Model performance monitoring is a well-established component of model risk management in financial institutions; however, a critical gap remains in translating monitoring outputs into consistent and defensible risk decisions. While existing practices generate signals such as performance degradation, data drift, and override activity, responses are often fragmented, judgment-driven, and inconsistently applied. Current approaches also remain largely model-centric and do not adequately address aggregate model risk, where multiple models respond simultaneously to shared macroeconomic conditions.

This paper proposes a structured decision framework for converting model performance signals into consistent governance actions at both model and aggregate levels. At the model level, the framework defines a rule-based decision layer comprising predefined thresholds, escalation logic, and action-mapping rules that translate observed signal patterns into actions such as continued monitoring, targeted validation, recalibration, or model redevelopment, reducing reliance on subjective judgment. Machine learning techniques, including supervised classification, unsupervised anomaly detection, and explainable attribution methods, are applied to support signal interpretation and root-cause identification, including changes arising from data distribution shifts, population variations, or macroeconomic transitions.

At the aggregate level, the framework introduces a multi-model signal aggregation approach that integrates internal performance indicators with macroeconomic and event-driven context to identify systemic risk drivers affecting multiple models. Given the complexity of cross-model dependencies, machine learning techniques support detection of correlated deterioration patterns and interpretation of aggregate model risk, allowing institutions to distinguish isolated issues from broader regime shifts across key financial risk domains, including credit risk, IFRS 9 impairment, stress testing, market risk, and broader balance sheet and regulatory capital models.

The key contribution is the formalization of a decision layer within model risk governance that bridges signal detection and governance response. The framework incorporates explainable decision pathways, human-in-the-loop oversight, and an audit structure to ensure traceability and regulatory defensibility under SR 26-2 and PRA SS1/23.

Over the past decade, there has been growing interest in the dissemination of non-financial information in corporate reporting. Its relevance stems from the significant influence it has on investor decisions, consumer behavior, and public policy orientation. Organizations are facing increasing pressure to present quality information in terms of ESG reporting and to take responsibility for their impact on society and the environment. In order to respond to the obligations of new regulations, companies are placing great emphasis on ESG risk management. This study aims to analyze the readability and transparency of non-financial reports in companies in different risk sectors to determine the impact of ESG reporting quality on operational and reputational risk. The case study is based on the sustainability reports of European companies in the oil and gas sectors (very high-risk), chemical and mining sectors (high-risk), the energy sector (medium-risk), and retail (low-risk) from 2019-2024 and the GRI Sustainability Disclosure Database. By including the four sectors with different risk profiles, readability and transparency will be systematically compared both within and between sectors. For the analysis of readability and transparency, an index calculated using the R package was built. Through this case study on a multi-sector comparative design that allows us to draw cross-industry generality and enabling sector-level risk comparisons, we have a sample size for panel regression and for robust statistical testing. Within the results obtained, we want to identify whether companies in sectors with high environmental risk use more opaque language in sustainability reports as a strategy for managing operational and reputational risk and to what extent the information is correlated with documented environmental incidents.

Producing reliable regional life tables is essential for capturing the disparities in mortality and life expectancy at the regional scale. These disparities provide critical insight for assessing demographic and actuarial risks that influence public health systems, social security, and financial institution schemes. In Algeria, the absence of regional life tables published by the Office for National Statistics (ONS) constrains the analysis and understanding of mortality patterns and life expectancy differences across regions. To address this gap, this study estimates mortality rates across seven Algerian regions (North Central Region, Northeast Region, Northwest Region, Central Highlands, Eastern Highlands, Western Highlands, and Great South) using data from the latest Multiple Indicator Cluster Surveys (MICS4 and MICS6), combined with official national mortality data from the ONS. However, the raw mortality rates derived from MICSs are sparse and noisy and require adjustment to ensure smoothness and completeness of mortality curves using a Bayesian relational model. Our results indicate the existence of substantial disparities in mortality and life expectancy across regions. The Great South exhibits the highest mortality levels and the lowest life expectancy for both sexes, while the Western Highlands show the most favourable profiles. These findings identify regions facing elevated demographic and actuarial risk, providing insights for insurance modelling, pension planning, and public health design. By combining survey data with statistical modelling, this study provides a practical framework for measuring and managing demographic risks in data-limited contexts.

Parametric and index-based risk transfer mechanisms are increasingly promoted as scalable solutions to climate-related losses across insurance and capital markets. However, the rapidly expanding literature on these instruments remains fragmented across disciplinary, institutional, and regulatory domains, obscuring how risk is governed and allocated under conditions of climate non-stationarity. This study conducts a concept-driven umbrella review of peer-reviewed reviews and synthesis studies to examine how parametric risk transfer is theorized, designed, and governed across insurance and capital market regimes. Using a structured search of the Scopus database and guided by the PRISMA framework, twenty-one review studies published between 1991 and 2026 were synthesized through a thematic analysis informed by core insurance principles and governance theory. An adapted AMSTAR 2 appraisal was employed to assess confidence in the evidence base. The synthesis reveals that parametric instruments operate under fundamentally different governance logics across market regimes, despite shared reliance on index-based triggers. In insurance-centered applications, trigger design is closely tied to principles of indemnification, insurable interest, and legitimacy, with basis risk framed as a social and contractual concern. In capital-market-based structures, including catastrophe bonds and insurance-linked securities, trigger design functions primarily as a risk allocation mechanism, shifting model uncertainty, basis risk, and climate non-stationarity from sponsors to investors. Across both regimes, the increasing reliance on complex models and externally produced data reconfigures traditional insurance principles, relocating key governance functions from underwriting and claims settlement to model governance and index calibration. These findings suggest that parametric risk transfer should not be understood solely as a technical innovation but as a governance arrangement that redistributes climate risk, uncertainty, and accountability across actors. The paper contributes a unifying conceptual framework that links trigger design, insurance principles, and regulatory boundaries, highlighting implications for climate risk governance, market regulation, and the future role of insurance under accelerating climate change.