Fraud in financial services—especially account opening fraud—poses major operational and reputational risks. Static rules struggle to adapt to evolving tactics, missing novel patterns and generating excessive false positives. Machine learning promises adaptive detection, but deployment faces severe class imbalance: in the NeurIPS 2022 BAF Base benchmark used here, fraud prevalence is 1.10%. Standard metrics (accuracy, f1_weighted) can look strong while doing little for the minority class. We compare Logistic Regression, SVM (RBF), Random Forest, LightGBM, and a GRU model on N = 1,000,000 accounts under a unified preprocessing pipeline. All models are trained to minimize their loss function, while configurations are selected on a stratified development set using validation-weighted F1-score f1_weighted. For the four classical models, class weighting in the loss (class_weight $\in \{\mathrm{None},‘\mathrm{balanced}’\}$) is treated as a hyperparameter and tuned. Similarly, the GRU is trained with a fixed class-weighted CrossEntropy loss that up-weights fraud cases. This ensures that both model families leverage weighted training objectives, while their final hyperparameters are consistently selected by the f1_weighted metric. Despite similar AUCs and aligned feature importance across families, the classical models converge to high-precision, low-recall solutions (1–6% fraud recall), whereas the GRU recovers 78% recall at 5% precision (AUC $=0.8800$). Under extreme imbalance, objective choice and operating point matter at least as much as architecture.

Labor market forecasting relies heavily on economic time-series data, often overlooking the “health–wealth” gradient that links population health to workforce participation. This study develops a machine learning framework integrating non-traditional health and social metrics to predict state-level employment density. Methods: We constructed a multi-source longitudinal dataset (2014–2024) by aggregating county-level Quarterly Census of Employment and Wages (QCEW) data with County Health Rankings to the state level. Using a time-aware split to evaluate performance across the COVID-19 structural break, we compared LASSO, Random Forest, and regularized XGBoost models, employing SHAP values for interpretability. Results: The tuned, regularized XGBoost model achieved strong out-of-sample performance (Test $R^2$ = 0.800). A leakage-safe stacked Ridge ensemble yielded comparable performance (Test $R^2$ = 0.827), while preserving the interpretability of the underlying tree model used for SHAP analysis.

Natural disasters pose significant threats to global communities, necessitating advanced predictive frameworks for effective risk assessment and management. This study presents an AI-driven disaster risk prediction system integrating XGBoost machine learning with SHAP (SHapley Additive exPlanations) interpretability analysis. Using the World Risk Index dataset spanning 11 years across 181 countries, we developed multi-class classification models for four key risk indicators: World Risk Index (WRI), Exposure, Vulnerability, and Susceptibility. The XGBoost classifier achieved test accuracies exceeding 0.85 across all categories, with macro-averaged AUC scores ranging from 0.92 to 0.96. SHAP analysis revealed critical driving factors influencing disaster susceptibility, demonstrating the interpretability of AI-powered predictions. Our explainable AI framework provides transparent, actionable insights for policymakers and disaster management authorities, bridging the gap between predictive accuracy and decision-making transparency in global risk assessment.

Urban traffic flow prediction is critical for intelligent transportation systems, yet remains challenging due to complex spatiotemporal dependencies and external weather influences. Existing methods suffer from three key limitations: (1) uniform time-domain modeling fails to disentangle periodic trends from aperiodic fluctuations, (2) weather information is integrated through simple concatenation without considering multi-granularity impacts, and (3) lack of interpretability hinders understanding of weather-traffic interactions. To address these issues, we propose MFTFormer, a novel framework that synergistically combines time-frequency decomposition with hierarchical weather-aware fusion. Our approach introduces three key innovations: (1) a dual-domain encoder with mixture-of-experts that separately processes trend and seasonal components via specialized frequency and temporal attention networks, (2) a block-aligned fusion module that hierarchically reorganizes features through convolutional refinement and weather-conditioned modulation, capturing both immediate and cumulative meteorological effects, and (3) interpretable attention mechanisms that reveal dynamic spatial dependencies under varying weather conditions. Extensive experiments on four real-world datasets (PeMS04, PeMS08, Metro-Traffic, METR-LA) demonstrate that MFTFormer achieves state-of-the-art performance with an average 6.2% MAE reduction over strong baselines, while providing actionable insights into weather-traffic relationships through attention visualization.

Traditional labor market forecasting relies heavily on economic time-series data, often overlooking the "health-wealth" gradient that links population health to workforce participation. This study develops a machine learning framework integrating non-traditional health and social metrics to predict state-level employment density. Methods: We constructed a multi-source longitudinal dataset (2014–2024) combining Quarterly Census of Employment and Wages (QCEW) data with County Health Rankings. Using a time-aware split to evaluate performance across the COVID-19 structural break, we compared LASSO, Random Forest, and regularized XGBoost models, employing SHAP values for interpretability.Results: The tuned, regularized XGBoost model achieved strong out-of-sample performance (Test R ² = 0.800)}. A leakage-safe stacked Ridge ensemble yielded comparable performance (Test R ² = 0.827), while preserving the interpretability of the underlying tree model used for SHAP analysis.

This paper develops a Business Resilience Index (BRI) that measures county-level resilience to natural disasters at a county quarter frequency for the United States over 2014-2024. The index integrates high-frequency labor-market outcomes from the Quarterly Census of Employment and Wages with flood insurance policy information from FEMA, disaster damages from the NOAA Storm Events Database, and social and health determinants from County Health Rankings. Starting from a broad candidate set, we apply an interpretable feature-screening pipeline to retain 79 variables and then use principal component analysis to extract four orthogonal structural dimensions of resilience: Market Scale, Resilience Capacity, Safety and Risk Profile, and Workforce Dynamics. We construct a domain-weighted Strategic Index and benchmark it against data-driven and equal-weight alternatives, showing that county rankings are highly stable across weighting schemes. To evaluate whether the BRI aligns with recovery behavior under acute shocks, we implement a matched difference-in-differences event study around two major flood episodes, Texas 2015Q2 and North Carolina 2018Q3. Conditional on exposure intensity and matched comparability, higher pre-event BRI counties exhibit earlier stabilization and a stronger post-event employment path relative to lower BRI counties, with differences in magnitude and timing across cases. Overall, the BRI provides an interpretable, high-frequency baseline for identifying capacity constraints that may slow recovery and for supporting preparedness targeting and post-disaster monitoring.