Credit Risk

Credit risk assessment aims to predict the likelihood of borrowers defaulting on loans, informing lending decisions and mitigating financial losses. Current research heavily emphasizes machine learning, employing diverse architectures like graph neural networks (to capture borrower relationships), tree-boosting models (for handling non-linearities and spatio-temporal effects), and transformers (for analyzing textual data), often enhanced by techniques like attention mechanisms and feature engineering. These advancements improve prediction accuracy and offer insights into risk factors, impacting both financial institutions' profitability and the development of more robust risk management strategies.