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Machine learning explainability in finance: an application to default risk analysis

Citations

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Cited by:

  1. 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.
  2. Zhou Lu & Zhuyao Zhuo, 2021. "Modelling of Chinese corporate bond default – A machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(5), pages 6147-6191, December.
  3. Lu, Xuefei & Calabrese, Raffaella, 2023. "The Cohort Shapley value to measure fairness in financing small and medium enterprises in the UK," Finance Research Letters, Elsevier, vol. 58(PC).
  4. Starosta, Wojciech, 2021. "Loss given default decomposition using mixture distributions of in-default events," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1187-1199.
  5. Kim Long Tran & Hoang Anh Le & Thanh Hien Nguyen & Duc Trung Nguyen, 2022. "Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam," Data, MDPI, vol. 7(11), pages 1-12, November.
  6. Sullivan Hué, 2022. "GAM(L)A: An econometric model for interpretable machine learning," French Stata Users' Group Meetings 2022 19, Stata Users Group.
  7. Susanna Levantesi & Giulia Zacchia, 2021. "Machine Learning and Financial Literacy: An Exploration of Factors Influencing Financial Knowledge in Italy," JRFM, MDPI, vol. 14(3), pages 1-21, March.
  8. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
  9. Niklas Bussmann & Paolo Giudici & Dimitri Marinelli & Jochen Papenbrock, 2021. "Explainable Machine Learning in Credit Risk Management," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 203-216, January.
  10. Branka Hadji Misheva & Joerg Osterrieder & Ali Hirsa & Onkar Kulkarni & Stephen Fung Lin, 2021. "Explainable AI in Credit Risk Management," Papers 2103.00949, arXiv.org.
  11. Osama Wagdi & Yasmeen Tarek, 2022. "The Integration of Big Data and Artificial Neural Networks for Enhancing Credit Risk Scoring in Emerging Markets: Evidence from Egypt," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 14(2), pages 1-32, February.
  12. Mustafa, Andy Ali & Lin, Ching-Yang & Kakinaka, Makoto, 2022. "Detecting market pattern changes: A machine learning approach," Finance Research Letters, Elsevier, vol. 47(PA).
  13. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
  14. Yili Chen & Congdong Li & Han Wang, 2022. "Big Data and Predictive Analytics for Business Intelligence: A Bibliographic Study (2000–2021)," Forecasting, MDPI, vol. 4(4), pages 1-20, September.
  15. Kumar, Rishabh & Koshiyama, Adriano & da Costa, Kleyton & Kingsman, Nigel & Tewarrie, Marvin & Kazim, Emre & Roy, Arunita & Treleaven, Philip & Lovell, Zac, 2023. "Deep learning model fragility and implications for financial stability and regulation," Bank of England working papers 1038, Bank of England.
  16. Andrés Alonso Robisco & José Manuel Carbó Martínez, 2022. "Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.
  17. 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.
  18. Giudici, Paolo & Gramegna, Alex & Raffinetti, Emanuela, 2023. "Machine Learning Classification Model Comparison," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
  19. Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2023. "Explainable FinTech lending," Journal of Economics and Business, Elsevier, vol. 125.
  20. Georges, Christophre & Pereira, Javier, 2021. "Market stability with machine learning agents," Journal of Economic Dynamics and Control, Elsevier, vol. 122(C).
  21. 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.
  22. Nicola Branzoli & Ilaria Supino, 2020. "FinTech credit: a critical review of empirical research," Questioni di Economia e Finanza (Occasional Papers) 549, Bank of Italy, Economic Research and International Relations Area.
  23. Zhiyu Cao & Zihan Chen & Prerna Mishra & Hamed Amini & Zachary Feinstein, 2023. "Modeling Inverse Demand Function with Explainable Dual Neural Networks," Papers 2307.14322, arXiv.org, revised Oct 2023.
  24. Giudici, Paolo & Raffinetti, Emanuela, 2023. "SAFE Artificial Intelligence in finance," Finance Research Letters, Elsevier, vol. 56(C).
  25. Ishan S. Kapoor & Sunint Bindra & Monika Bhatia, 2023. "Machine Learning in Accounting & Finance: Architecture, Scope & Challenges," International Journal of Business and Management, Canadian Center of Science and Education, vol. 17(5), pages 1-13, February.
  26. Johann Pfitzinger, 2021. "An Interpretable Neural Network for Parameter Inference," Papers 2106.05536, arXiv.org.
  27. Tiago E. Pratas & Filipe R. Ramos & Lihki Rubio, 2023. "Forecasting bitcoin volatility: exploring the potential of deep learning," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(2), pages 285-305, June.
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