Volume 19 (2026)

The rise of stablecoins, tokenised bank deposits, and central bank digital currencies (CBDCs) is reshaping the mechanics of settlement in modern financial systems. While debate has focused on technological innovation — programmability, atomic execution, and 24/7 availability — the risk implications of tokenised money depend less on token design than on the settlement architecture that governs it. This paper argues that real-time gross settlement (RTGS) systems provide the appropriate benchmark for evaluating tokenised arrangements from a risk lens, because tokenised transfers operate on a gross, transaction-by-transaction basis and therefore inherit RTGS-level expectations of legal finality, prefunded liquidity, and accountable governance. The paper develops a Principles for Financial Market Infrastructures (PFMI)-aligned assessment framework organised across three dimensions: settlement finality and asset quality; liquidity and credit risk under programmable atomic settlement; and governance and interoperability. Applying the Committee on Payments and Market Infrastructures/International Organization of Securities Commissions (CPMI-IOSCO) observance methodology across stablecoins, tokenised deposits, and CBDCs, the analysis identifies material gaps in statutory insolvency protection, default management, cross-border legal recognition, and system-wide governance in many emerging arrangements. It shows that programmable atomic settlement compresses the temporal buffers that traditional infrastructures use to manage risk, requiring new institutional mechanisms calibrated for continuous gross execution. The central finding is that digital monetary coexistence is sustainable only when settlement systems ensure finality, reliable value at par, and real-time risk control across all instruments. Tokenisation does not eliminate settlement risk; it relocates trust into legal design, liquidity architecture, and governance accountability. Settlement architecture, which is defined by institutional arrangements, determines whether settlement, liquidity, and systemic risks are contained or amplified at scale. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: tokenised money; stablecoins; tokenised deposits; central bank digital currency; CBDC; settlement and liquidity risk; financial market infrastructure; governance and interoperability

This paper presents a practical framework for business environment analysis and horizon scanning specifically tailored to the operational requirements of risk management and strategy departments within financial institutions. While it briefly acknowledges that traditional models based on historical data encounter limitations when facing structural change, the primary objective is to provide a structured three-phase process based on established foresight methodologies. To facilitate immediate use by risk practitioners who may not have extensive prior experience in foresight science, the paper introduces a clear ontology of future phenomena which avoids excessive theoretical complexity while ensuring conceptual precision. In this context, the paper delineates the vital distinction between horizon scanning and traditional materiality assessments, identifying the former as a critical upstream input that informs the latter by challenging existing system boundaries. Special emphasis is placed on the methodological foundations of the assessment phase, where structured elicitation protocols are utilised when expert judgement is needed. These protocols are designed to minimise cognitive biases and ensure that expert opinions are statistically reliable. The paper concludes by illustrating the operational integration of qualitative foresight intelligence into established banking procedures, including monitoring, stress testing, and strategic planning. In doing so, horizon scanning moves beyond a mere compliance activity, positioning it as a valuable tool for enhancing long-term strategic resilience and risk management practice. The paper also provides illustrative examples that help to turn horizon scanning into a practical tool, advancing it beyond compliance to support strategic resilience and risk management. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: horizon scanning; business environment analysis; climate risk; polycrisis; weak signals; emerging issues; strategic foresight; expert elicitation

Artificial intelligence (AI) is rapidly reshaping the UK insurance sector, offering significant opportunities for efficiency, innovation, and improved customer outcomes. At the same time, AI introduces complex and fast-moving risks relating to model opacity, bias, data protection, consumer fairness, operational resilience, and third party dependency. This paper argues that AI does not constitute a wholly new category of risk for insurers; rather, it acts as a powerful amplifier of existing enterprise risks, increasing their speed, scale, connectedness, and potential impact. Drawing on regulatory guidance, industry practice, and emerging AI risk frameworks, the paper examines divergent stakeholder perspectives on AI adoption, including those of insurers, regulators, and consumers. It highlights a growing consensus that organisations must act now to integrate AI risk considerations into established enterprise risk management (ERM) frameworks, rather than relying on parallel or siloed AI governance structures. The paper proposes a principles-based practical approach for embedding AI risk within existing taxonomies, registers, and control environments, with particular emphasis on identifying where AI amplified prudential, conduct, data protection, resilience, reputational, and third party risks. Particular attention is given to the role of data protection impact assessments (DPIAs) as a critical governance mechanism for identifying and managing AI-amplified risks, aligned with UK regulatory expectations and international developments such as the National Institute of Standards and Technology (NIST) AI Risk Management Framework (RMF) and the forthcoming European Union (EU) Artificial Intelligence Act (AI Act). The paper concludes that insurers which proactively integrate AI governance into core ERM processes, supported by cross-functional collaboration between risk, compliance, data protection, and technology teams, will be best positioned to realise AI’s benefits while maintaining consumer trust, regulatory compliance, and resilience. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: artificial intelligence risk; enterprise risk management; insurance risk governance; algorithmic accountability; data protection and AI; operational resilience; responsible AI

This paper presents the results of a qualitative study based on in-depth interviews with certified risk management experts — Financial Risk Manager (FRM), Professional Risk Manager (PRM), and Credit and Counterparty Risk Manager (CCRM) from Poland. The findings reveal that despite an increased focus on machine learning (ML) methods in research, classic statistical methods, such as discriminant analysis and logistic regression, remain in use. The reason lies in the structural barriers that inhibit the adoption of ML models in bankruptcy assessment. Key limitations include the lack of transparency and difficulty in explaining and justifying model decisions to regulators and courts (‘black box’ effect1), insufficient availability of high-quality data, and complex implementation requirements involving IT systems,2 supervision processes, and governance frameworks. Experts also report limited trust in algorithmic decision making and perceive ML as a technology often driven by marketing narratives rather than demonstrated added value in insolvency prediction. This study offers a critical insight into the predictive superiority, which, when considered independently, is insufficient for adoption. The effectiveness of ML in financial risk assessment depends on its alignment with regulatory expectations for explainability, interpretability, legal accountability, and organisational trust. By incorporating certified expert perspectives, this paper contributes a missing dimension, outlining the conditions under which ML can be responsibly and realistically applied to advance bankruptcy risk management in Poland. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: bankruptcy risk assessment; machine learning in finance; expert insights; expert interviews; credit risk

This paper develops a grounded theory explanation of why financial institutions adopt enterprise risk management (ERM) and what makes ERM effective once implemented, using interview evidence from chief risk officers and senior risk leaders and analysing the data through constructivist grounded theory procedures and systematic coding from interview segments to categories and themes. The adoption framework indicates that ERM adoption is triggered by a combination of internal push factors and external pull forces that together create the impetus to formalise enterprise-wide risk practices. The effectiveness framework comprises four interdependent themes — shared norms, risk information capacity, risk management leadership imperative, and engagement at the top — which together explain how ERM moves beyond a formal programme to influence decisions, risk-taking behaviour, and organisational outcomes. The resulting frameworks provide a practice-oriented account of ERM as a socio-organisational system, offering boards and risk leaders a coherent set of levers and diagnostic questions to strengthen ERM adoption pathways and improve the conditions under which ERM delivers sustained effectiveness. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: enterprise risk management; financial institutions; chief risk officer; grounded theory

The benchmarking of current expected credit loss (CECL) estimates presents a persistent challenge for financial institutions, regulators, and auditors alike: one that is particularly acute for smaller institutions whose limited modelling capabilities place them at a material disadvantage relative to the multi-variable approaches employed by larger banks. Models involving extensive variables can be complex and difficult to validate, and a transparent, replicable benchmark would provide the institutions, supervisors, and auditors with a reliable instrument for detecting over or under-provisioning. In order to address this problem, this paper proposes and validates a credit loss elasticity model grounded in a Cobb-Douglas specification, in which current credit losses are modelled as a function of outstanding loan balances raised to an estimated elasticity coefficient. A log-linear regression framework is applied to a panel dataset of US banks with assets exceeding US$300m, with robustness tests conducted across bank size, charter type, geographic region, and time period, and with macroeconomic variables — gross domestic product growth, unemployment rate, and real estate index — incorporated to assess the model’s stability under varying conditions. The central finding of this analysis is that loan loss elasticity is stable over time, including during the inaugural year of CECL implementation in 2023, and that the model performs consistently across bank segments and timeframes. This stability yields several important implications. The elasticity model introduces a novel benchmarking instrument that is accessible to institutions with limited modelling resources, equipping auditors and regulators with a practical means of identifying deviations from normative provisioning behaviour. The model serves solely as a benchmarking tool; it cannot substitute for a compliant CECL or ECL estimation model, as it does not capture the portfolio-specific risk characteristics required by regulatory standards. By providing a transparent, replicable external reference point against which institutional CECL and ECL estimates may be evaluated, the model supports the broader goals of financial system transparency and comparability. There are, however, acknowledged limitations: the model does not differentiate between loan vintages or credit quality, and negative credit loss entries are excluded owing to concerns about accrual manipulation. The implications of this exclusion for longitudinal benchmarking across credit cycles are discussed in the limitations section. This paper concludes that no single modelling approach can resolve the full complexity of CECL estimation, but that the elasticity model offers institutions a transparent, replicable, and theoretically grounded tool that meaningfully improves comparability across institutions; and that its simplicity is, in this context, a feature rather than a limitation. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: ASC 326; CECL; CVA; credit risk; elasticity; loan loss provisioning; financial reporting

A forward-looking framework is proposed to assess financial risks arising from climate policy uncertainty in equity markets. In contrast to traditional stress-testing methods based on fixed scenario paths, transition risk is modelled as a sequence of stochastic carbon price shocks, the frequency of which evolves over time, through a Bayesian learning process reflecting the revision of investor beliefs. This approach captures the dynamic nature of regulatory uncertainty and its impact on asset valuations. To evaluate financial vulnerability, a novel metric — the market compensation probability — is introduced. This metric quantifies the likelihood that the underlying dynamics of financial market prices offset climate-induced financial losses. The framework is empirically applied to the Moroccan equity market, an emerging market context characterised by significant limitations in the availability of relevant climate-related financial data. Results indicate that the overall market exhibits moderate exposure due to its relatively low average carbon intensity, while the electricity sector, which is characterised by high emissions, faces persistent financial erosion. These findings highlight the importance of integrating climate policy uncertainty into financial risk assessments. By identifying sectors most at risk and quantifying downside exposures, the model offers valuable guidance for risk managers, institutional investors, and regulators. It supports the development of climate-aware investment strategies and promotes capital reallocation towards lower-carbon assets, thereby enhancing portfolio resilience and advancing climate transition goals. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: climate change; transition risk; policy uncertainty; equity markets; Bayesian learning; emerging markets; sustainable finance

This opinion piece examines the regulatory divergence in the European sustainability agenda as of late 2025 and its implications for environmental, social, and governance (ESG) risk management in the banking sector. While the European Union’s Omnibus package reduces the administrative burden on the real economy, the new European Banking Authority (EBA) guidelines mandate a significant intensification of risk oversight for the financial sector. The paper analyses the ‘horizon mismatch’ between short-term capital planning and long-term climate and environmental risks, evaluating the role of new instruments such as CRD-based transition plans and resilience analysis. This demonstrates that the EBA’s move towards forward-looking methodologies represents a continued shift away from approaches that rely solely on retrospective data. The analysis highlights that the EBA already acknowledges the necessity of expert judgment and institutional discretion, particularly by allowing institutions to define their own most likely scenario as a reference within resilience analysis, even as it avoids the explicit term ‘subjectivity’ in the regulatory vocabulary. The author concludes that effective ESG risk management must transcend the mere expansion of data lakes. Instead, it requires a robust framework of epistemic governance, a system designed to manage the generation, validation, and communication of knowledge under deep uncertainty, effectively transforming ‘subjectivity’ into a structured and transparent institutional process. Rather than relying on closing data gaps through technical granularity alone, the paper emphasises the need for a risk culture rooted in intellectual humility and the systematic integration of expert-based insights to ensure the long-term resilience of the financial system. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: ESG risk management; EBA guidelines; epistemic governance; deep uncertainty; resilience analysis; expert judgment; subjectivity

Political instability and institutional fragility are increasingly recognised as systemic risk factors that undermine financial resilience in emerging markets and developing economies (EMDEs). This study examines how political and institutional conditions influence key dimensions of macro-financial vulnerability such as banking sector soundness, capital flow dynamics, and external balances, using a balanced sample of 36 EMDEs across six major regions over the period 2002–2023. Employing descriptive analysis, correlation matrices, and fixed-effects panel regressions with robust country-clustered standard errors, the study documents that weaker political stability and governance quality are systematically associated with elevated banking stress, constrained credit intermediation, heightened sensitivity of portfolio equity flows, and deteriorating external balances. Portfolio equity inflows respond more sharply to political shocks than foreign direct investment, and external debt accumulation and current account imbalances increase in politically fragile environments. Regional comparisons reveal substantial heterogeneity, with Sub-Saharan Africa and the Middle East and North Africa demonstrating the greatest vulnerability profiles, while East Asia and the Pacific exhibit stronger institutional buffers. These findings highlight the importance of incorporating political and institutional risk indicators into macro-prudential surveillance, stress testing, and capital flow risk management frameworks. The results inform policy makers, regulators, and risk managers on the channels through which governance fragility translates into financial instability, supporting enhanced banking resilience amid geopolitical and institutional uncertainty. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: political instability; institutional fragility; financial vulnerability; capital flows; external balances; emerging markets and developing economies; financial resilience

In an era of escalating geopolitical tensions, it is critical for governments to prevent or tightly control foreign influence and interference in the industries that are strategically vital to a country’s sovereignty, stability, and resilience. Financial services and banking is at or near the top of these vital industries where geopolitical risk significantly increases bank systemic risk. There are new guidelines and regulations in place to prevent foreign influence and interference from a national security perspective. Government intervention through the use of ministerial and national security tools (divest orders, enhanced security conditions) is relatively rare. When a government intervenes in a board’s governance, it introduces a unique dynamic where the board must balance its traditional fiduciary duties with specific government objectives, significantly affecting the board’s independence, accountability, and decision-making processes. It may also put the shareholders’ interests at odds with the financial institution. These topics are discussed in this paper. It presents a case study of government intervention due to national security concerns, relying on publicly available information. The discussion is then extended to the board governance challenges and application of board governance principles in this peculiar situation. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: geopolitical risk; banking board governance; foreign ownership; foreign interference; national security; integrity and security; government intervention

This paper examines how the global banking sector preserves resilience under a dual technological shock: the criminal use of generative artificial intelligence (GenAI) and agentic AI1 in money laundering, fraud, and sanctions evasion, and the slow deployment of defensive AI/machine learning (ML) in anti-money laundering (AML)/anti-financial crime (AFC) programmes. It tests the hypothesis of a systemic ‘risk-adoption gap’ between the speed of threat evolution and the industrialisation of defensive AI. A qualitative triangulation integrates (1) the Association of Certified Anti-Money Laundering Specialists (ACAMS) Global AFC Threats Report 2025;2 (2) quantitative evidence from ‘Global AML Research: The Road to Integration’ (SAS‒ACAMS‒KPMG, 2024); (3) Financial Action Task Force standards on digital transformation and virtual assets; and (4) Europol and Cybercrime Trends 2025 analyses on AI-enabled cybercrime. Cross-source patterns inform policy implications. The study finds that only 18 per cent of institutions have fully operational AI/ML in AML/counter-terrorism financing, while 40 per cent lack any adoption plan, despite AI-driven threats rated high/very high. Legacy IT and data constraints and a talent gap in data/ML/explainable AI (XAI) are the main internal barriers. Asia combines high AI use with fragile infrastructures, whereas the US/Oceania show delays linked to regulation and core-system complexity. Offensively, GenAI amplifies authorised push payment and document fraud, synthetic identities and multi-channel phishing, eroding rule-based controls. Non-homogeneous data sources limit statistical generalisation but enable framing AFC 5.0 as asymmetric warfare, where AI powers both crime and defence. The paper formalises the risk-adoption gap as a driver of systemic stability, aligns AI-enabled threats, banks’ AI/ML maturity and regulation, and outlines a resilience architecture grounded in data, governance, and talent. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: risk-adoption gap; banking resilience; artificial intelligence; anti-money laundering; AML

This paper proposes a capital-based pricing methodology for banking book loans that embeds Scope 3 emissions and the Partnership for Carbon Accounting Financials (PCAF) attribution standard into a practical transition risk framework for banks. Building on the Scope 3 capital design model of Trevisani et al.,1 the paper adapts the future carbon policy exposure, Climate-Policy-Risk-Weighted-Assets, and climate policy capital (CPC) concepts from trading book derivatives to amortising loan exposures. It shows how a bank can compute a loan-level climate premium by applying CPC as an incremental capital charge to individual facilities and converting it into an interest rate spread via a simple pass-through rule. The framework incorporates financed-emissions attribution in line with PCAF guidelines2,3 and is implemented in a simple, self-contained R framework that produces borrower-specific premia and portfolio views. The resulting tool allows risk managers to integrate climate transition risk into loan pricing and funds transfer pricing in a transparent, scenario-consistent, and operational way, thereby helping institutions prepare their balance sheets for the impact of future climate policy. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: climate risk; Scope 3 emissions; banking book; capital requirements; loan pricing; PCAF; transition risk

Between 2002 and 2025, 556 US insured depositories failed; more than 90 per cent held assets below US$10bn at the date of failure, locating the historical failure record in the small regional and community-bank population.1 The dominant screening tools, probability-of-default (PD) models, internal credit scores, and external ratings model screens2,3 are supervised classifiers fit on a curated set of accounting ratios and a fixed historical failure cohort, and they generalise poorly across distinct distress regimes (the 2008–2012 credit-loss wave, the 2013–2017 agriculture-and-energy stress wave, and the 2023 interest-rate-driven wave that culminated in the Silicon Valley Bank, Signature Bank, and First Republic Bank failures). This paper proposes a three-layer hybrid artificial intelligence framework. Layer 1 is an unsupervised, peer-relative anomaly engine over the Federal Financial Institutions Examination Council call report data.4 Layer 2 augments it with structured market signals for publicly listed bank holding companies and unstructured signals from regulatory disclosures, news, and social media. Layer 3 produces an analyst-facing triage brief. A population-scale proof of concept covers 4,978 institutions and 610,873 bank-quarter observations over Q1 2002–Q4 2025. On the 21 financial distress failures of 2016–2025 (five fraud-driven cases excluded), the Layer 1 engine flags 90 per cent at a top 10 per cent queue threshold with at least one year of advance warning, and 100 per cent at top 15 per cent. The 2023 Silicon Valley Bank cohort is detected at multiple pre-failure quarters by an engine trained only on data through Q4 2018, demonstrating regime portability without hindsight. The contribution is twofold: an unsupervised triage filter that narrows analyst attention to the right banks, and an analyst-facing screen that narrows attention, on each flagged bank, to the right questions.This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: counterparty credit risk; bank failure; unsupervised anomaly detection; FFIEC Call Report; agentic AI; human-in-the-loop

Banks now operate in an environment in which many stresses interact rapidly and non-linearly. In such conditions, resilience cannot be assessed solely by capital strength or periodic stress testing. This paper argues that banking resilience should be understood hierarchically. The primary objective is outcome resilience: the ability of a bank to withstand a severe compound shock. Capability resilience — the ability to run, update, and govern integrated scenario analysis quickly — is a secondary objective. The paper develops a practical framework for both dimensions and uses a quantitative, stylised scenario to illustrate the approach. The framework also compares key prudential metrics before and after management actions. For European institutions, it can also support both short-term environmental stress testing and longer-term resilience analysis. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: banking resilience; outcome resilience; capability resilience; compound shocks; transversality; scenario analysis; management actions; climate risk; geopolitical risk; decision infrastructure

Financial institutions are concerned about the quality of local institutions where they will invest capital as well as human resources and technology. In developing countries, political stability, the curb of corruption and ease of doing business are considered by most investors as the key variables to boost attractiveness. Higher degrees of political instability are associated with lower growth rates of gross domestic product (GDP) per capita. Regarding the channels of transmission, political turmoil adversely affects growth by lowering the rates of productivity and physical and human capital accumulation. Income per capita, institutional stability and democracy are correlated because economic and socio-political institutions transform growth into comprehensive and inclusive development. Today, with mounting global economic and geopolitical turbulences, measuring and anticipating the evolution of governance and institutional stability has never been more challenging. The authors’ recent research shows that governance can provide risk managers with a reliable warning signal of socio-economic and political turmoil. To measure the level of governance, a new composite indicator has been built for 130 developing countries. Its added value stems from a wide range of sub-indices including business conditions, institutional stability, corruption and human freedom, as well as an ‘expert assessment’ that is based on seasoned country risk analysts. This new indicator has been evaluated in several ways. A relationship is observed between the Governance Barometer and low income per capita, institutional fragility and corruption. This global indicator of governance aims to be a useful risk warning tool for financial and cross-border investment strategies. It remains that the structure of the new measure’s mean scores will evolve slowly and cannot be expected to flag an immediately impending crisis. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: country risk; governance; corruption; debt crisis; inclusive development; capital flight; socio-political stability

Flash crashes in algorithmic trading markets, exemplified by the event on 6th May, 2010, reveal vulnerabilities that extend beyond isolated errors or single-point failures. This paper introduces the Swiss Cheese Model — originally conceptualised in aviation and safety engineering domains — as a systems-level framework for comprehensively understanding the simultaneous failures of multiple defence layers within financial markets. The model is adapted to the intricate structure of contemporary trading ecosystems through a detailed conceptual analysis and case study approach, identifying critical defence layers, including circuit breakers, algorithmic controls, liquidity monitoring, regulatory oversight and human intervention. The paper advances theoretical understanding by integrating insights from Normal Accident Theory and the principles of complex adaptive systems, providing practical guidance for risk governance. The Swiss Cheese Model functions as a structured vocabulary and diagnostic framework for analysing emergent failures in high-speed markets, emphasising the necessity for multilayered, diversified and resilient defences in designing algorithmic trading systems and regulatory frameworks. This paper offers a novel approach to understanding and mitigating the risks associated with flash crashes by shifting the focus from single-point causality to systemic alignment. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: flash crash; algorithmic trading; Swiss Cheese Model; systemic risk; model risk; market microstructure

This paper presents comprehensive empirical evidence on minimum variance portfolio optimisation applied to the Swiss equity market over a decade-long period 2015–25. Using a robust quantitative framework with semi-annual rebalancing, the authors construct a concentrated portfolio of Swiss equities that significantly outperforms the Swiss Market Index (SMI) and Swiss Performance Index (SPI) across multiple market regimes while maintaining substantially lower volatility. The strategy exhibits exceptional risk-adjusted performance with a Sharpe ratio of 1.53, approximately 3.5 times higher than the SMI’s 0.43, while delivering a superior downside protection illustrated by the contained drawdowns in comparison with the indices. The portfolio demonstrates a pronounced defensive tilt toward low-volatility sectors, with approximately 50 per cent allocated to financials (predominantly cantonal banks), industrials and real estate companies. Beyond outperforming passive indices, the minimum variance approach substantially exceeds the cumulative performance of professionally managed Swiss equity funds across the sample period. These results have significant implications for risk-averse investors, wealth managers and institutional allocators seeking capital preservation with competitive returns in concentrated equity markets. The paper’s contribution extends the minimum variance literature by providing long-term evidence specifically for the Swiss market and demonstrating that systematic low-volatility strategies exploit persistent market opportunities, especially through the portfolio’s dual capacity to serve as a buffer during market drawdowns, experiencing roughly half the decline of benchmark indices during stress periods, while simultaneously capturing substantial upside performance during bull markets, thereby enabling superior long-term compounding through asymmetric return participation. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: minimum variance optimisation; portfolio construction; Swiss equity market; risk management; convex optimisation; capital preservation; quantitative investing; defensive strategies

This paper presents a case study of an artificial intelligence (AI)-supported media monitoring system implemented in a European commercial banking group to enhance early detection of emerging risks. The pipeline processes millions of media articles daily using a scalable Databricks-based architecture and large language models (LLMs) for relevance scoring, novelty detection and summarisation. It demonstrates how AI-based text analysis supports credit, liquidity and geopolitical risk monitoring by transforming unstructured news into risk-relevant intelligence delivered through automated e-mail briefings, dashboards and early warning system (EWS) integration. The case study further illustrates how such AI-supported media monitoring can be governed and deployed within the regulatory and supervisory framework of the European banking sector while strengthening risk awareness and decision making and offering additional value for compliance, operational risk and communication functions. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: media monitoring; artificial intelligence; AI; early warning system; EWS

Microinsurance serves as a risk transfer mechanism for low-income populations. Yet, significant economic challenges impede insurers’ risk assessment and the pricing of risk; this affects the viability of microinsurance and subsequently the widening of the protection gap, leading to underserved populations. Insurers need to be aware of the macroeconomic factors, such as the low penetration rates and the informality of the sector, both of which complicate the risk assessment and pricing process. Also, insurers must consider supply-side challenges such as the lack of reliable data, business models and cost perspectives when developing suitable products. Demand-side barriers, notably income volatility, further exacerbate the vulnerability of such populations, thus making insurance solutions unviable for both the insurer and the microinsured. Addressing these multifaceted challenges, coupled with active efforts to reduce the gap through government subsidies and private–public partnerships (PPP), improved strategies, business models, regulation and awareness, is essential to achieving sustainable microinsurance solutions and overcoming these viability risks. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: microinsurance; insurance; risk assessment; viability; macroeconomics; financial inclusion; governmental subsidies

This paper examines the relationship between banks’ business models, bank performance and banks’ business risks by employing both panel regression and long short-term memory (LSTM) neural networks. The bank business model definition addresses the fundamental endogeneity problem by strictly separating causal constructs from outcome variables. Building on previous efforts to develop continuous classifications, the authors address the limitations of categorical classifications and introduce a continuous ‘bank business model index’ (BBMI) that captures banks’ strategic balance sheet structures from retail- to market-oriented banks. An empirical analysis of 111 Eurozone banks from 2014 to 2023 reveals that retail-oriented banks outperform market-oriented banks in terms of bank performance. A mediation analysis demonstrates that bank business risks serve as a significant partial mediator in the relationship between banks’ business models and their performance. The study examines risk at the business model level rather than at the institution level. The empirical results show that the LSTM network achieves a higher prediction accuracy than panel regression. Using marginal effects, the authors introduce an approach to address the ‘black box’ limitation of LSTM networks and examine possible nonlinear effects of risk. The results provide valuable insights for regulators, bank managers and researchers to conduct cause-and-effect analyses with a measurable bank’s business model construct and marginal effects to explain deep learning outputs. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Keywords: bank’s business model measure; deep learning; deep learning interpretability; European banks; long short-term memory networks; panel regression; RWA density

Academic interest in understanding the role of financial technology (FinTech) in financial institutions has grown significantly in recent years. FinTech is defined as the use of innovative digital tools and platforms — such as mobile applications, blockchain, artificial intelligence (AI), cloud computing and application programming interfaces — to improve, automate and transform the delivery of financial services and risk management processes. Many studies have explored FinTech adoption, yet few have examined its integration with risk management and regulatory compliance. Therefore, this study sheds light on the literature trends associated with FinTech adoption and operational risk management using a systematic literature review (SLR) and the Theory, Context, Characteristics and Methods (TCCM) framework. The analysis reviews the habits and patterns of FinTech adoption across financial institutions, emphasising the regulatory landscape and cyber security challenges. With the SLR approach, 44 articles published from 2002 to 2023 were analysed. The findings indicate a strong nexus between FinTech adoption, operational efficiency and regulatory compliance, showing an increasing interest among scholars and practitioners. A comprehensive review of dominant theories (eg technology acceptance model and diffusion of innovations), specific contexts (eg banking and financial service providers), characteristics (eg independent, dependent, moderating and mediating variables) and methods (eg research approaches and analytical tools) is provided. This review is the first to critically assess the underexplored intersection between FinTech adoption and regulatory compliance. The findings may help policy makers, banking service providers and academics understand the necessity of balancing FinTech innovation with risk management. The future research agenda outlined in this study will facilitate researchers in exploring new insights within the domain of FinTech adoption and risk mitigation.
Keywords: FinTech adoption; risk management; financial institutions; systematic literature review; TCCM framework; cyber security; regulatory compliance

The implementation of the internal models approach under the Fundamental Review of the Trading Book (FRTB) is a contemporary topic among regulators and the global banking industry, considering the pending finalisation or implementation of localised standards in some major jurisdictions including the US, UK and European Union (EU). This paper proposes a simple intuitive approach to assess the joint impact and sensitivities of Basel III capital Output Floor and minimum desk coverage threshold on the internal models approach (IMA) application. In particular, the paper introduces the optimal desk coverage ratio as a metric to quantify the optimal proportion of the trading desks that a bank may cover under IMA to maximise capital savings given the constraints of Output Floor, minimum desk coverage threshold and other factors. The paper also presents Japan and Canada, where FRTB is in force, as live examples for analysis using actual bank-level regulatory disclosures from ten major banks. The paper illustrates the coverage cliff effect and concludes that the IMA application from a capital savings perspective is heavily driven not only by Output Floor and minimum desk coverage threshold but also by credit and market risk weightings and their respective capital saving ratios from internal models.
Keywords: FRTB; Output Floor; IMA; SA; minimum desk coverage; Basel III Endgame

Incident management — the response to an unplanned interruption or event that potentially harms assets or compromises operations — is the daily business of IT professionals and cyber defence specialists. Guidance on how to implement incident management can be found in international standards; however, the process does not receive sufficient attention from the rest of the organisation. Driven by the digitalisation of the financial sector and the growing threat of cyberattacks, global supervisory authorities have worked over the past five years to strengthen operational resilience. Incident management has been identified as one of the core elements, supported by thorough organisational measures, to provide more transparency, awareness and management attention, but even government influence failed to make this a prominent topic in boardrooms. The threat became a reality, however, with the CrowdStrike outage, the largest information and communication technology (ICT) incident in history, resulting in an estimated financial damage of US$10bn. Since the focus on operational resilience has shifted to ICT, the world has changed dramatically: geopolitical conflicts, extreme weather events and energy insecurity have evolved fast and will challenge organisations in parallel to ICT failures and cyberattacks. This requires a different approach to incident management with more comprehensive oversight, stronger collaboration and integration. As threats are increasingly interconnected, extremely fast coordination and synchronised activation will be required. This paper discusses the building blocks of an integrated incident management system and how operational and strategic elements are related. The paper also reviews European regulations for operational resilience (Digital Operational Resilience Act [DORA]) in the context of a broader implementation approach and how this connects with enterprise risk management (ERM).
Keywords: incident management; polycrisis; operational resilience; cyber incidents; ICT incidents; threats; DORA

This paper examines the Financial Accounting Standards Board’s (FASB) recent changes to define crypto assets, focusing on why non-fungible tokens (NFTs), utility tokens and asset-backed tokens (ABTs) were not included. By examining the core features of these excluded assets, the research unpacks the reasoning behind their omission. The absence of these assets from the standard definition creates challenges for financial institutions. Without clear accounting guidance, companies face uncertainty in valuation, liquidity risk and difficulty meeting compliance requirements. Risk managers are left guessing how to assess and report these holdings. This has an impact on everything from disclosures to capital planning. The findings highlight a critical need for accounting standards that keep pace with the complexity and growth of digital assets. Institutions may misprice assets, misjudge exposure and fall short of regulatory expectations without up-to-date guidance.
Keywords: non-fungible tokens; NFTs; art market; blockchain; financial reporting; valuation; IFRS; US GAAP; risk management; institutional digital asset risks

Financial institutions (FIs) need to develop transition plans for the future low-greenhouse gas (GHG) economy. This is not only a regulatory requirement but also a critical task to best position the institution for future success. This transition will require significant alterations and adjustments to FIs’ asset mix and business activities. Many FIs have publicly committed to 2050 net zero targets. There is an obvious linkage among the FIs’ risk taking, financial performance and financed emissions. Transitioning from high-emitting sectors will moderate the climate risk impact and reduce financed emissions but also affect the financial performance of the FIs. This paper proposes a framework to integrate the measurement and tracking of financed emissions into the FIs’ existing internal capital adequacy assessment (ICAAP) and own risk and solvency assessment (ORSA) frameworks. The process involves capturing climate risks in terms of the key risk drivers, developing a transition plan and quantifying the joint effects in risk, financial and emission metrics. The paper discusses the components, linkages and interconnectedness of the extended ICAAP/ORSA framework, and the dynamic, iterative process to co-manage financial and green performance against their respective targets using the framework. The integrated process enables course corrections and can be extended to an optimisation framework subject to interim green targets.
Keywords: environmental risk; transition plan; transition risk; ESG; green financing; banking capital and funding; ICAAP; ORSA