In the context of Japan’s rapidly aging population, people’s anxiety about life after 65, especially regarding financial sustainability, has become a growing concern. This study examines old age anxiety through the lens of digital financial literacy (DFL), which can significantly impact people’s retirement well-being and long-term financial security in today’s digital environment. Drawing on a large-scale dataset from the “Survey on Life and Money,” jointly conducted by Rakuten Securities and Hiroshima University, we analyze responses from 94,695 individuals aged 40 to 64 who are active bank account holders. Based on ordinal logistic regression, our findings reveal a negative association between DFL and old age anxiety. Further analysis of the five dimensions of DFL demonstrates that several practical components, such as digital financial know-how, decision-making abilities, and self-protection skills, are associated with alleviated old age anxiety. In contrast, a reliance on basic financial knowledge and general awareness alone may exacerbate anxiety. These findings underscore the need to move beyond basic digital awareness and focus on promoting practical skills in digital finance, ultimately supporting better financial decision-making and enhancing overall well-being in older age.

The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) designates AI systems used in life and health insurance underwriting as high-risk systems, imposing rigorous requirements for bias testing, technical documentation, and post-deployment monitoring. Leveraging 12.4 million quote–bind–claim observations from four pan-European insurers (2019 Q1–2024 Q4), we evaluate how compliance affects premium schedules, loss ratios, and solvency positions. We estimate gradient-boosted decision tree (Extreme Gradient Boosting (XGBoost)) models alongside benchmark GLMs for mortality, morbidity, and lapse risk, using Shapley Additive Explanations (SHAP) values for explainability. Protected attributes (gender, ethnicity proxy, disability, and postcode deprivation) are excluded from training but retained for audit. We measure bias via statistical parity difference, disparate impact ratio, and equalized odds gap against the 10 percent tolerance in regulatory guidance, and then apply counterfactual mitigation strategies—re-weighing, reject option classification, and adversarial debiasing. We simulate impacts on expected loss ratios, the Solvency II Standard Formula Solvency Capital Requirement (SCR), and internal model economic capital. To translate fairness breaches into compliance risk, we compute expected penalties under the Act’s two-tier fine structure and supervisory detection probabilities inferred from GDPR enforcement. Under stress scenarios—full retraining, feature excision, and proxy disclosure—preliminary results show that bottom-income quintile premiums exceed fair benchmarks by 5.8 percent (life) and 7.2 percent (health). Mitigation closes 65–82 percent of these gaps but raises capital requirements by up to 4.1 percent of own funds; expected fines exceed rectification costs once detection probability surpasses 9 percent. We conclude that proactive adversarial debiasing offers insurers a capital-efficient compliance pathway and outline implications for enterprise risk management and future monitoring.

This article provides an overview of the current state-of-the-art in cyber risk and cyber risk management, focusing on the mathematical models that have been created to help with risk quantification and insurance pricing. We discuss the main ways that cyber risk is measured, starting with vulnerability functions that show how systems react to threats and going all the way up to more complex stochastic and dynamic models that show how cyber attacks change over time. Next, we examine cyber insurance, including the structure and main features of the cyber insurance market, as well as the growing role of cyber reinsurance in strategies for transferring risk. Finally, we review the mathematical models that have been proposed in the literature for setting the prices of cyber insurance premiums and structuring reinsurance contracts, analysing their advantages, limitations, and potential applications for more effective risk management. The aim of this article is to provide researchers and professionals with a clear picture of the main quantitative tools available and to point out areas that need further research by summarising these contributions.

Accepting a certain degree of investment loss risk is essential for long-term portfolio management. However, overconfidence bias within financial literacy can prompt excessively risky behavior and amplify susceptibility to other cognitive biases. These tendencies can undermine investment loss tolerance beyond the baseline level shaped by sociodemographic, economic, psychological, and cultural factors. This study empirically examines the association between overconfidence and investment loss tolerance, which is measured by the point at which respondents indicate they would sell their investments in a hypothetical loss scenario. Using a large-scale dataset of 161,765 active investors from one of Japan’s largest online securities firms, we conduct ordered probit and ordered logit regression analyses, controlling for a range of sociodemographic, economic, and psychological variables. Our findings reveal that overconfidence is statistically significantly and negatively associated with investment loss tolerance, indicating that overconfident investors are more prone to prematurely liquidating assets during market downturns. This behavior reflects an impulse to avoid even modest losses. The findings suggest several possible practical strategies to mitigate the detrimental effects of overconfidence on long-term investment behavior.

The objective of this paper is to analyse profitability and risk through the return on equity (ROE) measure of the open economy insurance sector in a non-stable economic period with an economic shock chain, during the years 2018–2022, characterised by an overheating economy, the Covid pandemic, the war in Ukraine, and a high-inflation wave. The ROE pyramid decomposition structure is proposed, along with the detailed CARAMEL version. A static and risk (dynamic) decomposition deviation analysis is used. The yearly non-stable drivers of insurance sector profitability deviation were confirmed. Despite this, the most influential were the earnings ratio deviations in either increasing or decreasing ROE alternatives. Solvency positively influenced the ROE deviation. It turned out that earnings and asset quality enormously increase the risk of the insurance sector. Conversely, risk is decreased mainly by liquidity and management. Simultaneously, significant, influential factors were identified. The results can serve as a background for carrying out operations, strategic analysis, and decision-making.

Accurate corporate credit ratings are essential for financial risk assessment; yet, traditional methodologies relying on manual evaluation and basic statistical models often fall short in dynamic economic conditions. This study investigated the potential of machine-learning (ML) algorithms as a more precise and adaptable alternative for credit rating predictions. Using a seven-year dataset from S&P Capital IQ Pro, corporate credit ratings across 20 countries were analyzed, leveraging 51 financial and business risk variables. The study evaluated multiple ML models, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, Gradient Boosting (GB), and Neural Networks, using rigorous data pre-processing, feature selection, and validation techniques. Results indicate that Artificial Neural Networks (ANN) and GB consistently outperform traditional models, particularly in capturing non-linear relationships and complex interactions among predictive factors. This study advances financial risk management by demonstrating the efficacy of ML-driven credit rating systems, offering a more accurate, efficient, and scalable solution. Additionally, it provides practical insights for financial institutions aiming to enhance their risk assessment frameworks. Future research should explore alternative data sources, real-time analytics, and model explainability to facilitate regulatory adoption.

The relationship between ESG and firm performance is complex and tends to yield mixed results globally. In South Africa, ESG implementation is still in its infancy stage due to economic and developmental challenges. Despite these challenges, the JSE introduced sustainability disclosure guidelines in 2022 to enhance ESG adoption in South Africa. Thus, the study seeks to understand the impact of ESG and firm size on the financial performance of JSE-listed firms in South Africa. The study utilised the JSE Top 40 firms for the period from 2002 to 2022. Furthermore, the study employed a two-step System Generalised Method of Moments, to estimate the impact of total ESG and individual dimensions of ESG on firm financial performance. Additionally, the study examined the moderating effects of firm size on the relationship between financial performance and ESG. The results revealed a positive and significant relationship between total ESG and firm financial performance. However, the findings regarding individual ESG dimensions and firm performance are mixed. Firm size has a moderating effect on the relationship between ESG and firm financial performance. The implication of these findings for South Africa is increased foreign direct investment from green investors and listed firms seriously considering ESG in their operations.

Our study explores how ESG performance affects corporate misconduct (CM) in Gulf Cooperation Council (GCC) firms and whether having more women on corporate boards influences this relationship. Using logistic regression and using data collected from GCC firms, we analyse the moderating effect of board gender diversity (BGD) on the relationship between ESG and CM. Our findings show that strong ESG performance reduces CM, and greater BGD further decreases misconduct. Moreover, gender-diverse boards strengthen the link between ESG and lower CM rates. This study contributes to the literature by examining how BGD influences the ESG-CM relationship in the GCC region. The current findings are valuable for investors, businesses, and policymakers. Investors should prioritize companies with strong ESG practices and diverse boards to minimize the risks they might face. Businesses should integrate female directors on boards to enhance ethical practices. Policymakers can promote corporate responsibility by incentivizing gender diversity and ESG adoption, which is crucial for a more transparent and accountable business environment.

Women in emerging economies face unique constraints rooted in cultural norms, socio-economic disparities, and limited access to education and technology. Narrowing the digital gender gap by ensuring access to financial services may reduce the economic inequalities for women in these countries. This study examines the influence of digital finance in narrowing the gender gap, guided by the research question: To what extent do digital financial services contribute to narrowing the gender gap in access to and usage of financial services in low-and middle-income economies? Gender inclusion was measured by the ratio of accounts owned by women over the total number of accounts. Digital financial inclusion was constructed based on eight components: mobile money account, storing money in financial institutions, Internet access, mobile phone owned, savings, savings in financial institutions, making or receiving a digital payment, and mobile phone or use of the Internet for shopping. A Bayesian regression approach was computed using the Global Findex Database data for 73 countries classified as low and lower-middle-income economies from 2011 to 2022. The Machine Learning approach evaluates the model’s ability to predict women’s autonomy and the role of digital finance. The results show that digital financial services would reduce the gender gap in low-income economies while augmenting the number of open accounts, especially for women. The results aid in the establishment of policies to reduce the gender gap. These results are relevant to the UNSDG agenda, mainly Goal 5 and Goal 10.

This paper aims to develop optional semimartingale methods in risk theory to allow for a larger class of risk models. Optional semimartingales are left-continuous with right-limit stochastic processes defined on a probability space where the usual conditions—completeness and right-continuity of the filtration—are not assumed. Three risk models are formulated, accounting for inflation, interest rates, and claim occurrences. The first model extends the martingale approach to calculate ruin probabilities, the second employs the Gerber–Shiu function to evaluate the expected discounted penalty from financial oscillations or jumps, and the third introduces a Gaussian risk model using counting processes to capture premium and claim cash flow jumps in insurance companies.