Volume 10 (2025-26)

This paper argues that the persistent gap between automation potential and automation adoption in banking is not the result of technological limitations but of inadequate discovery methodologies. Although banks have invested heavily in robotic process automation (RPA), intelligent document processing (IDP) and process mining technologies, most institutions remain confined to tactical, department‑level deployments that emphasise short‑term efficiency gains. Traditional discovery methods focus on operational metrics such as full‑time equivalent (FTE) reduction, cycle‑time improvement and error‑rate reduction, producing business cases that rarely resonate with executive decision makers responsible for strategic capital allocation. This paper demonstrates that scaling automation requires a shift toward strategic discovery centred on benefit–cost ratio (BCR) analysis, which integrates operational assessment with quantification of multi‑year strategic value, including regulatory resilience, competitive positioning, customer experience enhancement and workforce enablement. By embedding BCR analysis into the discovery phase rather than applying it only during evaluation, institutions can construct business cases that reflect the full scope of value delivered by modern automation initiatives. The paper outlines a practical framework for implementing BCR‑enhanced discovery and illustrates, through case evidence, how this approach strengthens executive sponsorship, accelerates adoption and enables financial institutions to evolve from isolated pilots to enterprise‑wide automation transformation.

Artificial intelligence (AI) is rapidly transforming digital banking, promising efficiency, personalisation and competitive advantage. Nevertheless, most AI strategy failures in financial institutions are rooted not in algorithmic shortcomings but in weaknesses at the intersection of data governance, data quality and AI lifecycle management. This paper introduces a diagnostic framework designed for senior banking executives to uncover hidden fault lines in AI strategies by examining their underlying data practices. Drawing from regulatory guidance, industry case studies and scholarly research, the framework offers actionable methods to detect, trace and mitigate risks across the AI value chain. By placing data governance at the center of AI risk management, digital banks can ensure compliance, protect customer trust, maximise return on AI investment and safeguard operational resilience.

Rarely do two transformational technologies in the financial sector emerge at the same time. Both Agentic artificial intelligence (Agentic AI) and stablecoin carry enormous potential to disrupt so much of the financial services value chain and extend their impact well beyond the sector into the broader economy, yet they also raise significant questions related to scalability and risk. This paper analyses the benefits, risks and potential future business models that might evolve from these two nascent technologies. Finally, the paper explores the convergence of Agentic AI and stablecoins, imagining the profound possibilities and challenges that emerge when autonomous intelligence is paired with stable, programmable money.

India has achieved unprecedented success in digital payments, largely propelled by the Unified Payments Interface (UPI) and the widespread adoption of quick response (QR) code-based merchant payments. This paper analyses the unique factors driving UPI’s growth — including its user-centric design, regulatory flexibility, zero merchant discount rate (MDR) policy and the proliferation of asset-light QR infrastructure. However, despite this remarkable progress, a crucial challenge persists: the lack of network-level interoperability in UPI QR codes. This structural limitation has led to systemic concentration, exposure to network outages and inconsistencies in merchant onboarding, all of which constrain further financial inclusion and undermine the resilience of the digital payments ecosystem. Drawing lessons from successful QR interoperability models such as Singapore Quick Response code, Malaysia’s DuitNow QR and Indonesia’s efforts to enhance the Quick Response Code Indonesian Standard, this paper argues that true scheme- and network-level interoperability is not merely a technical upgrade but a strategic necessity. It would expand consumer choice, enhance merchant acceptance while reducing costs, strengthen system-wide resilience and foster inclusive innovation. The paper also highlights the crucial role of banks in the acceptance ecosystem and emphasises that their continued engagement is essential for achieving economies of scale, sustainable revenue models and infrastructure resiliency. We propose a road map of important policy actions, including the establishment of a unified QR code standard, mandatory network-level interoperability, infrastructure modernisation, scheme-agnostic security frameworks and enhanced regulatory oversight. The paper also recommends that the Reserve Bank of India urgently reconsider the licensing of new umbrella entities (NUEs) to mitigate the overall concentration risk in India’s digital payment ecosystem. While acknowledging implementation challenges, the paper contends that India must act decisively to address existing gaps and evolve towards a more inclusive, competitive and future-ready digital payments architecture. These insights also hold important lessons for other countries building similar ecosystems, underscoring the importance of designing QR-based acceptance infrastructure with interoperability and public interest at the core.

To thrive in a commercial, technological and regulatory environment that is in constant flux, banks must be able to react at the speed of change. This paper argues that building an adaptive operational model depends on Process Intelligence (PI). Traditional, slow-moving approaches for business transformation are no longer sufficient in today’s volatile market. Four primary forces of change are reshaping the banking industry: the explosion of artificial intelligence (AI), rising customer expectations, the need for smarter operations as well as evolving risks and regulations. Adaptability empowers businesses to overcome these challenges and turn change into commercial advantage. The paper gives readers a solid understanding of what it means to be an ‘adaptive bank’ and how a process-centric operational model is the foundation for this agility. It introduces PI as the enabling technology for this transformation and explains how PI provides a ‘digital twin’ of a bank’s operations, offering real-time visibility into workflows and breaking down operational silos. Ultimately, the readers will learn how PI can be used to address each of the four major industry challenges directly, from fuelling AI with accurate data and context to enhancing customer experiences and ensuring compliance with shifting regulations. It concludes that becoming adaptive is a continuous process that delivers benefits far beyond operational agility, positioning banks to be ready for whatever the future holds.

This paper discusses the evolution of digital transformation in banking beyond the simple application of the latest technologies. It argues that long-term success requires an integrated strategy focused on people, technology and business implementation, balancing enhanced customer experience with operational efficiency and embedding the ‘human touch’ in digital architectures, while remaining inclusive across the customer adoption curve. The paper examines transformation building blocks (people and skills, technology infrastructure and business implementation); looks at typical pitfalls of partial archetype scenarios in contrast with an ideal, integrated approach; and integrates insight from recent industry reports. It further details four strategic transformation paths defined by the Massachusetts Institute of Technology Center for Information Systems Research with guidance on selection. The discussion is grounded in practical transformation experience, including the strategic application of artificial intelligence (AI). The paper finds that comprehensive digital transformation requires tackling cultural and organisational agility, in addition to needed technological upgrades and successful business implementation. It is essential t select the appropriate strategic pathway and thoughtfully integrate AI technology. Achieving a healthy balance between high-tech capability and high-touch, inclusive engagement continues to be a major challenge. The paper brings real world insights, condensed into top imperatives for banking professionals to drive their transformation initiatives, and points out the importance of balancing people, technology and implementation, as well as human-centred design, adaptive leadership and AI, for value creation.

The evolving financial services landscape is increasingly characterised by robust collaborations between financial institutions (FIs) and third party providers (TPPs), a trend driven by widespread industry recognition of their strategic value. While FinTechs have historically accelerated agility and innovation, the dynamic has evolved from adversarial competition to mutual collaboration, reflecting a shared understanding of reciprocal value between FinTechs and FIs. This strategic approach allows both parties to leverage the established scale, regulatory frameworks and trusted brands of incumbent FIs. Despite the benefits, a considerable number of business relationships often struggle to operationalise effectively. This is largely attributable to factors such as ineffective strategies, scalability challenges and organisational misalignment, thereby highlighting the crucial need for comprehensive preparation. This paper outlines the essential elements for successful, mutually beneficial partnerships, emphasising preparedness across all stages: planning, due diligence, contract negotiation and ongoing monitoring. It provides purported best practices and business intelligence on what effective preparedness and strategic planning should entail for both FIs and TPPs at each phase of the partnership lifecycle.

This paper delves into the development and potential of stablecoins and central bank digital currencies (CBDCs) within the Asia-Pacific (APAC) region. It begins by tracing the evolution of digital currencies, highlighting the emergence of Bitcoin and the subsequent proliferation of various digital assets. The focus then shifts to stablecoins and CBDCs, examining their shared characteristics, such as their reliance on distributed ledger technology (DLT) and their value being tied to fiat currencies or other stable assets. Despite these similarities, the paper underscores the significant differences between stablecoins and CBDCs, particularly in terms of their issuance, regulatory frameworks and use cases. The APAC region, characterised by its diverse and dynamic economies, is presented as a unique landscape for the adoption and innovation of digital currencies. The paper discusses the varying levels of progress across different
APAC countries, with some nations leading in the development of retail and wholesale CBDCs, whereas others are advancing in the use of stablecoins. The potential benefits of CBDCs, such as enhanced financial inclusion, improved monetary policy implementation and increased security, are contrasted with the advantages of stablecoins, including accessibility, innovation and privacy. The paper concludes by emphasising the importance of collaboration between the public and private sectors in the APAC region to foster a robust and inclusive digital currency ecosystem. It also highlights the challenges that remain, such as regulatory hurdles and the need to balance innovation with control. Ultimately, the paper posits that the developments in the APAC region will significantly influence the global trajectory of digital currencies.

Traditionally, financial institutions have been the trusted place to hold funds in a safe and sound environment where consumers and nonconsumers receive products and services to maintain their financial well-being. Financial technology (FinTech) companies have emerged as a major alternative to traditional banking for many consumers, according to an October 2023 report by McKinsey & Company. These FinTechs, however, still need to rely on traditional financial institutions to provide their path to the payment channels through the Federal Reserve System. These relationships with third parties offering products and services to consumers bring opportunities for large deposits but also expose them to risks that require strategic risk management programmes. This paper explores these third-party relationships, how they function, the evolving automated clearinghouse (ACH) risk management requirements from the National Automated Clearing House Association (Nacha), mitigation controls to reduce related risks and recommended measures for the industry to strengthen the financial system and reduce fraud losses.

Building a successful financial technology (FinTech) product is not an easy task. Experience shows that it requires a deep understanding of the market; a clear view of the challenges facing users (whether small to medium enterprises [SMEs], corporations or everyday consumers); a combination of great product thinking, strong sales execution and savvy marketing, as well as and a grasp of the fast-evolving technology landscape. Success, however, is not static. Market insight, customer discovery, tech evolution, marketing refinement and distribution improvements must be ongoing exercises. While many teams continue to focus on cutting-edge technologies (eg artificial intelligence [AI] and stablecoins), the ‘last mile’ of product design is often overlooked. That final stretch, where the user meets the interface and a decision is made (or not), is where behavioural psychology plays a crucial role. Understanding the common cognitive biases and behavioural principles that shape user decisions can markedly improve product outcomes, leading to higher conversion, lower default rates, better click-through rates, better savings behaviour, greater customer retention, higher revenue and, ultimately, a more successful product. This paper outlines important behavioural psychology principles (including Hick’s law, the endowment effect, default bias, Miller’s law, and the paradox of choice) and shows how they can be applied across digital banking, payments, savings, lending and investment products to improve overall outcomes and supercharge the success of FinTech products. Through real-world FinTech examples and practical guidance, the paper makes the case that behavioural design provides one of the most powerful tool kits for teams seeking to combat the invisible forces that affect consumer decisions. By better understanding these factors, teams can design products that move customers from intent (wanting to save more, invest more or manage finances) into action, generating improved outcomes for all stakeholders.

Leveraging artificial intelligence (AI) in daily banking processes to excite nonretail banking customers, support revenue generation, (semi-)automate fundamental banking processes and/or better protect a corporate and investment bank from risks connected to its nonretail banking business are the focus of this paper. This paper provides empirical examples from its authors’ practical implementation experience in the two largest Austria-based international banking groups. The paper discusses identifying and building the most promising AI use cases for nonretail banking, setting up the IT infrastructure required to successfully run AI use cases in a compliant way and preparing the organisation and its employees to deal with this new technology ethically and in compliance with regulations.

This paper seeks to combine the merits of low/no-code programming (LNCP) with traditional programming (TP) systems for increased agility in digital banking software development. While it is easy to fall prey to shiny object syndrome in today’s dynamic banking technology landscape, it is not easy to select the right technology to suit the current and future needs of the financial industry. Instead, LNCP makes it possible to lower the technical entry barriers to technology development. Integrating TP with LNCP, when needed, compensates for the shortcomings related to LNCP and provides digital banks with a more comprehensive software development approach. The adoption of this approach improves time-to-market of new innovative financial solutions. There has been little progress in this direction, academically or in practice. This paper includes an empirical study, interviews with banking professionals, on the merits of LNCP and TP, as well as an experimental project implementation that integrates LNCP and TP in the development of a retail Internet banking application. In the context of digital banking solution development, the result of the experiment reveals the merits of LNCP/TP hybrid systems in terms of agility, scalability, change management and cost-effectiveness.

This paper explores mBank’s groundbreaking initiative mOkazje Zakupy, which integrates a full-featured shopping platform directly into its mobile banking app. Launched in partnership with Morele.net, the platform represents a strategic shift in digital banking, transforming the app from a transactional tool into a lifestyle companion. By leveraging its trusted brand, rich customer data and advanced personalisation engine, mBank delivers a seamless, secure and highly contextual e-commerce experience. The initiative reflects broader trends in platform banking and embedded finance, positioning mBank as a leader in redefining customer engagement in the European financial sector. The paper highlights the technological, operational and strategic dimensions of the project, emphasising its scalability, regulatory compliance and potential to reshape the role of banks in the digital economy. Beyond its technical execution, mOkazje Zakupy reflects a broader strategic vision: to increase customer engagement, reduce churn and unlock new monetisation pathways. The initiative aligns
with global trends in embedded finance and platform banking, offering a European benchmark for how banks can evolve into digital ecosystems.

As artificial intelligence (AI) increasingly becomes a strategic foundation for modern organisations, the focus is shifting from experimentation to value realisation. This paper presents a practical framework for aligning generative AI (GenAI) strategy with business objectives to deliver measurable impact. The alignment process begins with defining a clear purpose statement and measurable key performance indicators that translate the AI vision into actionable outcomes across efficiency, customer experience, revenue growth, risk mitigation and innovation. The paper emphasises embedding ethical safeguards — such as regulatory compliance, transparency and bias mitigation — early in the strategy to ensure trustworthy and responsible AI deployment. The paper explores strategic decisions regarding build versus buy, and centralised versus decentralised innovation models in order to guide capability development and organisational design. Execution bridges strategy and operations through a hybrid approach combining traditional project management disciplines — such as structured governance, risk control and milestone-based tracking — with agile methodologies focused on flexibility, iterative development and continuous stakeholder feedback. This combination ensures disciplined delivery while allowing adaptive responses to rapidly changing AI opportunities and challenges. The paper highlights essential roles and enterprise-wide AI training as crucial enablers of successful adoption. Cultural transformation is positioned as crucial, with AI literacy and prompt engineering skills embedded across the workforce to empower innovation and support continuous improvement. The framework encourages the development of internal champions and success stories to foster sustained engagement and momentum. Finally, the paper provides actionable insights for banking leaders seeking to operationalise AI at scale, integrate it into the central business and establish AI as a transformative lever for long-term competitive advantage.

In February 2025, OpenAI announced ‘Operator’, an agent that includes a computerusing agent (CUA) model, which means that it does not need an application programming interface (API) to access services but can use buttons and navigate menus just as people do. Currently, an Operator requires human supervision to complete certain tasks, so a consumer needs to take control, for example, to enter payment information. However, with the advent of CUAs, the practical evolution of full-blown agentic commerce in strategic timeframes is well underway. Agents will go online to obtain services, look for an agent or an agentic API and then, if no such access methods are found, simply access the web pages as a human customer does. Agents will need more than API or web access to execute financial transactions on behalf of individuals or organisations; however, they will need authorisation. This means that agents need a fundamental property to deliver for their users: identity. This paper demonstrates that extending digital identity to agents is a fundamental enabler for bots to access financial services on behalf of individuals or organisations (or, indeed, themselves) to create a new financial world that can admit nonhuman customers to generate better outcomes for consumers and businesses. To deliver this vision of financial health for all, the paper posits that companies must first have a digital identity infrastructure that can provide identification, authentication and authorisation for not only financial institutions and their customers, but also for the customers’ agents. Know-your-customer (KYC) is necessary, as is know-your-business (KYB) and know-your-employee (KYE). But without know-your-agent (KYA), which is much more complicated than KYC, KYB and KYE, there will be no progress.

Central banks are increasingly leveraging the vast amount of information available in modern societies. Granular datasets offer precision and insight that aggregation often masks, providing flexibility to address a broad spectrum of issues and enabling in-depth understanding of emerging behaviours for more targeted policy responses. Nevertheless, two important challenges persist: these datasets often lack the rigorous production processes and quality assessments of traditional macroeconomic statistics, and their value depends on coherent contextualisation within a policy framework. Fortunately, data science offers effective tools to tackle such issues. This paper describes how, in future, maximising granular data’s potential requires stringent security measures, quality assurance and transparent integration into policy making.

This paper examines the use of innovative tools based on artificial intelligence (AI) for the assessment of environmental, social and governance (ESG) scores and the potential of alternative scoring to enhance sustainable investing. Despite numerous potential benefits, there is a lack of research on the application of AI algorithms in ESG analysis . This paper provides an in-depth statistical analysis on utilising AI tools for ESG scoring and compares their outcomes with conventional scoring methods. Based on variations in correlation coefficients over shorter versus longer time horizons for the same sample, the analysis suggests that AI-supported integration of traditional and alternative real-time signals could lead to more informed ESG assessment and decision making.

This paper explores how digital transformation in finance is driving focus on customer-centricity. The paper analyses how data-driven innovation is often framed as the quantitative collection and analysis of digital customer information. The paper explores how qualitative data can elucidate customer contexts and pain points. The research highlights how quantitative data can overlook important insights into customers’ decision making, their use of different financial solutions, and their ability to manage new technological solutions. The paper recommends financial service providers broaden their innovation toolbox by integrating qualitative research methods and by involving customers and partners in innovation practices.

This paper explores how generative AI (GenAI) has evolved from early pilot initiatives to becoming embedded in enterprise operations in the financial services sector. It identifies emerging high-impact use cases, operational maturity requirements and risk governance frameworks across banking, insurance and asset management. Drawing from industry reports, executive interviews and recent regulatory developments, the paper provides practical guidance for financial services industry professionals on how to scale GenAI responsibly and strategically. It also offers insights into value realisation, risk mitigation and future operating model considerations for financial institutions.

Against the backdrop of a pivotal moment for artificial intelligence (AI) and automation in the global economy (particularly within the banking sector) this paper aims to provide a strategic roadmap for banks to harness the diverse benefits of AI, with a strong emphasis on its application in the payments space. Following a contextual overview of AI’s recent evolution, the paper explores multiple real-world applications of AI within the banking sector and subsequently outlines the related benefits for both banks and their customers, particularly in how they interact and ultimately transact. As with any cuttingedge technology, the integration of AI poses numerous challenges and obstacles for banks to negotiate. This paper outlines strategies to overcome those hurdles, enabling banks to leverage AI and automation to streamline operations, optimise customer experience, drive
growth and position their businesses for long-term success.

The revolution in retail financial services comes not when banks are using artificial intelligence (AI) to provide services to customers but when customers use AI to assess offers from financial institutions. Those customers will have access to AI as powerful as the banks themselves have, because ‘Big Tech’ will give it to them. For most people, most of the time, in the not-too-distant future, their financial decisions, transactions and analysis will be performed by ‘bots’ operating under relevant duty of care legislation, with the coordinated goal of delivering financial health. The transition to banks serving these nonhuman customers presents a significant threat to retail bank profit pools. Look at the simple case of net interest margin. At a time of low interest rates, only one in 20 Americans refinanced their car loans, thus donating an average of US$3,500 per car owner to the banks’ bottom line. Overall, US banks obtained a US$1tn windfall from the Federal Reserve’s two-and-a-half-year era of high interest rates, income that will vanish in an age of bots working across the financial services sector and operating on behalf of consumers to improve their financial health. Institutions must, therefore, begin to think seriously about bank strategies in this new age. This paper builds on the authors’ previous work on customer bots by examining the consequences of giving intelligent agents access to consumers’ accounts in real time, at any time, in a world where the consumer is supported and enabled by open finance, smart wallets, digital identity and, hopefully, good regulation. It explores important strategic elements for retail banks looking to prosper in a new age in which every customer has their own personal treasurer in their pocket, always on and always ready to act on their behalf.

Legacy modernisation is high on the agenda of banks but notoriously difficult to do well. The operating model and business architecture can enhance productivity and clarity on which business leaders should feel responsible for modernisation. When the alignment of technology to business has not happened yet, a tech or ‘supply-led’ modernisation effort ensues. Through a case study, this paper aims to explore the right balance between technology-led modernisation and business-led modernisation, the results of when modernisation works, and what can be done to avoid potential pitfalls. Although this paper focuses on value stream-based organisations with ‘product model’ delivery, learnings can apply to functional organisations working with a ‘project model’ as well.

Based on research conducted in early 2024, this paper aims to provide frameworks, insights and approaches required for senior management and boards of scaling FinTech firms of various sizes and maturity levels, including guidance for navigating common challenges associated with profitable scaling. The authors contend that the significance and impact of effective boards in assisting FinTechs in their scaling efforts have been underestimated. Furthermore, many FinTech board members require education on the similarities and differences between FinTech boards and those of incumbent financial services firms. They have constructed a holistic framework against which boards can measure their progress and address the questions that need to be posed at various stages of the growth lifecycle. The authors argue that maintaining focus on customer experience and board diversity is a fundamental enabler of successful growth, with due consideration of regulatory, financial and other risks.

Globally, 1.4 billion adults remain unbanked, with many more inadequately served by existing financial services. Technological advancements are transforming the accessibility and affordability of financial products, enabling the viability of high-volume, low-value financial products. The demand for access to a greater number of services and more personalised products and services is growing, placing the industry in a position where new ways of working are required to meet these needs. To address the needs of all consumers, especially those who are unserved or underserved, a robust and collaborative ecosystem is essential. Such an ecosystem fosters innovation across both supply and demand while also enhancing essential enablers such as regulations, policies, infrastructure and technology. Participation in this ecosystem requires significant changes to organisational culture, technology, governance and business models. This paper concludes that effective change management is crucial for organisations to successfully navigate and derive value from these evolving ecosystems.

Real-world assets (RWAs), including bonds, real estate and commodities, are tangible assets with economic value that contribute to the financial ecosystem. This paper will explore how RWAs are being integrated into novel digital banking platforms, focusing on the potential of blockchain technology, tokenisation and smart contracts. It aims to provide an in-depth look at two important areas — corporate banking and investments — while addressing current challenges, showcasing practical applications and illustrating how RWAs can help make financial services more inclusive. By lowering traditional barriers to entry, RWAs can potentially make investments more accessible to a wider audience, thereby creating opportunities for both individuals and financial institutions (FIs) to expand their assets under management (AUM) and financial service offerings. This paper seeks to offer actionable insights for digital banking professionals, encouraging further exploration and innovation in the RWA space.

As major players in the global economy, banks are uniquely positioned to facilitate the transition to net zero. However, achieving meaningful impact requires overcoming complex challenges, including data integration, customer engagement and regulatory compliance. This paper explores how partnerships with green FinTechs can support banks to overcome these obstacles, mitigate risks and unlock new opportunities for innovation and growth, ultimately enabling decarbonisation and securing long-term success.

The banking sector stands at a pivotal moment in its journey of digital transformation, propelled by rising customer expectations for seamless, personalised experiences and rapid advances in artificial intelligence, blockchain and cloud technology. This paper explores how traditional banks can successfully navigate digital transformation to stay competitive in an era where agility, innovation and customer centricity are essential. Through real-world examples, it examines strategies like agile development, phased modernisation of legacy systems and fostering of a digital-first culture. The paper also addresses pressing challenges — from outdated infrastructure to cybersecurity risks — highlighting the need for a strategic, incremental approach to overcoming these barriers. As banks embrace digital solutions and forge partnerships with FinTechs, they pave the way for a future defined by autonomous financial agents, open finance and secure, real-time transactions. In an increasingly digital world, those who prioritise transformation and customer relevance will shape the future of banking.