Machine Learning for Credit Scoring: Improving Logistic Regression with Non Linear Decision Tree Effects

Citations

Cited by:

1. Dangxing Chen & Weicheng Ye & Jiahui Ye, 2022. "Interpretable Selective Learning in Credit Risk," Papers 2209.10127, arXiv.org.
2. Dangxing Chen & Weicheng Ye, 2022. "Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance," Papers 2209.10082, arXiv.org, revised Jul 2024.
3. Emmanuel Flachaire & Gilles Hacheme & Sullivan Hu'e & S'ebastien Laurent, 2022. "GAM(L)A: An econometric model for interpretable Machine Learning," Papers 2203.11691, arXiv.org.
4. 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.
5. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
6. 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.
7. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
8. Dangxing Chen & Weicheng Ye, 2022. "Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring," Papers 2209.10070, arXiv.org.
9. Dangxing Chen & Luyao Zhang, 2023. "Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance," Papers 2301.07060, arXiv.org.
10. Ahmad El Majzoub & Fethi A. Rabhi & Walayat Hussain, 2023. "Evaluating interpretable machine learning predictions for cryptocurrencies," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 30(3), pages 137-149, July.
11. 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.
12. John Martin & Sona Taheri & Mali Abdollahian, 2024. "Optimizing Ensemble Learning to Reduce Misclassification Costs in Credit Risk Scorecards," Mathematics, MDPI, vol. 12(6), pages 1-15, March.
13. Sun, Weixin & Zhang, Xuantao & Li, Minghao & Wang, Yong, 2023. "Interpretable high-stakes decision support system for credit default forecasting," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
14. Al-Amin Abba Dabo & Amin Hosseinian-Far, 2023. "An Integrated Methodology for Enhancing Reverse Logistics Flows and Networks in Industry 5.0," Logistics, MDPI, vol. 7(4), pages 1-26, December.
15. 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).
16. Wei Jie Yeo & Wihan van der Heever & Rui Mao & Erik Cambria & Ranjan Satapathy & Gianmarco Mengaldo, 2023. "A Comprehensive Review on Financial Explainable AI," Papers 2309.11960, arXiv.org.
17. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
18. Kraus, Mathias & Tschernutter, Daniel & Weinzierl, Sven & Zschech, Patrick, 2024. "Interpretable generalized additive neural networks," European Journal of Operational Research, Elsevier, vol. 317(2), pages 303-316.
19. Sullivan Hué, 2022. "GAM(L)A: An econometric model for interpretable machine learning," French Stata Users' Group Meetings 2022 19, Stata Users Group.
20. Simone Narizzano & Marco Orlandi & Antonio Scalia, 2024. "The Bank of Italy’s statistical model for the credit assessment of non-financial firms," Temi di discussione (Economic working papers) 53, Bank of Italy, Economic Research and International Relations Area.
21. 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.
22. Margherita Doria & Elisa Luciano & Patrizia Semeraro, 2022. "Machine learning techniques in joint default assessment," Papers 2205.01524, arXiv.org, revised Sep 2023.
23. Sunghyon Kyeong & Daehee Kim & Jinho Shin, 2021. "Can System Log Data Enhance the Performance of Credit Scoring?—Evidence from an Internet Bank in Korea," Sustainability, MDPI, vol. 14(1), pages 1-12, December.
24. Flavio Bazzana & Marco Bee & Ahmed Almustfa Hussin Adam Khatir, 2024. "Machine learning techniques for default prediction: an application to small Italian companies," Risk Management, Palgrave Macmillan, vol. 26(1), pages 1-23, February.
25. Shi, Yong & Qu, Yi & Chen, Zhensong & Mi, Yunlong & Wang, Yunong, 2024. "Improved credit risk prediction based on an integrated graph representation learning approach with graph transformation," European Journal of Operational Research, Elsevier, vol. 315(2), pages 786-801.
26. Li, Zhe & Liang, Shuguang & Pan, Xianyou & Pang, Meng, 2024. "Credit risk prediction based on loan profit: Evidence from Chinese SMEs," Research in International Business and Finance, Elsevier, vol. 67(PA).
27. Miao Zhu & Ben-Chang Shia & Meng Su & Jialin Liu, 2024. "Consumer Default Risk Portrait: An Intelligent Management Framework of Online Consumer Credit Default Risk," Mathematics, MDPI, vol. 12(10), pages 1-19, May.
28. Yusheng Li & Mengyi Sha, 2024. "Two‐stage credit risk prediction framework based on three‐way decisions with automatic threshold learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1263-1277, August.
29. Piccialli, Veronica & Romero Morales, Dolores & Salvatore, Cecilia, 2024. "Supervised feature compression based on counterfactual analysis," European Journal of Operational Research, Elsevier, vol. 317(2), pages 273-285.
30. Zhou, Ying & Shen, Long & Ballester, Laura, 2023. "A two-stage credit scoring model based on random forest: Evidence from Chinese small firms," International Review of Financial Analysis, Elsevier, vol. 89(C).
31. Chi, Guotai & Dong, Bingjie & Zhou, Ying & Jin, Peng, 2024. "Long-horizon predictions of credit default with inconsistent customers," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
32. Dangxing Chen, 2022. "Two-stage Modeling for Prediction with Confidence," Papers 2209.08848, arXiv.org.
33. Katsafados, Apostolos G. & Leledakis, George N. & Pyrgiotakis, Emmanouil G. & Androutsopoulos, Ion & Fergadiotis, Manos, 2024. "Machine learning in bank merger prediction: A text-based approach," European Journal of Operational Research, Elsevier, vol. 312(2), pages 783-797.
    • Katsafados, Apostolos G. & Leledakis, George N. & Pyrgiotakis, Emmanouil G. & Androutsopoulos, Ion & Fergadiotis, Manos, 2021. "Machine Learning in U.S. Bank Merger Prediction: A Text-Based Approach," MPRA Paper 108272, University Library of Munich, Germany.
34. Chen, Yujia & Calabrese, Raffaella & Martin-Barragan, Belen, 2024. "Interpretable machine learning for imbalanced credit scoring datasets," European Journal of Operational Research, Elsevier, vol. 312(1), pages 357-372.
35. Jomark Pablo Noriega & Luis Antonio Rivera & José Alfredo Herrera, 2023. "Machine Learning for Credit Risk Prediction: A Systematic Literature Review," Data, MDPI, vol. 8(11), pages 1-17, November.
36. Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).
37. Kellner, Ralf & Nagl, Maximilian & Rösch, Daniel, 2022. "Opening the black box – Quantile neural networks for loss given default prediction," Journal of Banking & Finance, Elsevier, vol. 134(C).