ESG management has become the most prominent topic this year as the worldwide business environment of companies changes. ESG management is not an option to raise a company’s reputation but a factor that significantly affects corporate sustainability and corporate value in the long run as well as can be seen as an essential requirement for a company’s survival. As this trend is essential in evaluating corporate sustainability, ESG management is an inevitable global issue. This study aims to develop an index for ESG, a key indicator of corporate sustainability, targeting 49 companies that issue ‘sustainability reports’ among the top 150 asset-ranked companies. In addition, companies were divided into high and low groups based on the ESG index, and each group attempted to study the effect of economic responsibility among corporate social responsibility on financial performance. As a result, out of 49 companies, the high group was divided into 17 companies, and the low group was split into 14 companies. It was found that only the low group had a significantly positive (+) effect on the relationship between corporate economic responsibility and financial performance. This study can be used as a reference data for ESG research by developing an ESG index related to corporate sustainability. It is meaningful to confirm the management paradigm for corporate social responsibility and the importance of financial performance through comparison.

Background: Financial institutions face significant challenges in predicting loan defaults, which directly impact the non-performing loan (NPL) rate. Incorrect predictions can lead to misinformed decisions and substantial financial losses. Objectives: This study aims to enhance default prediction by employing advanced ensemble learning techniques in machine learning and deep learning. Methods/Approach: Instead of relying on transformation, fine-tuning, or single algorithm models, this research focuses on combining multiple models using voting and stacking techniques, particularly highlighting a stacking model combining Light Gradient Boosting Machine (LGBM) and Artificial Neural Networks (ANN). Results: The ensemble learning methods, especially the LGBM-LSTM and XGB-LSTM stacking models, showed higher precision in identifying borrowers who defaulted, while the LGBM-LSTM and XGB-LSTM voting models excelled in recall and achieved an F1-score 0.1% higher. Both the stacking and voting models attained AUC values close to 90%, indicating strong overall classification performance. Conclusions: The findings not only contribute to the fields of lending and peer-to-peer financial operations but also offer crucial insights that aid financial organizations in making well-informed decisions regarding loan processing and management.

Customer value analysis is a critical component in formulating effective marketing and customer relationship management (CRM) strategies, especially in sectors where client movement and strong competition are prevalent A key element of this process lies in enhancing customer retention rates, as retaining existing clients is typically more cost-effective than acquiring new ones and directly contributes to improving overall profitability. In today’s banking environment, where customers can choose from a broad range of financial services, customer churn has become a critical challenge. Predicting and understanding attrition enables financial institutions to implement proactive and targeted interventions to protect market share and strengthen customer loyalty. This study analyzes a real-world dataset comprising 10,127 customer records from a commercial bank, where only 1,627 entries correspond to churned customers, thereby presenting a notable class imbalance problem. To address this, several data balancing techniques were applied, including class-weight adjustment, SMOTE, SMOTE-Tomek Links, and SMOTE-ENN. Multiple machine learning models - Support Vector Machine, Random Forest, Decision Tree, Logistic Regression, AdaBoost - were evaluated to identify the most effective approach for churn prediction. The Random Forest model achieved an 86% F1-score after applying SMOTE-Tomek Links, demonstrating strong predictive capability. The key contribution of this study lies in integrating advanced resampling techniques with ensemble learning and customer behavioral insights to improve churn prediction performance and support data-driven retention strategies in the banking sector.

Traditional linear models in urban informatics struggle to capture the complex, non-linear spatial non-stationarity inherent in metropolitan housing markets. To overcome these constraints, this study introduces a data-driven computational framework integrating a Spatio-Temporal Graph Convolutional Network (STGCN) with gradient-based Explainable Artificial Intelligence (XAI) and Geographically Weighted Regression (GWR). This framework is empirically tested using 217,598 apartment transactions in Busan, the Republic of Korea, augmented with high-resolution micro-demographic grids and Digital Elevation Model (DEM) topographical data. Utilizing unsupervised K-Means clustering, the region is spatially stratified into a dense Urban Core and a dispersed Suburban Periphery. The STGCN demonstrates overwhelming predictive superiority ($R^2=0.802$) over the traditional Spatial Error Model ($R^2=0.437$). Crucially, gradient-based XAI and localized GWR coefficients successfully unspool the deep learning “black box,” visualizing hyper-localized economic realities that global linear models obscure. The analysis expose stark regional market segmentation driven by environmental topography, mathematically quantifying non-linear dynamics such as coastal high-floor premiums, severe mountainous altitude penalties, and latent urban reconstruction premiums. Ultimately, this research bridges the gap between predictive computational power and spatial economic interpretability, offering a robust informatics framework for equitable urban planning.

This study examined public library use intensity across seven metropolitan cities in South Korea, considering spatial dependence and the surrounding urban environment. From 485 public libraries, model-specific analytical samples were constructed for visits per m², loans per m², and borrowers per m². Urban facility variables within 1- and 2-km catchments were used as predictors, and academy density was corrected using education office records geocoded using the Kakao Local API. Moran’s I and Anselin–Florax Lagrange Multiplier diagnostics were used to assess spatial dependence among libraries and compare ordinary least squares, spatial autoregressive (SAR), and spatial error model results. Moran’s I indicated significant spatial clustering across all three outcome variables. SAR results revealed that the indirect component accounted for 46.3% and 40.8% of the total academy effect on visits and loans per m², respectively, representing model-based indirect effects rather than direct evidence of user movement. A supplementary negative binomial model using library floor area as an exposure did not corroborate the academy-density association, indicating that this finding is specific to the area-normalized model rather than scale-invariant. These findings highlight the importance of considering spatial dependence and urban facility contexts when interpreting public library use intensity at the regional scale.