AI in the Banking Sector

AI in banking is no longer just a technological novelty; it has become an expectation driven by both business needs and customer demands. Which processes, then, can be optimized through the implementation of AI-powered services?

In this article, we explore innovations in banking and the broader financial sector, highlighting the areas where banks are increasingly leveraging artificial intelligence.

Among the industries most often mentioned as embracing AI are healthcare (supporting diagnostics, treatment, analytics, and patient services), education (personalizing learning paths, democratizing access to education in developing countries, modernizing learning materials) and also transport (introducing shared economy models in deliveries, optimizing supply channels, and enabling predictive analytics).

Also at the forefront are the legal sector and the financial services sector.

The latter, in particular, shows growing interest in robotics and automation, especially in simplifying complex business processes.
Polish banks serve as a benchmark in this area. According to Deloitte’s Digital Banking Maturity 2024 report, several leading domestic financial institutions were ranked among the industry leaders. These include PKO Bank Polski, ING, Santander Bank Polska, and Alior Bank—all of which have introduced significant improvements, particularly in customer service.

Banks are increasingly investing in mobile banking (Poland is among the top countries with the highest share of electronic banking users) as well as new functionalities designed to make customer interactions easier, for example, while traveling. However, many of the most pressing changes are needed within financial institutions themselves.

In the following sections, we discuss the opportunities that different AI tools are creating in the financial sector.

The first major theme concerning the role of AI in financial services is process automation, which can be divided into internal and external processes.

Internal processes encompass tasks customers don’t directly see.

These are often extensive workflows requiring input from multiple teams. AI can help by solving a specific problem or delivering value end-to-end, across every stage of a process. The latter approach is especially impactful, as it allows for greater relief of employee workloads.

When discussing how to relieve employee workloads in banks and other financial firms, the time-consuming nature of document processing cannot be overlooked.

Banks providing loans to individuals and companies rely on vast amounts of documentation. This includes contracts, financial statements, notarial deeds, identity documents, certificates, and more. Many banks have already implemented basic OCR systems to digitize documents.

However, older “legacy” OCR systems have significant limitations. They rely on fixed document templates, struggle with lower-quality documents, and often lack multilingual support.

AI models based on NLP (Natural Language Processing) significantly accelerate this type of work. With solutions that enable data extraction from documents, many processes can be fully automated. AI models can be tailored to the specific needs of a company and do not rely on predefined document templates. Machine learning—the continuous improvement of AI models—further enhances processing quality. In the case of document management, this translates into higher data extraction accuracy and substantial time savings compared to manual processing.

Artificial intelligence also addresses the challenge of process scalability.

Human work is inherently difficult to scale—especially when it involves reviewing hundreds of pages of documents. The key advantage of applying AI in document processing is breaking the direct link between scaling operations and expanding the workforce.

Regardless of the industry, every company aims to conduct an in-depth analysis of customer behavior – this is the key to improving its offering and adapting to client needs. It is no surprise, then, that banks use artificial intelligence to both identify and address those needs.

AI enables faster and more personalized customer service. This is one of the areas where AI adoption happened earliest and with notable success, often replacing basic customer support in large call centers.

Today, many banks in Poland use automated hotlines, chatbots, and virtual assistants powered by Natural Language Processing (NLP). Their tasks include automatically responding to customer questions, solving repetitive and simple issues, and supporting banking processes such as password resets or application submissions.

Such straightforward automation increases the security of online and mobile applications while significantly reducing response times.

Another important optimization area for customer service and advisory functions lies in the implementation of data-driven algorithms. These solutions support financial advisors in creating personalized credit and investment offers. AI not only accelerates the generation of tailored solutions but also allows banks to serve more clients in parallel.

Where vast amounts of data exist, automation is essential. The financial sector is a prime example.

AI enables rapid processing and interpretation of massive datasets, far beyond human capability. Instead of relying on massive spreadsheets, dedicated AI-driven tools identify patterns and correlations – for example, linking increased interest in certain products with specific market events.

This empowers banks and financial institutions to make better decisions and design offerings based on real-time market insights and customer trends.

At this point, it is worth emphasizing that one of the major challenges for financial institutions lies in the integration of different AI-driven tools.

For example, to maximize efficiency, data extraction models should seamlessly feed information into a central repository, which can then be utilized by Big Data platforms or risk analysis systems. Only in such a connected environment can the full potential of automation and AI be unlocked.

Critical issue is implementation management – contribute to a coherent digital transformation strategy, rather than functioning as isolated improvements. While standalone tools may bring tangible benefits, their value increases significantly when combined with complementary technologies.

When mBank launched in 2000 as the first fully virtual bank in Poland, many experts questioned whether such a bold move could succeed. A bank without physical branches, breaking away from the traditional customer service model, seemed like a risky experiment. Yet the gamble paid off – today, most banks in Poland aim for clients to handle the majority of their banking needs online, through apps or hotlines.

It is likely that the growing role of artificial intelligence in banking will soon force industry leaders to make crucial decisions about the future of customer services. What will become the key competitive advantage?

Perhaps generative AI models will go beyond merely creating offers and start managing simpler customer service decisions, combining historical data with predictive models. Virtual assistants could then deliver even more personalized products, tailored to each customer’s transaction history.

Interestingly, ChatGPT, when asked about the future of banking, suggested potential integration of banking ecosystems with Smart City solutions – for instance, recommending better leasing terms based on a client’s actual driving history.

As the AI revolution is still in its early stages, its long-term effects on the banking sector remain uncertain. While many advocate for transformation and digitization, challenges such as genAI hallucinations and integration difficulties cannot be ignored. It is unclear whether banks will limit AI’s role over time or move toward allowing generative AI to fully manage customer offers.

Time will tell, but all indications suggest that market leaders will increasingly adopt AI-powered tools.

Do you work in the financial sector? Let’s talk. 4Semantics has extensive experience in implementing document automation solutions for financial institutions. In a conversation, we can walk you through our platform and help you identify the processes that place the heaviest burden on your team.