Volume 11 (2026-27)

Artificial intelligence (AI) is rapidly becoming embedded within the operating models of financial institutions, reshaping activities such as credit decisioning, fraud detection and customer engagement. This increased adoption is accompanied by heightened regulatory scrutiny and growing expectations around governance, accountability and risk management. This paper presents a leadership-focused perspective on managing AI as a regulated life cycle rather than a standalone technology initiative. The study argues that organisations must align AI strategy with risk appetite and regulatory obligations, supported by clear governance frameworks that integrate business, risk, compliance and technology functions. The paper outlines a structured life-cycle approach, from strategic definition through to development, implementation, monitoring and audit, highlighting the importance of embedding controls at each stage. It emphasises crucial factors, including data governance, model risk management, third party oversight and continuous performance monitoring. Drawing on current regulatory developments and industry practice, the paper illustrates how organisations can operationalise responsible AI while maintaining innovation momentum. Practical guidance is provided for senior leaders seeking to balance speed of innovation with regulatory discipline. The paper concludes that organisations adopting a life cycle–based, risk-led approach to AI governance will be better positioned to innovate sustainably while meeting evolving regulatory expectations. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: artificial intelligence governance; AI risk management; model risk management; financial services compliance; third party AI risk; responsible AI governance

The contemporary banking relationship has fundamentally shifted from physical convenience and preferential rates to being determined by the quality of digital experience delivery. Traditional financial institutions face unprecedented competitive pressure from agile FinTech challengers and platform-based entrants who set new standards for customer expectations. This paper argues that sustainable customer loyalty in senior financial institutions requires a dual-layer strategy: (i) establishing a foundational architecture of engineered seamlessness that eliminates digital friction; (ii) deploying a differentiation layer of hyper-personalisation built upon ethical, real-time data orchestration. Beyond introductory discourse, this paper provides senior digital banking professionals with actionable guidance on essential architectural transformations (microservices adoption and real-time data fabrics), implementation of digital-native metrics (Digital Engagement Score, Customer Effort Score and Digital Containment Rate) and the organisational realignment necessary to execute a relationship-centric digital mandate. Drawing on recent industry research, analyst insights and empirical evidence, this framework equips banking executives to transform digital experience from a cost centre into a strategic driver of customer lifetime value and competitive differentiation. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: digital banking experience; customer loyalty; hyper-personalisation; seamless omnichannel banking; AI infrastructure; customer effort score

The paper explores the implications of financial tokenisation and the rise of stablecoins for Europe’s monetary sovereignty and financial stability. Distributed ledger technologies are driving a structural transformation in financial markets by enabling tokenised transactions that promise efficiency gains, enhanced automation and cost reductions. However, the rapid growth of stablecoins — predominantly US dollar– denominated and issued by nonbank entities — poses risks for monetary sovereignty and financial stability, as well as rising interconnections with public debt markets, which could affect monetary policy in the future. These include potential fragmentation of liquidity, increased counterparty risk, disruptions in monetary policy transmission and disintermediation of banks. Moreover, the expansion of stablecoins could challenge the two-tier monetary system that underpins trust in money and economic growth. Against this backdrop, the European Union (EU) and its system of Central Banks — the Eurosystem — face a dual imperative: to safeguard the quality of money and capacity to finance the European economy thanks to the two-tier monetary system, and to preserve European sovereignty in a multipolar world. The introduction of a wholesale central bank digital currency, a clear and robust regulatory framework and initiatives to develop tokenised commercial bank money are central to this strategy. The paper examines these issues through three lenses: Eurosystem actions, the current regulatory environment and European private-sector initiatives. This paper is also included in The Business & Management Collection, which can be accessed at https://hstalks.com/business/.
Keywords: money; tokenisation; stablecoin; sovereignty; finance; euro

Traditional credit underwriting models rely heavily on bureau-based scores, limiting access to credit for thin-file and credit-invisible consumers. This paper focuses on a large and growing segment described as ‘Emerging Achievers’, including young adults, migrants and gig workers who demonstrate responsible financial behaviour through banking activity but remain underserved by conventional models. It argues for a shift from score-led approaches to a relationship-based underwriting framework that incorporates banking relationships and transactional data alongside traditional measures. The paper explains how open banking and emerging open finance ecosystems enable the use of permissioned financial data, including income patterns, spending behaviour and nontraditional payment histories, to provide a more complete and explainable view of consumer risk. It outlines a framework that integrates relationship-based segmentation, transactional underwriting and embedded fraud detection, supporting more accurate, transparent and inclusive credit decisions while maintaining regulatory alignment. Case evidence indicates improved decisioning performance and expanded access without compromising risk appetite. The paper concludes that adopting a relationship-driven, data-led approach offers lenders a scalable pathway to serve underserved segments while balancing inclusion, explainability and fraud resilience. It also highlights implementation considerations — including data governance, model transparency, customer consent and operational integration — as essential enablers of sustainable deployment at scale. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: FinTech; emerging achievers; open banking; relationship risk underwriting; transactional underwriting; thin-file; inclusive credit; fraud management

Financial supervisory and regulatory organisations increasingly operate under conditions characterised by heightened uncertainty, a higher incidence of large systemic disruptions, and rapid socioeconomic transformation. In this context, horizon scanning has emerged as an institutional strategic intelligence practice for systematically detecting early signals as indicators of potential change. Despite its growing adoption, horizon scanning remains largely descriptive, episodic and weakly operationalised, limiting its standardisation and effectiveness for continuous monitoring and anticipatory decision-making in high-stakes environments such as finance. In this paper, we advance horizon scanning from a predominantly conceptual and qualitative episodic practice, towards a resilient, computationally grounded, and institutionally governable capability designed with, but not limited to, a suitability for financial supervisory and regulatory use in mind. We inform a design and engineering blueprint for a supervisory and regulatory grade horizon scanning computational system based on modular decomposition of cross-domain drivers of change and their recombination into structured, semantically plausible yet novelty enabling future scenarios. The proposed framework leverages agentic, generative artificial intelligence (AI) to support continuous horizon mapping, exploratory scenario generation, and policy-oriented evaluation, while embedding human-in-the-loop oversight, auditability, and ethical governance mechanisms. The paper introduces agentic generation and evaluation of modular integrated scenarios as a concrete instantiation of this design logic and discusses its functional value for central banks and financial supervisory and regulatory organisations. The paper concludes that modular, agentic horizon scanning has the potential to enhance institutional anticipatory capacity and support resilient policy design under uncertainty, while expert human-in-the-loop provides a robust governance foundation for the responsible use of AI in strategic foresight. This paper is also included in The Business & Management Collection, which can be accessed at https://hstalks .com/business/.
Keywords: horizon scanning; agentic AI; financial resilience; modularity; systemic risk

This paper examines Latvia’s FinTech ecosystem as a case of small-market adaptation to technology-driven change in the financial sector. It develops and applies an Ecosystem Effectiveness Model that connects enabling capacities (technological readiness, institutional collaboration, regulatory environment and market adoption) with outcome dimensions of competitiveness, profitability, innovation and resilience. The study draws on a bibliometric mapping of FinTech research and on micro-level evidence from the 2025 Bank of Latvia Innovation Survey, using principal component analysis, cluster analysis and logistic regression to profile ecosystem actors and their innovation behaviour. The results demonstrate that Latvia has achieved relatively high Application Programming Interface penetration, growing use of artificial intelligence (AI) in compliance and analytics, and structured FinTech– bank cooperation, supported by innovation-oriented regulation and access to payment infrastructure. At the same time, gaps in deep-tech scaling, venture capital availability, talent and international promotion could limit improvements in ecosystem performance. By situating Latvia’s experience within broader European FinTech dynamics, the paper demonstrates how small economies can design policy and governance arrangements to convert ‘secondgeneration disruptors’ (eg AI, tokenisation) into focused, resilient specialisation paths rather than scale-based competition. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: FinTech ecosystem; ecosystem effectiveness; small open economy; regulatory testbed; Latvia; financial innovation policy

In the fast-paced world of digital banking, the ability to modernise legacy systems while preserving operational resilience is essential. Financial institutions across the United Kingdom and Europe are increasingly confronted with ageing technology that, while historically reliable, now poses challenges in integration, cybersecurity and agility. This paper examines the intricacies of legacy system modernisation, including when to upgrade or retain legacy components, and strategies for achieving a balanced, risk-based transformation. Drawing on examples from major institutions and recent regulatory guidance, it offers practical insights for decision makers committed to technological renewal without compromising stability. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: legacy system modernisation; operational resilience; digital transformation; technical debt management; financial services technology

The value chain within the banking and payments sector of the European Union (EU) is undergoing a profound transformation, driven by digitalisation, the proliferation of innovative platforms and the emergence of new business models. As a result, financial institutions are increasingly turning to third parties for the marketing and distribution of financial services, leading to greater fragmentation and specialisation in the sector. Among these developments, white labelling has emerged as a widespread business model, allowing regulated firms to distribute products and services under the brand of (often) nonfinancial partners. According to recent data from the European Banking Authority, over a third of European banks utilise white labelling arrangements. White labelling presents numerous advantages, including cost reduction, expanded service offerings and enhanced financial inclusion, thereby fostering competition within the industry. The model, however, introduces new complexities, increasing operational and compliance risks and raising potential challenges for consumer protection and proper supervision. In particular, supervisory authorities face the challenge of overseeing these increasingly fragmented value chains and ensuring effective regulatory convergence across EU member states. The traditional boundaries between regulated institutions, technology firms and nonfinancial entities are dissolving, forcing supervisors to adapt their practices. This paper explores how these trends are fragmenting traditional value chains, enabling both incumbent financial institutions and nonregulated entities, including FinTechs and BigTechs, to enter the market and offer financial services and products in the EU. The paper focuses in particular on white labelling: it presents use cases, benefits and risks and analyses the resulting supervisory challenges. This article is also included in ‘The Business & Management Collection’, which can be accessed at https://hstalks.com/business/.
Keywords: innovation; white labelling; digital finance; payments

This paper explores the transformative potential of agentic artificial intelligence (AI) — autonomous, goal-driven artificial intelligence systems — within the banking sector. Building on the widespread adoption of generative AI (GenAI) as a standard tool for content creation and summarisation, the paper examines how agentic AI represents the next evolutionary step: moving from reactive assistance to proactive, autonomous execution of complex workflows. It outlines the crucial differences between GenAI and agentic AI, emphasising the latter’s ability to plan, coordinate and make decisions with minimal human input. The paper provides a comprehensive overview of agentic AI’s capabilities, its autonomy spectrum and its growing adoption across industries, with banking, financial services and insurance leading the way. Practical examples illustrate how agentic AI can transform processes such as loan approvals, fraud detection and compliance checks, while multi-agent systems enable collaborative automation of intricate tasks. The discussion also addresses crucial prerequisites for successful implementation, including workforce readiness, governance frameworks, technology architecture and robust data foundations. Readers will gain insights into the strategic implications of agentic AI, learn how to identify suitable use cases and understand the phased approach required for deployment in highly regulated environments. By the end of the paper, decision makers will be equipped with knowledge on aligning agentic AI with business objectives, mitigating risks and leveraging this technology to enhance operational agility, scalability and resilience — ultimately positioning their institutions for long-term competitiveness in an increasingly digital landscape. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: agentic AI; generative AI; autonomous AI systems; banking digital transformation; AI governance and regulation

The global promise of digital banking lies in the industry’s potential to combine efficiency, scale and inclusion. Yet, the reality in many emerging markets is more complex. Purely digital platforms often struggle to gain traction in cash-heavy economies where trust is limited and digital literacy uneven. At the same time, traditional branch–based banking remains too costly to extend to low-income and/or low-density communities, like peri-urban and rural areas, without state support or direct government ownership of the financial institution. Tyme, through its TymeBank in South Africa, pioneered a novel ‘phygital’ approach that bridges these extremes and then took this model to the Philippines for its GoTyme Bank. Tyme’s network of physical-meets-digital kiosks, brightly coloured and mobile, resembling ATM machines, embedded in high-footfall retail outlets — initially grocery stores and then fashion outlets — allows customers to open fully functional accounts in minutes, receive debit cards instantly and transact within familiar, trusted environments. This paper examines the kiosk model in depth, exploring its technological design, operational model, impact measured by gender, challenges and replicability. It posits that the kiosk is more than a distribution tool; it is a social and technological innovation that has redefined what digital banking means in contexts of embedded exclusion. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: banking; digital; financial inclusion; FinTech

This paper examines the role of artificial intelligence (AI) in modernising enterprise workflows within legacy-dependent banking environments. It identifies structural limitations in traditional banking systems, including siloed architectures, limited interoperability and reliance on manual processes, which constrain efficiency, scalability and responsiveness. To address these challenges, the paper proposes an AI-driven integration framework that combines machine learning, natural language processing and robotic process automation within a modular, middleware-based architecture. The framework enables intelligent data extraction, automated decision making and real-time workflow orchestration across legacy and modern systems. It introduces AI-enabled middleware connectors and agent-based orchestration to support seamless interoperability while preserving existing infrastructure investments. The study evaluates the framework across digital lending and accountopening processes, demonstrating substantial improvements in operational performance, including reduced processing times, decreased manual intervention and enhanced compliance accuracy. The findings highlight the potential of AI to transform enterprise workflows by enabling dynamic, data-driven operations and improving risk detection and regulatory adherence. The paper also addresses implementation challenges, including data governance, model interpretability and integration risks, emphasising the need for robust architectural design and explainable AI. Overall, it provides a practical, scalable approach to achieving digital transformation in complex banking ecosystems and improving longterm operational resilience. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: AI-driven workflow automation; legacy system integration; banking modernisation; robotic process automation (RPA); natural language processing (NLP); mainframe integration; enterprise architecture; digital transformation; compliance automation; financial services innovation

This paper examines the digitalisation of the Thai capital market and outlines the strategy of the Securities and Exchange Commission Thailand to support this transition. It identifies persistent inefficiencies, including fragmented data, manual processes, high costs and inconsistent standards, which limit efficiency and transparency. The paper presents the digital securities ecosystem, designed to enable end-to-end digital processes across issuance, trading, settlement and custody. It discusses the application of distributed ledger technology, block chain and smart contracts to improve data sharing, automation and real-time settlement.1 Supporting measures include legal reform to enable digital securities, regulatory sandboxes for innovation testing,2 and enhanced supervision using data-driven tools. The paper emphasises investor readiness, governance and standardisation as crucial to adoption and trust. It argues that digitalisation can enhance liquidity, reduce costs, increase transparency and support financial inclusion. Overall, it provides a policy and implementation perspective on enabling digital transformation while maintaining market integrity and investor protection, and offers practical insights into aligning innovation with regulatory objectives effectively. It also notes the importance of interoperability, data standards and cross-sector collaboration in delivering scalable outcomes, as well as highlights initiatives and pilot projects that demonstrate practical use cases in the Thai capital markets. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: capital market; digitalisation; digital securities; digital securities ecosystem; technology

Many economies have been leveraging real-time electronic payment systems to drive financial innovation and inclusion. This paper assesses whether data from Sistema De Pagos Electronicos Interbancarios (SPEI), an interbank electronic funds transfer system owned and operated by Banco de Mexico, can improve the timeliness and accuracy of Mexico’s gross domestic product (GDP) forecasts by acting as a complementary indicator of economic activity. In addition to being almost immediately available to the Central Bank, SPEI data is extremely comprehensive and about 168 times larger than credit and debit card transactions. The paper presents a cointegrated forecasting model using SPEI as the sole predictor of GDP. In the next stage, they build an auto regressive integrated moving average (ARIMA) model using the residuals of the cointegrated model. Compared to much more sophisticated, data-intensive models, the paper shows that using a complementary indicator like SPEI can be a parsimonious forecasting alternative, especially during highly volatile periods when it can mitigate information gaps. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/ business/.
Keywords: nowcasting; forecasting; Mexico; GDP; ARIMA; error modelling; electronic payment systems; Sistema De Pagos Electronicos Interbancarios; SPEI; complementary indicator