Transparency, auditability, and explainability of machine learning models in credit scoring
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DOI: 10.1080/01605682.2021.1922098
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Cited by:
- Yang, Fan & Abedin, Mohammad Zoynul & Hajek, Petr, 2024. "An explainable federated learning and blockchain-based secure credit modeling method," European Journal of Operational Research, Elsevier, vol. 317(2), pages 449-467.
- Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2023. "Explainable FinTech lending," Journal of Economics and Business, Elsevier, vol. 125.
- Nadia Ayed & Khemaies Bougatef, 2024. "Performance Assessment of Logistic Regression (LR), Artificial Neural Network (ANN), Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy System (ANFIS) in Predicting Default Probability: The Case of," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1803-1835, September.
- Gero Szepannek, 2022. "An Overview on the Landscape of R Packages for Open Source Scorecard Modelling," Risks, MDPI, vol. 10(3), pages 1-33, March.
- Jiaming Liu & Xuemei Zhang & Haitao Xiong, 2024. "Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1625-1660, August.
- Tigges, Maximilian & Mestwerdt, Sönke & Tschirner, Sebastian & Mauer, René, 2024. "Who gets the money? A qualitative analysis of fintech lending and credit scoring through the adoption of AI and alternative data," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
- Ghosh, Indranil & Jana, Rabin K. & David, Roubaud & Grebinevych, Oksana & Wanke, Peter & Tan, Yong, 2024. "Modelling financial stress during the COVID-19 pandemic: Prediction and deeper insights," International Review of Economics & Finance, Elsevier, vol. 91(C), pages 680-698.
- Yang Liu & Fei Huang & Lili Ma & Qingguo Zeng & Jiale Shi, 2024. "Credit scoring prediction leveraging interpretable ensemble learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 286-308, March.
- Andrés Alonso & José Manuel Carbó, 2022. "Accuracy of explanations of machine learning models for credit decisions," Working Papers 2222, Banco de España.
- Luis J. Mena & Vicente García & Vanessa G. Félix & Rodolfo Ostos & Rafael Martínez-Peláez & Alberto Ochoa-Brust & Pablo Velarde-Alvarado, 2024. "Enhancing financial risk prediction with symbolic classifiers: addressing class imbalance and the accuracy–interpretability trade–off," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
- Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).
- Chen, Pengfang & Zhang, Xiaoqiang & Gao, Dongsheng, 2024. "Preference heterogeneity analysis on train choice behaviour of high-speed railway passengers: A case study in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 188(C).
- Janssens, Bram & Schetgen, Lisa & Bogaert, Matthias & Meire, Matthijs & Van den Poel, Dirk, 2024. "360 Degrees rumor detection: When explanations got some explaining to do," European Journal of Operational Research, Elsevier, vol. 317(2), pages 366-381.
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