The Future of Finance: How Artificial Intelligence Is Changing Banks and Investments

According to a KPMG study, since 2024, AI has been actively used in the financial sector. About 88% of respondents confirm that they are actively implementing artificial intelligence into financial infrastructure.

Analysts predict (and business professionals confirm) that in the near future, 100% of fintech solutions will partially or fully operate with artificial intelligence.

On the one hand, this is great, because in 92% of cases, investments in AI have a positive ROI. On the other hand, it is quite a big risk. Both for users and, in fact, for business itself.

Innovation Everywhere: Changing the Technological Landscape of Financial Solutions

Analysts from the McKinsey group note that about 64% of companies see innovative benefits from implementing artificial intelligence. In the context of fintech solutions specifically, it really provides businesses with a number of advantages:

• Personalization of financial services. AI makes it possible to create individual offers for clients. From credit products to investment advice.
• Forecasting and analytics. Machine learning helps predict market trends, assess risks, and optimize investment portfolios.
• Process automation. Robotic systems reduce the time needed for customer verification (KYC), combating money laundering (AML), and compliance.
• Fraud protection. AI systems detect anomalies in transactions in real time, which significantly reduces the risks of financial crimes.
• Cost reduction. Automation and process optimization allow financial companies to reduce operating costs and increase efficiency.
• Innovative products. Thanks to AI, new services appear: robo-advisors, smart contracts, dynamic insurance products.

And these trends can be seen in every business area related to finance in one form or another.

The Evolution of Fintech and the Role of AI

Just a couple of years ago, fintech was everything that could process financial transactions or related operations. Now these are full-fledged AI platforms with analytics based on LLMs. Moreover, with the help of AI, new products are now being created, market scenarios are being modeled, and so on.

As stated in the Stanford HAI AI Index Report 2025, the US government invested about $109.1 billion in artificial intelligence. And most of this funding went specifically to the financial sector. Quite serious, isn't it?

AI in the Banking Sector

With banks, the situation is almost identical to the general one in the FinTech segment. The investments are colossal, except that the emphases are somewhat different. In particular, in banking, AI is used for:

• credit scoring;
• risk assessment;
• combating fraud;
• product personalization;
• optimization of KYC/AML processes and compliance;
• reduction of costs for operational procedures.

As noted back in the US Department of the Treasury Report 2024, AI is considered a strategic driver. Although at the same time, the risks are also emphasized, which we will discuss in the next parts of the article.

AI in Investments

Investment platforms and exchanges are also actively deploying LLMs in their own products. AI-based tools are becoming a support for beginners and a valuable assistance for professionals. For example:

• algorithmic trading and robo-advisors are becoming the standard in portfolio management;
• machine learning is used to predict market trends and evaluate startups;
• AI helps venture funds analyze risks and investment potential faster.

As stated in Reuters AI Investment Wave 2025, the volume of investments in AI has already exceeded $1.6 trillion and continues to grow. And this is despite the current bottlenecks of the technology.

Challenges and Risks of Using AI in Finance

The advantages of using AI are clear. These are cost-effective tool models that are equally useful for businesses and the target audience. But what about the reverse side of the technology? Where are the guarantees that critical vulnerabilities, outright scams, and so on are not hidden behind a beautiful facade?

Of course, one can argue with the policies of fintech startups and companies, compliance, and high standards. But a number of challenges are still relevant.

Ethical Issues: Algorithm Transparency, Bias in Data

Due to the specifics of the segment, AI in finance works practically like "black boxes." That is, the end user has no idea about the "inner kitchen," does not see how data is processed, and so on. And there is also a risk of discrimination due to bias in the data (for example, in credit scoring).

Even the US Department of the Treasury Report 2024 points out this point as a risky one.

Cybersecurity and Personal Data Protection

Let us recall how many times in 2025–2026 cloud infrastructures of absolutely different (and far from small) providers "went down." Partly this is due to overload by the same AI data processing processes.

But this is only one side of the issue. The other is: who can guarantee that during such external and internal failures, there will be no leaks of financial data?

Even the KPMG AI in Finance Report 2024 notes that 72% of financial companies in the USA consider cybersecurity the main challenge when implementing AI.

Regulatory Barriers and the Need for International Standards

Although they are trying to standardize the AI niche, the norms and rules differ somewhat between the US and European markets. This complicates migration and scaling even for large representatives of the FinTech league. Although startups have an advantage in this matter.

Balance Between Innovation and Customer Trust

"58% of customers are ready to use AI banking only on the condition of a transparent explanation of decisions," says the McKinsey 2025 report. Customers expect innovative services, but at the same time, they fear losing control over their finances.

Therefore, financial companies must communicate the benefits of AI. They must explain to customers how exactly the algorithms work.

Future Prospects

Provided that issues of artificial intelligence in the segment of financial technologies and services are properly regulated, the prospects are quite positive. However, it is important here that both regulators (including at the international level), and enterprises, and even end users, are open to innovations.

How to achieve this:

• Work on the technological and financial literacy of the population.
• Modernize solutions with an emphasis on their transparency, security, and stability.
• Implement unambiguous rules of operation, demand their compliance, and constantly monitor financial providers.

Looking at the trends and volumes of investments in the FinTech sector (including AI), it can be stated that a transformation of the niche is visible on the horizon. Whether in a better or worse direction—time and the readiness of business and its customers for innovations will show.