Financial risk early warning systems are essential for proactive risk management in volatile markets, particularly for emerging economies such as China. This study develops a hybrid deep learning model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated...

The rapid expansion of small and medium enterprise (SME) lending has intensified the need for accurate and interpretable credit risk forecasting. Financial institutions must anticipate potential business loan delinquency to maintain portfolio stability and meet regulatory standards. This study...

Landslides claim more than 4,000 lives annually and lead to approximately US$20 billion in economic losses. However, landslide hazard, risk assessment, and early warning systems remain constrained by fragmented, inconsistent, and incomplete data. This study addresses the global data gap by...

This study presents a novel Early Warning System for monitoring the credit risk of commercial customers at a large international bank headquartered in the Netherlands. Traditional early warning methods often rely on backward-looking indicators such as probability of default or loss given...

This paper introduces a novel application of Growth-at-Risk (GaR) as an early warning system (EWS) for predicting recessions. By transforming GaR into a classification model, we assess its ability to signal economic downturns across 11 Central and Eastern European (CEE) economies from 2005 to...

This study predicts bankruptcy among Indian firms using artificial intelligence-machine learning (AI-ML) methods, demonstrating their superior performance over traditional statistical models. Addressing class imbalance through oversampling techniques such as the synthetic minority oversampling...