A Multimodal Dataset for Bank Failure Prediction in Vietnam

This dataset integrates textual, financial, and macroeconomic indicators to support research on bank failure prediction and financial distress forecasting in Vietnam. It includes financial news from the BKAI News Corpus Dataset (2009–2023) and financial crisis data from "A Dataset for the Vietnamese Banking System (2002–2021)" (Tu Le et al., 2022), covering crisis-related events such as restructuring, special control, mergers, and acquisitions.The dataset was systematically processed to align textual and financial data by time and entity (banks). Bank mentions in news articles were identified using common spelling variations and abbreviations. Macroeconomic and banking sector indicators were collected from the World Bank and the General Statistics Office of Vietnam. A categorical variable indicates whether a bank was classified as weak based on official State Bank of Vietnam reports.This dataset is suitable for time-series analysis and machine learning applications, enabling researchers to explore relationships between financial narratives, macroeconomic factors, and banking crises. It facilitates the development of predictive models, including deep learning architectures, for early warning systems in financial risk management.Proper attribution to BKAI, Tu Le et al. (2022), the World Bank, and the General Statistics Office of Vietnam is required when using this dataset.Keywords: Bank failure prediction, multimodal dataset, financial news analysis, CAMELS rating, time-series analysis, machine learning, deep learning, Vietnam banking system.