aspg-37-4443-1792223492 20261017075132 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Financial Technology and Innovation FinTechI 2836-5372 2026 6 1 Asifa Iqbal School of International Languages, Zhengzhou University, Henan, China Shahid Mahmood School of Finance and Economics, Jiangsu University, Zhenjiang, People’s Republic of China The quick expansion of digital lending platforms has, you know, put borrower default risk right at the center of attention for FinTech investors, regulators, and researchers too. Even though machine learning is now quite often used for credit risk prediction, many models still focus mostly on predictive accuracy rather than interpretability, and also, single-algorithm setups don’t really take advantage of the extra value you’d get from using several different classifiers together. In this work, we put forward the Stacked Ensemble Credit Risk Framework (SECRF). It’s basically a multi layer architecture that merges base learners—logistic regression, random forest, and gradient boosted trees—then routes them to a meta learner, plus an added layer for SHAP based post hoc interpretation and calibration checkups. Across several evaluation perspectives, SECRF shows solid and fairly stable results, and in cross validated experiments the area under the ROC curve stays higher than what single model alternatives typically deliver. When it comes to what actually drives defaults, loan grade along with interest rate emerge as the main predictors under all estimation setups, which lines up with how credit risk is theoretically priced. At the borrower level, the FICO score and debt-to-income ratio also matter in a clear way, while macroeconomic proxies add extra explanatory signals beyond what individual loan descriptors already capture. The interpretability layer is there on purpose so the approach stays compatible with regulatory requirements, and so credit officers get insights they can act on. Finally, the calibration analysis indicates that SECRF outputs are dependable, especially for risk based pricing and provisioning decisions. 2026 35 41 4443 https://creativecommons.org/licenses/by/4.0/ 10.54216/FinTech-I.060105 https://americaspg.com/journal/37/article/4443 https://americaspg.com/storage/articles/manuscripts/01KXPYTBWD4R5MNXVQYGPD50JQ.pdf