Risks

Background: Despite the apparent importance of global catastrophe risks (GCRs), human society has invested relatively little to reduce them. One possible reason is that we do not understand the significance of reducing GCRs, especially when measured in the monetary terms that we typically use to make decisions. Consequently, we cannot compare them to other issues that influence our decision making and well-being. Purpose: In this study, we quantified the benefits of reducing all non-natural GCRs to highlight their importance. Method: We used a probabilistic model for simulation. Due to limited information, we introduced concepts and assumptions to aid the calculations, such as steady-state economics and sensitivity analyses. In addition, we converted expert opinions to help us focus on a narrower range of risk levels. Results: Within a considerably plausible range of the GCR, we found the following: 1. The benefits of halving the overall non-natural GCR over the next 100 years are substantial. 2. The expected human survival years are sensitive to the mitigation effort but robust to the horizon length. 3. The higher the population growth rate, the larger the expected life years saved. 4. The expected monetary benefits are positively related to the GWP per capita growth rate, mitigation period, and magnitude of natural GCRs but are negatively related to the discounting rate. Significance: The human species is actually facing multiple GCRs simultaneously. In the literature, there is still a gap in quantifying the benefits of reducing all non-natural GCRs/ERs in the coming century while accounting for the very long run on a million-year scale. This article fills such a gap, and the results may serve as a reference for global policymaking to handle this global public issue.

European Union legislation, particularly Council Directive 2004/113/EC, mandates gender neutrality in credit scoring to prevent discrimination. However, this creates a regulatory paradox if gender is a statistically relevant predictor of default risk. This study investigates this “fairness-through-unawareness” approach by empirically testing for systematic mispricing. We employ a twofold econometric analysis on a dataset of consumer loans from a Lithuanian peer-to-peer platform. After data preparation for the regression, the sample consists of 9707 loans. First, logistic regression is used to model actual default risk, controlling for credit rating, age, loan amount, and education. Second, Ordinary Least Squares (OLS) regression is used to model the interest rate set by the platform. The Logit model finds that gender is a highly significant predictor of default (p < 0.001), with male borrowers associated with a higher probability of default. Conversely, the OLS model finds that gender is not a statistically significant factor in loan pricing (p = 0.263), confirming the platform’s compliance with EU law. The findings empirically demonstrate the regulatory paradox: the legally compliant, gender-blind pricing model fails to account for a significant risk differential. This leads to systematic risk mispricing and an implicit cross-subsidy from lower-risk female borrowers to higher-risk male counterparts, highlighting a critical tension between regulatory intent and outcome fairness. The analysis is limited to observed loan-level characteristics; it does not incorporate household composition or the internal structure of the platform’s proprietary scoring model.

The rapid growth of the consumer credit card market has introduced substantial regulatory and risk management challenges. To address these challenges, financial institutions increasingly adopt advanced machine learning models to improve default prediction and portfolio monitoring. However, the use of such models raises additional concerns regarding transparency and fairness for both institutions and regulators. In this study, we investigate the consistency of Shapley Additive Explanations (SHAPs), a widely used Explainable Artificial Intelligence (XAI) technique, through a case study on credit card probability-of-default modeling. Using the Default of Credit Card dataset containing 30,000 consumer credit accounts information, we train 100 Extreme Gradient Boosting (XGBoost) models with different random seeds to quantify the consistency of SHAP-based feature attributions. The results show that the feature SHAP stability is strongly associated with feature importance level. Features with high predictive power tend to yield consistent SHAP rankings (Kendall’s W = 0.93 for the top five features), while features with moderate contributions exhibit greater variability (Kendall’s W = 0.34 for six mid-importance features). Based on these findings, we recommend incorporating SHAP stability analysis into model validation procedures and avoiding the use of unstable features in regulatory or customer-facing explanations. We believe these recommendations can help enhance the reliability and accountability of explainable machine learning framework in credit risk management.