Digital Transformation in Banks of Different Sizes: Evidence From the Polish Banking Sector

Digital Transformation in Banks of Different Sizes: Evidence From the Polish Banking Sector

Teresa Czerwińska, Adam Głogowski, Tomasz Gromek, Paweł Pisany
DOI: 10.4018/978-1-7998-4390-0.ch009
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Abstract

Technological advances in data transmission and processing are an important structural factor influencing the banking sector. As they have an important impact on the cost base of banks and are characterized by large one-off costs, it is argued that investments in digital technologies enhance the positive returns to scale in the banking system. This in turn further improves the competitive position of the largest market players, creating a positive feedback loop. In the longer run, this leads to a polarization of the banking sector, between large universal banks and small specialized banks. These processes are important from the point of view of macroprudential policy, notably in the dimension of reducing the risks connected with the emergence of “too big to fail” institutions. The chapter illustrates these issues using the results of a survey on the investments of banks in digital transformation in Poland, focusing on the relationships between market structure, financial position, and investments in digital technologies.
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Introduction

Technological advances in data transmission and processing are an important structural factor influencing the banking sector. As they can affect the cost base of banks, investments in digital technologies influence the competitive position of individual banks, the competitive landscape – and in medium term – the market structure of the banking sector.

Rapid technological changes challenge the role of banks in the economy. Advances in data processing and data transmission allow financial services to be delivered remotely, with limited need for in-person contact between the financial institution and the customer. This can be seen as the way in which the so-called “Fourth Industrial Revolution” - as described e.g. by Schwab (2016) manifests itself in the banking sector. Advances in information analysis and processing, driven by technology companies and developed in non-financial industries – such as e-commerce or tailored advertising based on search results or social media content – mean that banks are no longer the industry which has the competitive advantage in processing information for business purposes. This lowers the barriers to entry into the markets of some financial services1.

As a result, banks face increased competitive pressure from fintechs, technology firms entering into finance (“bigtechs”) as well as other banks or financial institutions operating cross-border. In order to stay competitive, banks are starting to look for new ways to reduce costs or new sources of income and find them in providing innovative services and solutions for their clients, like mobile banking, new channels of communication with clients, big data analytics coupled with artificial intelligence algorithms, robo-advisory, etc. Banks need to adapt to the new competitive environment and use flexible and innovative technologies.

The digital transformation of the banking sector is not without challenges for financial stability, as the importance of cyber risk increases in the operational risk profile of banks. Thus public policies have a role to play in ensuring a sustainable development path of financial system. Public authorities and financial supervisors should reconcile various goals and balance different values in their activity, i.e. supporting innovation in the banking sector, but also protecting competition and consumer rights as well ensuring level playing field for different agents and preventing regulatory arbitrage. In the context of microprudential supervision cybersecurity issues have received increased attention, notably through the issuance of guidelines on managing cybersecurity risks2. As far as macroprudential perspective is concerned, potential structural changes, for example increasing concentration in the tech-suppliers markets, need to be monitored3. A trade-off between development pace and sustainability exists and well-balanced regulatory scheme may slow down financial innovation in the short term, while maximizing benefits versus risks ratio in the long-term.

As investments in digital technologies are characterized by large one-off costs, their implementation is easier for banks which are characterized by a large scale of operations and sound financial position. Given that many of these innovations result in lower long-term operational costs, it is argued that they enhance positive returns to scale in the banking system. This, in turn, further improves the competitive position of the largest market players, creating a positive feedback loop. Furthermore, this amplifies the “demise of the middle tier” in the banking sector – i.e. medium sized universal banks are less able to compete with the largest players, as the mid-tier banks find it hard to invest in digitalization at the same pace as the largest ones and thus are at risk of being stuck with higher cost ratios and lower profitability. In the longer run, this leads to a polarization of the banking sector between large universal banks and small specialized banks. Another factor accelerating this process is the growing role of remote (internet and mobile) banking, giving the most technologically proficient banks the ability to compete for clients without the need to scale up their physical network. The role of remote transaction channels is particularly important during disruptions in the functioning of the economy which make it difficult to use face-to-face communication, such as during public health emergencies or natural disasters. Of course these solutions can function only when the telecommunications infrastructure is operational and is not affected by the disruption in question.

Key Terms in this Chapter

Cloud Computing: An online network (“cloud”) of computer system resources, available on- and/or off-site, scalable, and flexible that can be accessed on a pay-as-you-go basis. Cloud service providers offer a wide range of services, namely: storage space, computing power, developer platforms or software and web applications. This model enables convenient access to a shared pool of configurable hardware and software (e.g. networks, servers, storage facilities, applications, and services) swiftly called up and released with minimal management effort or service provider interaction.

Fintech: Technology-enabled innovation in financial services that could result in new business models, applications, processes, or products with an associated material effect on the provision of financial services. The term is used to refer both to the phenomenon as a whole and to companies, especially in start-up or growth phase, which aim to deliver financial services in new ways, heavily relying on ICT.

Big Data: The use of very large and complex data sets for business analytics. Big Data sets are usually defined by their size (exceeding the capability of common statistical software and metods), complexity (as they can contain data of multiple types, including numbers, text, image, etc.), velocity (as these sets can be collected and analyzed in a continuous fashion, e.g. in case of data capturing the patterns of use of an application by clients).

Digital Transformation: The process by which an organization increases the role of digital processes in its activities. In this research this term is used to describe gradual changes in business models of banks as a result of increased use of ICT both in internal operations of banks as well as in the customer interaction channels.

Distributed Ledger Technology (DLT): A type of database where data is stored in multiple synchronized copies and is updated and replicated using a consensus algorithm, which ensures that only entries that are accepted by a majority of participants can be entered into the database. Blockchain systems are a form of DLT.

Artificial Intelligence and Machine Learning (AI & ML): AI may be broadly defined as the application of computational tools to address tasks traditionally requiring human sophistication (Financial Stability Board, 2017). In the view presented by FSB, ML is a sub-category of AI and refers to the method of designing a sequence of actions to solve a problem, known as algorithms, which optimize automatically through experience and with limited or no human intervention. In other words, ML is a science that is aimed at providing computers with ability to learn directly from the data, without applying a program created ex-ante.

Robotic Process Automation (RPA): A business automation technology, where within digital systems, routine tasks are partially or fully performed by software solutions or “bots” that automate manual, laborious, rule-based, high-volume, repeatable tasks and repetitive activities originally executed by humans.

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