Almost 200 years ago, Charles Darwin said, “It is not the strongest or the most intelligent of the species that survive, but the one that adapts to change the fastest”. And although this thought originally referred to evolutionary processes, today it could well be a commentary on changes in the financial market.

AI and machine learning are technologies that have been increasingly used in banking in recent years. Banks are aware that in order to remain competitive, they must develop technologically, so they turn their focus to solutions that will ensure their growth. According to Accenture, by 2035, AI will double its annual economic growth rate, contributing to the evolution of working methods and building new relationships between humans and machines. In addition, forecasts indicate that artificial intelligence will increase the efficiency of enterprises by up to 40 percent, and enable employees to use their working time more efficiently. Undoubtedly, there are also a number of functionalities in the financial industry that can be optimized based on AI mechanisms, so we decided to take a closer look at them. We chose five key areas that we believe will soon be dominated by AI:
• customer service,
• customer experience,
• consulting (robo-consulting),
• data processing,
• cybersecurity and fraud detection.


Banks are aware that in order to remain competitive, they must develop technologically, so they turn their focus to solutions that will ensure their growth.

1. Customer Service

Although it’s difficult to imagine customer service utterly devoid of the human factor, AI modules can be effectively used mainly in ​​process automation and contact channel management. Automating repetitive banking processes that don’t require excessive verification not only saves a lot of money in the long run, but also reduces the number of mistakes, which are an integral part of manual handling. Artificial intelligence implemented in customer service processes also means increased efficiency and transparency of activities.

Due to changes in communication models, i.e., the transition from traditional channels to ones in which customers communicate with brands (social media, messaging, mobile applications), the number of channels that banks must use in order to contact their customers is growing every year. In this case, the solution doesn’t have to be increasing the resources of customer service departments, but also, and perhaps above all, implementing an artificial intelligence module, for example, in chatbots or various types of virtual assistants. A well-configured chatbot is able to handle many more standard inquiries and problems faced by customers, and most importantly, it’s available almost immediately after the customer initiates contact.

2. Customer Experience

Artificial intelligence can also serve as a support in building the customer experience. Analysis and interpretation of data allow for even greater personalization, not only in terms of the offer, but also contact with the brand itself. User-specific content and high availability of services are just some of the elements of a good CX. Thanks to behavioral analyses and statistics generated in real time, banks can more accurately draw conclusions about customer needs. In addition, artificial intelligence helps in optimizing the customer journey – a touchpoint analysis helps identify problems that may affect the customer’s “purchasing” decisions. According to an IDC report, artificial intelligence can optimize processes at almost every stage of the customer’s contact with the bank, particularly in the following areas:

  • Advertising, marketing and engaging processes at the stage of interaction between the brand and the customer. This allows a better understanding of the consumer, and tailors a unique and personalized service to them.
  • Interaction with the consumer to provide additional information digitally and to support and help employees in cooperation with the customer.
  • Direct and indirect customer and business support, getting the best value out of your transaction and resolving any problems or errors that may arise.
  • Better understanding and supporting the relationship between the customer and the company, primarily through data analysis and interpretation.
  • Focusing on customer characteristics through the use of artificial intelligence. Analysis of data collected on the customer to better understand their needs.

3. Consulting (robo-consulting)

An interesting area in the implementation of artificial intelligence is robo-consulting, i.e., automatic investment consulting for clients. It consists of artificial intelligence learning the client’s needs on the basis of databases, and proposes investment strategies dedicated to the customer, then manages the assets until a specific profit is obtained. Robo-advisers also enable full automation of some asset management services and online financial planning tools. By analyzing a number of historical data, they’re able to make better predictions about the behavior of investment portfolios. At the same time, they help customers make better-informed spending and savings decisions based on behavioral analysis. However, the lack of appropriate legislation regulating the functioning of robo-advisory services may stand in the way of the development of these types of services. Nevertheless, in many countries, the situation is changing and, for example, in Poland – one of the largest fintech markets in Central and Eastern Europe – the Polish Financial Supervision Authority (the body supervising domestic financial institutions within the meaning of EU regulations) has prepared a draft establishing a formal position on robo-advisory services. The website of the organization stated: “The draft document is aimed at comprehensively referring to the most important issues related to the conduct of robo-advisory services, which should be included in the activities of the supervised entity. The project also applies to the entire process, from the service design phase to its practical implementation and monitoring of existing solutions. The position will be aimed at ensuring uniform implementation of robo-advisory services by interested financial institutions, while taking into account adequate protection of clients, especially non-professional investors.

4. Data processing

Due to their specific nature, banks process huge volumes of data on a daily basis. There are two challenges in this approach: how to do it quickly and how to extract the maximum amount of information from the data. AI addresses the problem of high performance and speed, and also allows for high-level inference based on highly advanced analyses resulting from machine learning. Robots based on cognitive technologies related to the development of artificial intelligence can analyze the content of correspondence with customers, verify the correctness of complex loan documentation, and make behavioral segmentation based on the actual financial behavior of customers or even provide consulting services.

5. Cybersecurity and fraud detection

In online banking, AI is primarily used for customer identification and fraud prevention. Credit card fraud has become one of the most widespread forms of cybercrime in recent years, driven by the massive increase in online and mobile payments. To identify illegal activity, artificial intelligence algorithms validate customers’ credit card transactions in real time and compare new transactions with previous amounts and the locations from which they were performed. The system blocks transactions if it sees any potential risk. To combat fraud, organizations are also increasingly using biometrics, which makes it possible to recognize people based on their physical characteristics. This method assumes the verification of users before they log into the system, based on, inter alia, fingerprints, iris, or face shape (so-called facial recognition). Modules such as AML, Anti-fraud and KYC, supported by artificial intelligence, allow a significant reduction in the risk and losses related to financial fraud, which is reflected in the activities of organizations exposed to this risk.


The future of the banking industry in the context of the use of AI and machine learning is extremely intriguing. The progressive automation of the banking industry and greater openness to new technologies on the one hand realizes the huge potential of this service area, and on the other, opens the door to new threats and cybercriminals. That’s why it’s so important that the realization of tasks related to the implementation of artificial intelligence and machine learning takes place in accordance with good practices and with the participation of experienced technology and business partners.



Michał Mazur is the Senior Business Development Manager at INCAT Sp. z o.o. In his over 20-year career, he has been involved in financial and IT sector projects. He has extensive experience in project management, analysis, business development, system architecture, and quality assurance.
Michał Mazur is a graduate of AGH University of Science and Technology.

Contact an author: