How artificial intelligence is transforming the financial and banking industries

Yes, banks have not yet handed everything over to AI. But it is here that we can clearly see that the transformation of the financial sector is already in full swing. Not in words, but in the specific services we use every day.

We will discuss all this in more detail with our leading expert Yevhen  Kasyanenko in today’s article. We will analyze how artificial intelligence is already changing banks and finance today, where it is showing tangible results, and where it remains a prospect for the future.

Artificial intelligence and automation of financial processes

“Automation in finance often sounds like something grandiose and complicated. In reality, it’s much simpler: less manual routine, faster processes, fewer errors. Artificial intelligence in this story is like a reliable assistant that takes on the boring work. What takes employees hours to do, algorithms do in minutes. As a result, the bank does not waste energy on endless reconciliations and checks, but can focus on what is really important—service and new solutions for customers.”, notes our expert.

Let’s take a look at where exactly artificial intelligence in the financial and banking sector has already become part of everyday work.

How AI helps automate banking operations

Most of the work in banking remains behind the scenes—reporting, reconciliation, auditing. This is where the power of AI comes into play:

• Real-time data processing. Algorithms analyze transactions and customer profiles on the fly, reducing the risk of errors that are easy to make when entering data manually.
• Digital assistants instead of manual operations. Machines take over accounting, payment control, and compliance procedures. This frees up specialists and speeds up work cycles.
• Savings on routine tasks. Automating “back office” processes yields billions in savings, which banks can then invest in products and services for their customers.

Artificial intelligence in financial transactions

This is an area where everyone feels the changes—transfers, payments, and loans. Here, AI is already working directly:

• Verification of documents and transactions. Algorithms automatically check data and identify suspicious transactions, making payments faster and safer.
• More accurate customer assessment. In addition to income and credit history, AI looks at behavior, spending, and digital activity. This scoring helps make decisions faster and reduces the risk of defaults.
• Automatic reconciliation. Algorithms validate details and indicate where errors occur. The result is fewer failures and returns, and greater trust in the system.

These changes are almost imperceptible, but they are changing the rules of the game: banks are working faster and more reliably, and customers are receiving service that solves problems immediately and without unnecessary red tape.

Artificial intelligence in risk management and fraud prevention

“The financial sector is always walking on thin ice: any mistake can cost millions. That’s why banks and fintech companies were among the first to turn to artificial intelligence. But not because it’s trendy, but to really protect both their money and their customers. In the end, it turned out to be a win-win situation: businesses got smart tools to help keep risks under control, and users got more peace of mind and confidence that their finances are well protected,” says Yevhen  Kasyanenko.

Let’s take a closer look at how artificial intelligence helps banks assess borrowers more accurately and defend against fraudsters.

AI in credit scoring and borrower assessment

In the past, banks looked at a standard set of data: income, credit history, and a few basic indicators. But many people simply don’t have such a history, and they were automatically classified as “unreliable.” This is where AI comes in handy.

What has changed:

• A large amount of data for analysis. Algorithms take into account not only classic indicators, but also behavior: spending, shopping style, even digital activity.
• New opportunities for customers. People without a credit history but with stable financial behavior get a chance to take out a loan on fair terms.
• Fewer errors and rejections. Algorithms are becoming more accurate and fair, allowing more people to access financial products without unnecessary barriers.

Artificial intelligence against financial fraud

Fraudsters are always one step ahead, and traditional checks often fail to respond in time. But algorithms play by different rules—they see things that are difficult for humans to notice.

Here’s how it works:

• Real-time transaction analysis. AI tracks transactions on the fly and immediately flags suspicious ones. If something is wrong, the transfer is blocked or the system requests additional confirmation.
• Attack prevention. Algorithms can detect phishing attempts, document forgery, or account hacking before any damage is done.
• Monitoring complex schemes. AI helps banks combat fraudulent insurance claims, fictitious returns, and illegal transfers.

In the end, everyone wins—customers feel protected, and banks save time and money on security.

Personalizing customer services with AI

“Financial services are gradually learning to be real helpers rather than formal intermediaries. With the help of artificial intelligence, they are beginning to understand the customer: their habits, goals, and spending style. And instead of dry actions like ‘fulfill the request,’ they offer solutions that really simplify life,” says our expert.

Personalization in finance has long gone beyond simple templates. It can take many forms, from instant chatbot responses to insurance or investment recommendations that take into account your specific habits and goals.

Chatbots and voice assistants based on artificial intelligence in banks

Tiresome waits on the support line are becoming a rarity. Increasingly, instead of an operator, an AI assistant is the first to respond to a customer’s request. It offers the following advantages:

• 24/7 support without queues or waiting;
• assistance with transfers, card blocking, and product selection without operator involvement;
• advice on expenses and tariffs based on customer habits;
• more natural communication thanks to NLP technologies that bring the dialogue closer to live speech.

AI in insurance and asset management

But the help of assistants is just the tip of the iceberg. Artificial intelligence is penetrating deeper and deeper into financial areas where serious money and risks are at stake: insurance and investments. Here are some real-life examples:

• In insurance. AI takes into account medical data, lifestyle, and payment history to select conditions that are truly suitable for a particular person.
• In investments. Algorithms study trends, past experience, and the investor’s own style to offer more balanced and sustainable strategies.
• In portfolio management. Systems rebalance assets themselves, maintaining an optimal ratio and reducing risks without constant monitoring by the owner.

As a result, financial companies are moving away from universal templates and toward services that are tailored to specific individuals. Customers receive products that truly work for their needs, rather than just for the sake of it.

AI in investments and algorithmic trading

“Investing has always been a nerve-wracking business: the market rises and falls, the risks are enormous, and decisions are often made on the basis of emotions. Experience helps, but it’s still like playing a guessing game. With the advent of artificial intelligence, everything is changing. Algorithms are not subject to panic and excitement — they only work with data, look for patterns, and help predict market movements. Thanks to this, tools that were previously only available to professionals are becoming more understandable for ordinary investors,” notes Yevhen  Kasyanenko.

Let’s take a look at where AI has already made its mark in investing.

Artificial intelligence in the stock market

With AI, investors rely less on intuition and more on real data. Its key capabilities are as follows:

• More accurate forecasts. Algorithms sift through tons of historical data and current trends to find patterns that are difficult to spot on your own.
• Trading without unnecessary emotions. Automated systems operate at split-second speeds and react to the market without panic or excitement. This is especially important where split seconds matter—in high-frequency trading.
• Risk assessment. AI takes a broader view: it considers the economy, politics, and the actions of major players, helping to build more sustainable strategies.

The role of AI in investment portfolio management

Investments don’t like distractions: the market is constantly throwing up surprises, and it’s important to react in time. In such conditions, artificial intelligence in finance is very useful. It relieves investors of some of the routine work and helps them make more accurate decisions. Here’s how it works:

• Strategy selection. Algorithms look at a lot of data: from the investor’s risk level and goals to asset dynamics and the overall economic situation. The result is a more balanced portfolio.
• Robo-advisors. These are like digital consultants: they study the market themselves and offer ready-made solutions. They are cheaper than live experts and make investing more accessible.
• Auto-correction. When the market starts to “storm,” AI automatically changes the asset ratio, reducing risks and helping to maintain profitability.

As a result, investors don’t have to sit at their terminals for days on end. While AI handles the routine, investors can focus on strategy and big picture prospects.

The future of AI in finance: prospects and challenges

Finance is becoming faster and smarter with AI, but the challenges are growing along with it. It’s time to figure out what difficulties lie ahead for fintech.

Key challenges of implementing AI in fintech

As banks and fintech companies learn to trust algorithms, they face serious challenges:

• Regulation. The financial sector is full of rules and regulations, and algorithms must also comply with them. Data protection laws such as GDPR and PCI DSS are still in force. Transparency is also important: it must be clear why AI made a particular decision.
• Cybersecurity. AI works with the most valuable data – customers’ finances and personal information. For hackers, this is gold, so protection must be as reliable as possible.
• Ethics. Algorithms must play fair. It is unacceptable for AI to deny a loan simply because the data was incomplete or biased. It is important to understand exactly which factors are taken into account and to monitor that decisions are fair.

Where is AI headed in finance?

Despite the challenges, the direction of development is clear: AI will only become smarter and more deeply integrated into finance:

• Self-learning models. Algorithms capture market changes in real time and, in the future, will be able to warn of crises before they begin.
• AI and blockchain. Together, these technologies will make financial transactions even more transparent and secure by automating data analysis and tracking fraudulent schemes.
• Digital AI banks. Fully virtual banks will soon become commonplace: AI itself understands customer behavior, selects the right products, and can approve a loan in seconds.

“Artificial intelligence will not completely replace banks or financial specialists, but it will change the approach to work. It will take over routine tasks and calculations, leaving people with the most important things: strategy, creativity, and responsibility. It is in this symbiosis that the future of finance will be built,” summarizes Yevhen  Kasyanenko.

Why is it important to trust professionals with the development and application of artificial intelligence in finance?

Artificial intelligence in finance is not a field for experimentation. The slightest error in the algorithm can result in the loss of data or millions of dollars. That is why experience and accuracy are important here. Therefore, three things are particularly important when implementing AI:

• Expertise. Good models can only be built by specialists who know how machine learning works and how to apply it to real business tasks.
• Security. Financial data is a tempting target for hackers. Professional development takes security at all levels into account from the outset.
• Integration. Every company has its own rules and infrastructure. Experts help implement AI in a way that fits into processes and brings real benefits.

KISS Software – your partner in AI development

At KISS Software, led by Yevhen  Kasyanenko, we create AI solutions specifically for the financial industry. Our goal is not to use trendy technologies for the sake of technology, but to create real tools that help:

• eliminate routine tasks and speed up processes;
• reduce risks through smart analytics;
• make customer service personal and convenient.

Our experience shows that when AI is implemented correctly, it quickly turns from an experiment into a working tool for everyday use.

Conclusion

Artificial intelligence in finance has long ceased to be just a nice idea. It really simplifies processes, makes work more accurate, and customer service more convenient. Companies that use it today will win tomorrow: they are faster to restructure and cheaper to operate. But for all this to be effective, it is important to implement AI professionally, with the right protection and integration into the business.

Want to implement AI in finance? Entrust this to the KISS Software team — leave a request for a consultation, and together we will find the best solution for your tasks.