Financial statement fraud remains a substantial risk in environments marked by weak regulatory oversight and information asymmetry. This study develops a decision-centric framework that integrates machine learning, explainable artificial intelligence, and decision curve analysis to improve fraud detection under severe class imbalance. Using 969 firm-year observations from 132 Mongolian firms (2013–2024), we evaluate 21 financial ratios with models including Random Forest, XGBoost, LightGBM, MLP, TabNet, and a Stacking Ensemble trained with SMOTE and class-weighted learning. Performance was assessed using PR-AUC, F1-score, Recall, and DeLong-based significance testing. The Stacking Ensemble achieved the strongest results (PR-AUC = 0.93; F1 = 0.83), outperforming both classical and modern baseline models. Interpretability analyses (SHAP, LIME, and counterfactual explanations) consistently identified leverage, profitability, and liquidity indicators as dominant drivers of fraud risk, supported by a SHAP Stability Index of 0.87. Decision curve analysis showed that calibrated thresholds improved decision efficiency by 7–9% and reduced over-audit costs by 3–4%, while an audit cost simulation estimated annual savings of 80–100 million MNT. Overall, the proposed ML–XAI–DCA framework offers a transparent, interpretable, and cost-efficient approach for enhancing fraud detection in emerging-market contexts with limited textual disclosures.

Gross Primary Productivity (GPP) in grassland ecosystems is a fundamental eco-biophysical indicator for assessing carbon cycling, grazing capacity, and ecosystem responses to climatic stress. However, robust estimation of GPP in arid and semi-arid rangelands remains challenging because of pronounced spatial heterogeneity, strong climate variability, and inherent uncertainties associated with remotely sensed observations. Together, these factors constrain both modeling performance and out-of-sample generalization beyond the training domain. In this dryland grassland context, this study compares the performance of machine learning (ML) models for grassland GPP proxy-based characterization, downscaling, and predictive agreement using a multivariate dataset that integrates Sentinel-2-derived spectral and phenological features, a Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived GPP proxy, and complementary climatic and geographic information. Pixel-level observations spanning multiple years are analyzed, with ordinary linear regression used as a baseline benchmark and ensemble decision-tree models, including Random Forest, Gradient Boosting, and Histogram-based Gradient Boosting (HGB), compared. Instead of relying solely on random cross-validation, model performance is systematically assessed using a combination of spatially structured validation and a leave-one-year-out scheme to explicitly examine spatial and temporal generalization. The results indicate that ensemble tree-based models outperform linear approaches, with the HGB model showing the strongest agreement with the MODIS-derived GPP proxy (R² = 0.95, RMSE = 0.035 on the test set) and maintaining stable performance across spatial and temporal validations (R² = 0.86–0.96 across years). Taken together, the findings demonstrate that integrating multi-source remote sensing data with climatic information within a rigorous validation framework enables a more reliable assessment of model generalization and gap-filling consistency with respect to a remote-sensing-based proxy target, rather than an absolute validation against ground-based measurements, thereby supporting sustainability-relevant monitoring of arid grassland ecosystems.

Degraded rangelands in semi-arid pastoral systems are widely associated with declining vegetation, soil carbon loss, and worsening household livelihoods. However, the mechanisms linking rangeland degradation to household income remain poorly understood, particularly in a panel-data context. This study examines how rangeland condition, vegetation dynamics, and livestock by-product underutilization are related to household income in Öndörshireet Soum, Töv Aimag, Mongolia. The analysis is based on a multi-source panel dataset covering 2018 to 2024, combining Sentinel-2 NDVI time series, soil organic carbon measurements from 120 permanent plots, and a five-wave survey of 114 households. The results indicate widespread and persistent degradation. Nearly 90 percent of monitored plots are at least moderately degraded; NDVI shows a steady decline over time; and average soil carbon levels remain well below those observed at a managed reference site. Over the same period, real household income declined despite a gradual increase in herd size. Econometric estimates show that vegetation condition is positively associated with income, whereas higher levels of by-product waste are associated with lower income, even after accounting for precipitation variability. The interaction results further suggest that the benefits of herd expansion weaken when production losses remains high. Taken together, these findings indicate that ecological decline and low value capture from livestock operate simultaneously to constrain pastoral livelihoods. Improvements in pasture condition alone appear insufficient to offset these pressures when a substantial share of livestock value is not recovered. While the results offer useful insights for rangeland policy, further evidence from multiple sites would be needed to assess causality and the extent to which these patterns apply beyond a single soum.

This study examines the relationship between firms’ financial adaptability and performance during periods of macroeconomic stress. Using panel data on companies listed on the Mongolian Stock Exchange from 2015 to 2024, the analysis measures financial adaptability through a Firm Adaptability Index (FAI) constructed from observable indicators of liquidity, coverage capacity, and asset-use efficiency. The index is constructed using principal component analysis (PCA) to avoid arbitrary equal-weighting assumptions, and the debt ratio is deliberately excluded to prevent multicollinearity with the leverage control variable used in the regression models. The empirical framework primarily relies on panel regression models with interaction terms, supplemented by a DID-style comparison and an event-study-based diagnostic. The validity of the quasi-experimental design is confirmed by a formal parallel-trend test and placebo checks using artificial shock dates. The findings do not support the view that financial adaptability exerts a uniformly strong and stable direct effect on firm performance across all conditions. Instead, its empirical relevance becomes more visible when macroeconomic conditions worsen. In particular, the interaction result related to interest rates suggests that firms with higher levels of financial adaptability tend to exhibit less pronounced profitability sensitivity to financing cost pressure. Additional analyses point to short-term liquidity buffers as a plausible channel and show that the strength of this relationship varies by firm size and sectoral characteristics. This study contributes to the literature by bringing together the related concepts of financial flexibility, organizational resilience, dynamic capabilities, and strategic adaptability within a firm-level empirical setting. It also proposes a practical way to measure financial adaptability not through a single proxy, but through a composite index that integrates several observable financial dimensions. Overall, the evidence suggests that financial adaptability is better understood not as a constant determinant of profitability, but as an internal capability whose relevance becomes more apparent under conditions of heightened uncertainty.

International tourism plays an important role in the global service economy, contributing to trade, employment, and regional development. For this reason, identifying the factors that influence tourist flows is an important issue for tourism policy, market strategy, and infrastructure planning. A large body of research has applied gravity models to analyze tourism flows between countries. While this approach provides a clear economic interpretation, it is usually based on linear specifications and may therefore capture only part of the relationships present in tourism data. This study examines the economic and geographic determinants of international tourism flows to Mongolia using a framework that combines a traditional gravity model with machine learning techniques. Mongolia serves as an instructive empirical setting, a landlocked, geographically peripheral destination whose inbound demand determinants have received limited systematic empirical attention. The analysis uses panel data for 27 origin countries covering the period from 2000 to 2024. In the first stage, a gravity model is estimated to assess how tourism flows relate to economic size and geographic distance. The results show that tourism flows tend to increase with the economic size of origin and destination countries, while greater geographical distance is associated with lower tourism flows. The estimated distance elasticity ranges from approximately −1.85 to −2.10 across model specifications, which is larger in absolute terms than the values typically reported in cross-country studies. This result is consistent with the relatively high travel cost barriers associated with Mongolia’s geographic location. These findings are consistent with the distance decay relationship commonly reported in the tourism literature. In the second stage, machine learning algorithms, including Random Forest, LightGBM, and XGBoost, are used as complementary interpretive instruments rather than forecasting tools to explore possible nonlinear relationships among the explanatory variables. To make the results more interpretable, the contribution of individual variables is examined using SHAP (Shapley Additive Explanations). The machine learning results indicate that some relationships in tourism demand may be nonlinear and not fully captured by the linear gravity specification. Specifically, distance sensitivity is approximately 6.5 times greater in nearby markets than in long-haul markets, with a structural inflexion at around 5700 km. Further analysis suggests that the influence of geographical distance is not uniform across all markets. In particular, tourism flows originating from middle-income countries appear to be more sensitive to increases in travel distance than those from higher-income countries. Overall, the findings indicate that economic size and geographical distance remain key determinants of international tourism flows to Mongolia. At the same time, the use of machine learning methods provides additional insight into potential nonlinear patterns in tourism demand. By combining econometric modelling with explainable machine learning techniques, the study offers an integrated analytical perspective for examining international tourism flows at geographically peripheral destinations where standard gravity assumptions may be insufficient.

Machine learning-based credit risk models are commonly assessed using discrimination metrics alone. Such evaluation, however, does not fully capture economic consequences, temporal deployment conditions, or changes in the underlying risk environment. This study develops a decision-aligned, temporally consistent evaluation framework for real-world deployment. Using loan-level data, model performance is examined under a rolling forward validation scheme. A coverage-based alert policy is implemented to reflect operational resource constraints. Predictive discrimination is measured using PR-AUC, while economic performance is evaluated through a cost-sensitive saving function. The false-negative cost parameter (λ) is varied between 5 and 25 to assess sensitivity. Performance is also compared across high- and low-default regimes, and alternative alert budgets of 5%, 10%, and 20% are considered to examine policy stability. The results indicate no systematic decline in PR-AUC over time. Changes in λ do not alter predictive ranking, although economic returns scale proportionally with the cost parameter. Economic gains are higher in high-default regimes, yet no structural deterioration is observed in low-default periods. Increasing coverage improves recall but reduces economic benefit due to higher false-positive costs. To consolidate these stability dimensions, the Unified Policy Stability Index (UPSI) is proposed as a composite measure integrating predictive variability, economic volatility, and regime differences. The index indicates sustained overall stability during the study period. The findings suggest that credit risk model evaluation should extend beyond accuracy-centered metrics and incorporate decision consistency, temporal robustness, and policy stability within a deployment-oriented framework.