AI-Driven Credit Risk Assessment for Bangladesh SMEs: Dataset and Analysis Code

Dataset and analysis code for "Structured Data Suffices: Machine Learning for SME Credit Risk Assessment in an Emerging Market", submitted to Financial Innovation (Springer Nature). The repository contains 1,200 SME loan applications from Bangladesh with structured features (numeric, categorical) and text features (business descriptions, loan narratives). The analysis demonstrates that simple logistic regression on structured features alone achieves 0.900 ROC-AUC in 5-fold cross-validation, with a train–test gap of only 0.017. Text features derived via TF-IDF add negligible predictive value (