Digitalization and technology are introducing a number of benefits to banks, allowing them to improve efficiencies, cut cost and enhance customer experience. However, these advancements also introduce risks, including operational, reputational, and strategic risks, according to a new report by the Bank for International Settlements (BIS).

The report, titled “Digitalisation of Finance” and authored by the BIS’s Basel Committee on Banking Supervision, examines the ongoing digitalization of finance on banks, highlighting both the advantages and risks of new technologies and the rise of new technology-enabled service providers in the banking sector.

APIs are facilitating data sharing

According to the report, the ongoing digitalization of finance is characterized by the emergence and growing use of innovative technologies across various aspects of the banking value chain. These technologies include application programming interfaces (APIs), artificial intelligence and machine learning (AI/ML), and distributed ledger technology (DLT).

APIs facilitate data sharing between different applications, enabling efficient real-time processing and increased data connectivity. Around the world, banks are using APIs to share and import data between their internal systems for mobile banking, collaborate with external partners within their systems, collaborate with external partners through models like banking-as-a-service (BaaS), and connect with third parties such as account software providers, payment processors and alternative credit scoring companies.

APIs are also commonly used in open banking and open finance frameworks, which are rapidly being implemented around the world to encourage further innovations in business models and products, and foster financial inclusion.

AI and ML to boost efficiencies

Banks are also increasingly adopting AI and ML techniques to enhance their operations. These techniques are capable of predicting a wide variety of complex phenomena and have the potential to increase banks’ operational efficiency, risk management capabilities and product offering. This includes improving customer experience through streamlined interactions, offerings superior pattern recognition ability and predictive power, providing greater accuracy and consistency in processing, as well as enabling cost efficiencies.

AI holds tremendous potential in finance, with McKinsey estimating that AI technologies could deliver up to US$1 trillion of additional value each year for the global banking industry. This would be achieved through increased revenues through personalized services, cost efficiencies, and the uncovering of new and previously unrealized opportunities using data.

Banks are using AI and ML applications for both back office and front office functions with use cases including credit underwriting, trading activities, pricing models, regulatory capital and planning, liquidity requirements and planning, fraud detection and prevention, anti-money laundering and combating the financing of terrorism (AML/CFT), chatbots and marketing.

Most recently, generative AI (gen AI), a subfield of AI focused on developing algorithms and models capable of generating new text, images, or other media, has received significant public attention. Though banks’ use of gen AI remains limited at present, the BIS report notes that some are exploring or piloting gen AI applications internally to improve operational efficiency and staff productivity. Specific use cases observed include digital assistants, market analysis, fraud detection and code generation.

McKinsey estimates that gen AI could improve productivity in core corporate and investment banking (CIB) activities by between 30% to 90% in individual use cases, potentially adding up to about 10% of CIB operating profits in the long run.

DLT is opening up new opportunities

DLT is another technology transforming the banking industry by enabling digital money, tokenization, and improving the operational management of banks’ existing business activities. The technology is praised for its ability to lower costs and enhance efficiencies through automation and desintermediation.

One particular area of interest for banks is the tokenization of assets. Asset tokenization refers to the process of recording the rights to a given asset into a digital token that can be held, sold, and traded on a DLT platform. The resulting tokens represent a stake of ownership in the underlying asset. Asset tokenization has been praised for its potential to facilitate new ways of using financial assets to serve end users, offering new opportunities previously hindered by monetary system frictions.

Global management consultancy Roland Berger forecasts that the market for asset tokenization could mushroom to at least US$10 trillion by 2030. The value implies a 40-fold increase of the value of tokenized assets from 2022 to 2030, and marks a significant rise from the current value of around US$300 billion.

Notable use cases of asset tokenization by banks include the issuance of security tokens backed by real estate, the tokenization of banks’ shareholders’ equity, the tokenization and custody of bank customers’ shares, the tokenization of financial instruments such as intraday repo options and bonds, and the tokenization of the ownership rights in works of art.

Beyond tokenization, some banks are also using or exploring DLT for other purposes, including identification verification, settlement of tokenized transactions, cross-border payments, digital asset custody and bookkeeping.

Cloud computing fosters innovation

Finally, cloud computing promotes efficiency and economies of economies of scale by providing on-demand computer processing resources. These solutions allow for easier access to technology and computing infrastructure that would otherwise be expensive or take a long time to build and be costly to maintain. This reduces the barriers to entry for firms expanding into new products and services, and over time, reduce costs in financial services.

For banks, cloud services eliminate building costly on-premise data centers that cover peak-level computing burdens and, instead, allow them the flexibility to accommodate seasonal fluctuations in the need for computing.

For fintech startups, cloud services provide them with the infrastructure, tools, and flexibility needed to innovate, grow, and compete in the dynamic fintech landscape.

In the financial services sector, industry participants are embracing cloud computing at a fast pace. An industry survey conducted last year by Capgemini revealed that 91% of banks and insurance companies had initiated their cloud journey, a significant increase from 2020, when only 37% of firms had embarked on their cloud transformations. 89% of the financial services executives polled viewed cloud-enabled platform as crucial for delivering the agility, flexibility, innovation, and productivity necessary to meet escalating business demands.

Impact of new banking competitors and business models

Technological advances have led to the emergence of new market entrants and business models, increasing competition in the banking sector.

Digital-only banks, fintech startups, and bigtech firms are offering specialized digital financial services targeting individuals, entrepreneurs, and small and medium-sized enterprises (SMEs), often leveraging data and technology to enhance user experience. These companies also benefit from regulatory advantages over traditional banks due to their nimble nature, innovative technologies, and sometimes less complex business models.

Technological advances have also fostered strategic partnerships between banks and other firms. These partnerships aim to leverage the strengths of both parties, with banks providing infrastructure, expertise and regulatory permissions, and non-bank intermediaries contributing to product development, data analytics and user experience.

For banks, new technologies and partnerships offer opportunities for innovation, efficiency gains, and enhanced risk management. For consumers, digitalization promises expanded financial access, reduced transaction costs, improved experiences, and increased competition.

However, digital transformation also introduces new vulnerabilities and amplifies existing risks. Large-scale digital transformation projects carry risks related to legacy infrastructure and lack of expertise, particularly for smaller banks. Partnerships with non-banks, meanwhile, can create dependencies, jeopardizing banks’ control over volumes, product design, origination processes and customer relationship, and leading to potential losses in business and financial performance.

Furthermore, reputational and operational risks may arise from failures, non-compliance, and issues with third-party partners. Finally, increased data sharing and interconnectivity between banks and third parties pose challenges for data security and protection. This expanded access can lead to data breaches and a larger surface area for cyber attacks.

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