Volume 6 • Issue 1 • PP: 35–41 • 2026
FinTech Lending Default Risk: A Stacked Ensemble Credit Risk Framework with SHAP-Based Interpretability
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© 2026 The Author(s). Published by ASPG. This article is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
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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.
Keywords
References
[1] X. Jia, “FinTech penetration, charter value, and bank risk-taking,” Journal of Banking & Finance, vol. 161, p. 107111, 2024.
[2] R. Leite, L. Mendes, and E. Camelo, “Innovating microcredit: How FinTechs change the field,” Journal of Economics and Business, vol. 128, p. 106158, 2024.
[3] B. Koranteng and K. You, “FinTech and financial stability: Evidence from spatial analysis for 25 countries,” Journal of International Financial Markets, Institutions and Money, vol. 93, 2024.
[4] D. B. Vukovi´c, M. K. Hassan, B. Kwakye, A. Febtinugraini, and M. Shakib, “Does FinTech matter for financial inclusion and financial stability in BRICS markets?” Emerging Markets Review, vol. 61, p. 101164, 2024.
[5] X. Tian, Z. Tian, S. F. A. Khatib, and Y. Wang, “Machine learning in internet financial risk management: A systematic literature review,” PLOS ONE, vol. 19, no. 4, p. e0300195, 2024.
[6] J. Alvi, I. Arif, and K. Nizam, “Advancing financial resilience: A systematic review of default prediction models and future directions in credit risk management,” Heliyon, vol. 10, no. 21, p. e39770, 2024.
[7] J. ˇ Cerneviˇcien˙e and A. Kabašinskas, “Explainable artificial intelligence (XAI) in finance: A systematic literature review,” Artificial Intelligence Review, vol. 57, no. 8, p. 216, 2024.
[8] G. F. Bone-Winkel and F. Reichenbach, “Improving credit risk assessment in P2P lending with explainable machine learning survival analysis,” Digital Finance, vol. 6, no. 3, pp. 501–542, 2024.
[9] Q. Xu, C. Liu, J. Luo, and F. Liu, “Using machine learning to investigate the determinants of loan default in P2P lending: Are there differences between before and during COVID-19?” Pacific-Basin Finance Journal, vol. 88, 2024.
[10] E. Baumöhl, Š. Lyócsa, and P. Vašaniˇcová, “Macroeconomic environment and the future performance of loans: Evidence from three peer-to-peer platforms,” International Review of Financial Analysis, vol. 95, p. 103416, 2024.
[11] A. Nigmonov, S. Shams, and P. Urbonas, “Estimating probability of default via delinquencies? Evidence from European P2P lending market,” Global Finance Journal, vol. 63, p. 101050, 2024.
[12] Y. Liu, L. J. Baals, J. Osterrieder, and B. Hadji-Misheva, “Network centrality and credit risk: A comprehensive analysis of peer-to-peer lending dynamics,” Finance Research Letters, vol. 63, p. 105308, 2024.
[13] Y. Liu, L. J. Baals, J. Osterrieder, and B. Hadji-Misheva, “Leveraging network topology for credit risk assessment in P2P lending: A comparative study under the lens of machine learning,” Expert Systems with Applications, 2024.
[14] A. Andrikopoulos and X. Dassiou, “Bank market power and performance of financial technology firms,” International Journal of Finance & Economics, vol. 29, no. 1, pp. 1141–1156, 2024.
[15] F. Zhou, A. Chang, and J. Shi, “How the economic policy uncertainty (EPU) impacts FinTech: The implication of P2P lending markets,” Finance Research Letters, vol. 70, 2024.
[16] A. Nigmonov, S. Shams, and K. Alam, “Liquidity risk in FinTech lending: Early impact of the COVID-19 pandemic on the P2P lending market,” Emerging Markets Review, vol. 58, 2024.
[17] C. M. Corrales, L. A. O. González, and P. D. Santomil, “Estimation of default and pricing for invoice trading (P2B) on crowdlending platforms,” Financial Innovation, vol. 10, p. 109, 2024.
[18] R. Kräussl, Z. Kräussl, J. Pollet, and K. Rinne, “The performance of marketplace lenders,” Journal of Banking & Finance, vol. 162, 2024.
[19] X. Tian, W. Zhang, and S. F. A. Khatib, “Machine learning powered financial credit scoring: a systematic literature review,” Artificial Intelligence Review, vol. 58, p. 1416, 2025.
[20] X. Jia and K. Kanagaretnam, “Digital inclusion and financial inclusion: Evidence from peer-to peer lending,” Journal of Business Ethics, 2024.
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