Why AI in Fintech Is About Speed, Not Replacement

According to an article on AIJourn, Aliaksei Tulia, Chief Technical Officer at CoinsPaid, believes the most practical role of AI in fintech is not to replace human decision-making but to eliminate unnecessary waiting across engineering and operational processes. In a recent discussion, he shared how the company approaches AI adoption, where it is already creating measurable improvements, and why strong governance remains essential in financial technology.

Defining “Useful AI” in Payments

For Tulia, the concept of useful AI is simple: it removes friction from everyday work.

Modern fintech companies process enormous volumes of information — technical documentation, system logs, customer requests, and operational reports. Many of these activities are repetitive and time-consuming, even though they still require human oversight.

AI can help handle the first layer of these tasks. It can summarize documents, analyze logs, classify files, or draft initial technical documentation. When these repetitive steps are automated, engineers and operations teams can concentrate on areas that truly require human expertise, such as architecture design, complex troubleshooting, and improving product reliability.

In other words, AI becomes a productivity tool that frees people to focus on higher-impact work.

Why CoinsPaid Started Using AI

The company initially turned to AI for a very practical reason: improving speed without sacrificing control.

Like many technology organizations, CoinsPaid deals with a large number of operational processes that do not directly create business value but still consume significant time. These include preparing test cases, reviewing documentation, answering routine requests, and retrieving information from internal systems.

When AI assists with these processes, queues become shorter and teams can respond more quickly.

However, Tulia stresses that fintech companies must treat AI with caution. Introducing new tools without proper oversight can create security vulnerabilities or compliance risks. That is why CoinsPaid prioritized governance frameworks and security controls before expanding its AI usage internally.

AI in Engineering and Operations

Today, the company focuses on practical applications rather than experimental ones.

Within engineering teams, AI tools help developers generate routine code, analyze technical requirements, identify logic gaps, and produce testing scripts. These capabilities accelerate development cycles while maintaining consistency across projects.

AI is also used in operational workflows. One important example is document processing, where the company built an internal system that automatically classifies files based on their sensitivity.

Protecting Sensitive Data

Handling payment infrastructure involves large volumes of confidential information, including financial data, internal documentation, and security artifacts. To manage this safely, CoinsPaid developed a document classification system that labels files as public, internal, confidential, or secret.

Each label determines how the document can be stored, accessed, or processed.

The system operates entirely within the company’s own infrastructure, ensuring that sensitive information never leaves the organization’s secure environment. This approach helps maintain strict control over data while still allowing teams to benefit from AI-assisted tools.

Managing Developer Access to AI

AI coding assistants can significantly boost developer productivity, but they also introduce potential risks, particularly when proprietary code is involved.

To address this, CoinsPaid established clear internal policies. Sensitive data cannot be exposed to external systems, and engineers must work within approved tools and environments. While AI may assist with drafting code or generating tests, the final responsibility for every output remains with human developers.

Critical decisions — especially those related to security architecture or regulatory compliance — are always reviewed by experienced specialists.

Measuring the Impact

The company evaluates AI adoption through measurable performance improvements rather than hype.

In certain areas, particularly test generation and routine development tasks, teams have observed noticeable reductions in cycle time. This means faster iterations and shorter development queues.

At the same time, Tulia emphasizes that AI is not a universal shortcut. Complex engineering challenges still require careful analysis and expertise. AI is most effective when applied to repetitive processes that slow down teams.

AI and Security

Cybersecurity is another area where AI can offer meaningful support.

Security teams frequently perform repetitive analytical tasks such as reviewing system diagrams, mapping data flows, or drafting threat models. AI tools can produce an initial analysis quickly, allowing specialists to focus on evaluating risks and making final decisions.

However, the company maintains a strict rule: AI assists, but humans remain responsible.

Looking Ahead

In the coming years, CoinsPaid plans to expand AI usage in three main areas: engineering productivity, customer self-service tools, and operational forecasting.

These initiatives aim to help teams anticipate workload patterns, respond to client requests faster, and maintain reliable payment infrastructure.

For Tulia, the future of AI in fintech is not about autonomous financial systems but about better coordination between people and technology.

Companies that succeed will not simply adopt AI tools. They will build structured processes and governance around them — ensuring that innovation improves speed and efficiency without compromising trust or security.