, payment system rules, and broader legal requirements related to fraud prevention.

To process millions of transactions, banks rely on anti-fraud algorithms. These systems analyze user behavior, transaction geography, payment frequency, the device or channel used, and the overall risk profile of a transaction.

If the system detects potential risk factors, the transaction may be temporarily suspended or declined.

What is happening in practice

Banks are required to respond to suspicious activity. If a transaction falls under certain risk criteria, the bank may pause the transfer, initiate additional verification, or request confirmation from the client.

For example, a check may be triggered if the recipient appears in fraud-related databases, if user behavior changes unexpectedly, if contact details are modified, or if the transaction does not match typical patterns.

In some cases, the transfer may be temporarily suspended to verify that it was initiated voluntarily. The bank is expected to notify the client, explain the situation, and provide an opportunity to confirm the operation.

In recent years, such restrictions have become more frequent. This trend is often linked to stronger anti-fraud systems, broader definitions of suspicious activity, and the growing number of fraud schemes.

Why banks must explain their decisions

From a regulatory perspective, the principle is clear. A client should understand why a transaction was restricted and what needs to be done next.

For this reason, the response that the system made the decision is not considered sufficient.

Why this topic matters

Algorithms are no longer limited to technical processes. They now make decisions that directly affect people’s lives.

A loan may be approved or rejected. A resume may be shown to an employer or filtered out. An account may be blocked. A price may change. Content may be recommended or hidden.

In many cases, users are not even aware that an algorithm is making these decisions. Algorithms are becoming an invisible infrastructure of the digital economy.

Five decisions algorithms already make for you

What decisions algorithms already make for you

In today’s digital environment, many decisions are made automatically, often without the user noticing it.

An anti-fraud system may approve or reject a payment before it is completed. A credit scoring system may determine whether a loan is granted. Recruitment systems may filter candidates and decide which resumes are shown. Digital platforms may determine which content is visible and which is hidden. Marketplaces may adjust prices dynamically depending on demand, timing, and user behavior.

As a result, a large share of decisions in the digital economy is no longer made directly by a person, but by systems of rules and algorithms.

Where the main problem arises

Algorithms can be highly effective, but the key issue lies in transparency.

In practice, situations often unfold in the same way. A transaction is blocked. The user does not understand why. Customer support cannot explain the reasoning behind the decision.

This leads to what is often described as the algorithmic black box. The system makes a decision, but the logic behind it remains unclear.

Why this becomes a legal issue

Automated decisions can directly affect access to money, the ability to complete transactions, the use of services, and even a person’s reputation.

At the same time, it is often unclear which criteria are applied, what data is used, and how a decision can be challenged. This is why algorithmic transparency has become one of the key issues in the digital economy.

Banks are only part of the picture. Algorithms are widely used in credit scoring, fraud detection, transaction monitoring, recruitment systems, content ranking, marketplace pricing, and insurance risk assessment.

Why algorithms make mistakes

Automated decisions are not always correct.

Errors may occur because of incorrect data, incomplete information, biased datasets, or flaws in the model itself. In some cases, decisions are made without any human review.

Any system that operates on probabilities can produce incorrect outcomes.

Who is actually responsible for AI decisions

From a legal perspective, an algorithm is not a subject of law. It cannot bear responsibility or act as an independent decision maker.

An algorithm is a tool.

In practice, however, the situation is more complex. Software developers often attempt to limit their liability through user agreements, stating that the system provides recommendations and that responsibility lies with the user.

For the end user, this distinction changes little. If a bank or digital platform uses an automated system to make decisions and restrict user actions, that service is responsible for implementing the system and dealing with its consequences.

Developers may be held accountable in other contexts, such as software defects or security violations. However, disclaimers alone cannot remove responsibility.

What is happening globally

This issue is not unique to Russia. Around the world, regulators are actively discussing who should be responsible for algorithmic decisions.

In the European Union, the AI Act establishes obligations for developers and operators of AI systems. International frameworks, such as OECD principles and NIST risk management guidelines, follow the same logic.

Artificial intelligence is treated as a tool, while responsibility remains with people and organizations that design and use it.

Why these situations will become more frequent

Algorithms are already deeply embedded in banking, marketplaces, social networks, government services, healthcare, advertising, and recommendation systems.

As more decisions become automated, users will increasingly encounter situations where a system makes a decision, but no one can clearly explain it.

What users can do and why algorithms are called a “black box”

What users can do

If you encounter a blocked transaction or a rejected operation, it is important to request an explanation and clarify the specific reason for the restriction.

A decision should be justified. Written requests are often reviewed more carefully than verbal inquiries. If necessary, users can contact a regulator, such as the central bank or another supervisory authority.

Why algorithms are called a “black box”

In many systems, it is difficult to quickly explain why a particular decision was made. Algorithms may analyze dozens or even hundreds of factors simultaneously.

For the user, however, the situation appears simple. The payment fails. The account is blocked. The loan is rejected.

And the explanation often remains the same. The system made the decision.

The main question of the digital era

The problem is not that algorithms can make mistakes. Any system can fail.

The real question is different. Who is responsible for a decision made by an algorithm?

An algorithm does not act independently. It operates within a system designed and controlled by people.

How ContentGuard can help

Automated decisions increasingly affect everyday life, from banking operations to digital services.

ContentGuard lawyers help users navigate situations where decisions are made automatically and user rights need to be protected. In a world driven by algorithms, it is essential to understand not only how systems work, but also who is responsible for their outcomes.