The rapid pace of technological innovation, globalization, and environmental change is generating a new class of emerging risks that are complex, uncertain, and cross-sectoral. These risks, ranging from climate–energy transitions and digital vulnerabilities to biosecurity and resource scarcity, cannot be understood through single-disciplinary lenses. Addressing them requires integrated and collaborative approaches that combine insights from science, engineering, data analytics, and social sciences. This paper explores how interdisciplinary frameworks enhance our ability to identify, assess, and manage emerging risks in dynamic systems. It highlights critical intersections such as the climate–energy–infrastructure nexus, artificial intelligence and ethics, and health environment interactions, where risks evolve through interlinked feedbacks. By connecting technical innovation with socio-economic and governance perspectives, interdisciplinary research promotes holistic understanding and more adaptive policy responses. We argue that resilience in modern societies depends on bridging disciplinary boundaries to anticipate systemic vulnerabilities before they escalate. Emerging tools such as machine learning, network modeling, and systems analysis can support this transition by revealing hidden dependencies and cascading effects. Strengthening cross-sectoral collaboration, transparent data exchange, and adaptive governance will be vital for transforming risk management from reactive control to proactive foresight. In summary, managing emerging risks demands a shift toward integrative, interdisciplinary thinking that transforms uncertainty into knowledge and builds sustainable, resilient futures.

In the Solvency II framework for insurance, the cost of capital rate is a critical metric that encapsulates the cost of holding capital to meet regulatory solvency requirements, while also reflecting the investor’s opportunity cost of capital allocation. It is therefore essential for insurers to rigorously justify the magnitude of this rate, particularly from the perspective of investors who perceive it as a required rate of return on capital. In the literature, they investigated the magnitude of this rate in the economic triangle of the policyholder, the shareholder, and the regulator. This paper seeks to extend that analysis by incorporating access to the financial market and focusing on hybrid life liabilities, which combine financial and mortality risks, thereby affording an asset-liability management perspective that insurers can employ to optimize business run-off. Furthermore, by incorporating partial hedging strategies, we show how hedging can affect both the numerator (i.e., the risk margin) and the denominator (i.e., the solvency capital requirement) of the cost of capital ratio. We focus precisely on when the hedging operation is considered effective. In particular, we demonstrate that, depending on its cost, effective hedging may not necessarily reduce the policyholder's risk margin. Our results provide insights into the practical limitations and regulatory implications of the cost of capital methodology in partially replicable environments.

In applied insurance data science, practitioners routinely face critical challenges during the data preparation stage that can undermine the statistical validity and reliability of subsequent actuarial modeling. This study addresses a fundamental issue: demonstrating that conventional data preparation procedures, particularly random train–test partitioning, often yield unreliable and unstable results when confronted with highly imbalanced insurance loss data. Such naive splitting fails to maintain representativeness, especially of rare high-loss events, compromising the validity of model evaluation. To mitigate these limitations and establish a foundation for more robust modeling, we propose a novel data preparation framework leveraging two recent statistical advancements. First, we employ Support Points to achieve data splitting that is demonstrably more representative of the underlying data distribution than simple random sampling. Second, we utilize the Chatterjee Correlation Coefficient for an initial, non-parametric screening of feature relevance and dependence structure. We integrate these theoretical methods into a unified, efficient framework that also incorporates advanced handling of missing data, and we further embed the framework within our custom InsurAutoML\footnote{ \href{https://github.com/PanyiDong/InsurAutoML}{https://github.com/PanyiDong/InsurAutoML}.} pipeline. The performance of this proposed approach is rigorously evaluated using both simulated datasets and datasets often cited in the academic literature. The empirical assessment focuses on quantifying the gains in computational efficiency and model stability—key concerns in industry-scale workflows. Our findings definitively demonstrate that incorporating statistically rigorous data preparation methods not only significantly enhances model robustness and interpretability but also substantially reduces computational resource requirements across diverse insurance loss modeling tasks. This work provides a crucial methodological upgrade for achieving reliable results in high-stakes insurance applications.

The large-scale integration of renewable energy sources into deregulated power systems introduces significant stochasticity in generation, network congestion, and market operations, leading to pronounced volatility and financial risk in day-ahead electricity prices. Accurate and risk-aware price forecasting is therefore essential for market participants to optimize bidding strategies, manage exposure to price uncertainty, and support sustainable electricity market operation. This paper proposes a multi-objective stochastic market-oriented optimal power flow (MO-SMOOPF) framework for day-ahead electricity price forecasting, explicitly embedding economic efficiency, price formation, congestion effects, and risk management within the physical constraints of the power flow problem.

The proposed framework formulates electricity market clearing as a many-objective optimization problem, simultaneously considering social welfare maximization, producer profit, consumer payment minimization, congestion rent allocation, reserve procurement cost, locational marginal price (LMP) volatility reduction, and conditional value-at-risk (CVaR)-based financial risk mitigation. These objectives are optimized subject to AC power flow equations, transmission capacity limits, generator operating constraints, reserve adequacy requirements, and market settlement balance conditions, ensuring both physical feasibility and financial consistency.

To solve the resulting high-dimensional and nonlinear optimization problem, an improved non-dominated sorting genetic algorithm II (NSGA-II) is integrated with a radial basis function (RBF) neural network. The enhanced NSGA-II incorporates adaptive crossover and mutation strategies, dynamic crowding distance evaluation, and elite retention mechanisms to effectively balance convergence, diversity, and robustness. The RBF neural network is employed to capture the nonlinear and stochastic dependencies between market prices and system states, enabling accurate day-ahead price forecasting under uncertainty.

The proposed approach is validated on the IEEE-118 bus system under stochastic renewable and load scenarios. Numerical results demonstrate superior forecasting accuracy, improved risk-adjusted market outcomes, and enhanced price stability compared with conventional methods. The developed framework provides a robust decision-support tool for financial risk management and sustainable operation of future electricity markets.

Introduction:
Discounted cash flow (DCF) valuation is a standard method for estimating firm value, yet many applications implicitly assume independent cash flows. In practice, however, financial cash flows frequently exhibit temporal dependence, which may substantially affect valuation risk.

Methods:
This study derives analytical expressions for the expectation and variance of discounted cash flows under three stochastic processes: white noise, ARMA(1,1), and random walk (ARIMA(0,1,0)). The formulas are obtained for finite and infinite planning horizons and also include limiting cases of discounting structures.

Results:
The results show that autocorrelation increases valuation uncertainty while leaving expected firm value unchanged. For ARMA(1,1) processes, valuation risk depends on both innovation variance and the induced covariance structure. In the random walk case, shocks are fully persistent, leading to a strong accumulation of uncertainty over time and a more than proportional growth of valuation variance.

Conclusions:
The analysis demonstrates that ignoring temporal dependence can lead to a substantial underestimation of valuation risk in DCF models. The derived closed-form expressions provide a tractable framework for incorporating dependence structures into firm valuation. Future research may extend the framework to long-memory ARFIMA processes, which allow for gradually decaying dependence between the short-memory ARMA structure and the fully persistent random walk case, offering a more flexible description of persistence in cash-flow dynamics.

This study explores the relationship between macroeconomic performance and bank credit risk across Sub-Saharan Africa (SSA), a region where financial institutions operate amid persistent structural vulnerabilities, limited diversification, and exposure to both domestic and global shocks. While substantial research has analyzed how macroeconomic instability impacts credit risk in advanced and emerging markets (Beck et al., 2013; Louzis et al., 2012), empirical evidence from SSA remains fragmented, often lacking region-specific modeling that captures institutional weaknesses and external dependencies.

The central research question asks the following: To what extent do macroeconomic variables such as inflation, GDP growth, exchange rate volatility, and fiscal balance affect non-performing loan (NPL) ratios across SSA banking systems? The study hypothesizes that poor macroeconomic performance exacerbates credit risk, with more severe effects observed in low-income countries where monetary policy tools and supervisory mechanisms are weaker (Khemraj & Pasha, 2009; Nkusu, 2011).

Using a panel dataset from 25 SSA countries between 2005 and 2023, the study applies a system GMM estimator to address endogeneity and cross-country heterogeneity. Additional controls include institutional quality, regulatory strength, and bank-specific indicators.

Findings are expected to generate actionable insights for regional central banks and financial regulators, helping to design macroprudential tools suited to SSA economies. The paper contributes to the growing literature on financial stability by emphasizing the role of macroeconomic context in shaping bank risk across structurally diverse low- and middle-income countries.

This research examines the primary factors contributing to financial system instability, with an emphasis on leveraged ETFs that involve long and short positions in alternative forms of currency. Utilizing the sophisticated approach of network connectedness and daily data from the inception of ETFs for digital, fiat, and metal currencies, our findings show that while precious metal-based ETFs shield investors from variations in fiat or digital investments, cryptocurrency ETFs provide better protection against both metal and fiat ETFs during bear markets but lose stability during bull markets. The primary stabilizer against short positions is gold, as these positions destabilize other forms of currency during bull markets, while sub-leading currencies such as silver and Ether heighten systemic risk. If national currencies were absent during bull markets, it would lead to bubbles in gold and silver and cause serious systemic instability down the line. These findings offer new insights into the intricate dynamics of alternative monetary tools and demonstrate how they contribute to systemic risk within the current fragile monetary system. Moreover, this paper provides a compass to interested investors and policymakers by shedding light on how systemic resilience can be amplified or mitigated depending on the type of assets considered. Leverage being a protagonistic phenomenon for the outbreak of crises makes leveraged ETFs function as fuel for over-enthusiasm but potentially for abrupt turmoil.

The rapid adoption of artificial intelligence (AI) in operations and supply chain management has transformed decision-making related to demand forecasting, inventory optimization, supplier selection, and logistics planning. While AI-driven systems offer substantial efficiency and cost advantages, they also introduce emerging risks related to system reliability, bias, over-automation, and unintended sustainability trade-offs. This study develops an interdisciplinary framework that examines how AI-enabled operational decisions influence performance, risk exposure, and sustainability outcomes in operations and supply chains. Drawing on the responsible AI, risk management, and sustainability literature, the paper conceptualizes AI as both an operational capability and a source of systemic risk. Using secondary evidence and illustrative operational scenarios, the study identifies key AI-related risks, such as model opacity, data bias, and reduced human oversight, and analyzes how these risks affect operational resilience, environmental efficiency, and social responsibility across supply chains. The framework highlights the moderating role of human-centered AI governance mechanisms, including transparency, accountability, and decision oversight, in mitigating risk while preserving performance gains. The study contributes to operations and supply chain management research by integrating AI risk management with sustainability objectives. Managerially, it provides guidance on designing responsible and safe AI systems that balance efficiency, resilience, and long-term sustainable value creation.

Maritime piracy remains a persistent and evolving threat to global shipping networks, international trade, and supply chain stability. Despite significant advances in maritime security practices, piracy incidents continue to generate substantial operational, financial, and insurance-related risks for shipping companies and associated stakeholders. This paper proposes an interdisciplinary framework for assessing the risk of maritime piracy by integrating quantitative risk modeling with qualitative operational analysis. The study examines historical piracy data, regional risk patterns, and contributing socio-economic and geopolitical factors that influence the frequency and severity of piracy incidents.

The research develops a structured risk assessment model that combines statistical techniques, including probabilistic risk analysis and scenario-based modeling, with expert-based evaluation methods such as multi-criteria decision analysis. This approach enables the identification of key risk drivers, interdependencies, and potential escalation pathways. Particular attention is given to the impact of piracy on shipping operations, insurance costs, and logistical disruptions, as well as to the effectiveness of existing mitigation strategies such as onboard security measures, routing optimization, and international cooperative initiatives. The proposed framework is further positioned within the context of operational risk modeling and is designed to be compatible with emerging explainable artificial intelligence approaches in cyber and hybrid risk management.

The framework considers emerging challenges, including the increasing technological sophistication of maritime operations and the interaction between physical and cyber risks. The findings highlight the necessity of incorporating maritime piracy into comprehensive enterprise risk management and insurance decision-making processes. By treating piracy as an interdisciplinary risk that intersects operational, financial, and geopolitical domains, stakeholders can develop more resilient strategies to protect assets and maintain continuity of global trade. The proposed approach contributes to a broader understanding of emerging risks in maritime contexts and offers practical insights for policymakers, insurers, and shipping operators seeking to enhance risk preparedness and adaptive response capabilities.

Introduction The UK pension buyout market exceeded £50 billion in 2024, yet current industry mortality tables (S3PA, SAPS) aggregate data at the postcode sector level, potentially masking significant within-sector heterogeneity. Building on the Renshaw–Haberman cohort mortality framework, this work-in-progress examines whether finer postal district granularity (average 2,500 residents vs. 7,000 for sectors) improves longevity risk assessment and pricing accuracy in bulk annuity transactions.

Methods We analyze ONS mortality data (2015-2023) across UK postal districts, linked to Index of Multiple Deprivation scores and geodemographic classifications. Using Lee–Carter models with geographic random effects and stratified Cox proportional hazards with frailty terms, we quantify life expectancy differentials controlling for socioeconomic factors, with validation against recent mortality experience. We apply postal district mortality rates to representative defined benefit pension scheme portfolios, comparing reserve adequacy against S3PA baseline assumptions through Monte Carlo simulation under various geographic concentration scenarios.

Results Preliminary findings indicate substantial life expectancy variation across postal districts, exceeding current sector-level adjustments in industry tables. Geographic concentration of scheme membership appears to drive material pricing divergence from standard assumptions, with implications varying by deprivation profile and regional mortality patterns. Both positive and negative pricing adjustments emerge depending on membership characteristics. Results demonstrate robustness across alternative deprivation indices and sensitivity analyses on mortality improvement assumptions. Statistical significance testing and quantification of financial impacts are ongoing.

Conclusions Postal district granularity shows promise for materially improving bulk annuity pricing accuracy, directly addressing FCA fair value assessment requirements. The transparent methodology using publicly available ONS data offers insurers enhanced risk selection capabilities while providing pension trustees with tools for better membership longevity profiling. This approach is generalizable to other countries with fine-grained mortality statistics and supports enhanced regulatory capital modeling under Solvency II. We will present completed quantitative findings and practical implementation frameworks at the conference.