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

Citations

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Cited by:

  1. Giudici, Paolo & Piergallini, Alessandro & Recchioni, Maria Cristina & Raffinetti, Emanuela, 2024. "Explainable Artificial Intelligence methods for financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 655(C).
  2. Afsaneh Azimi & Navid Khaledian, 2025. "Multi-stage mortgage default prediction using ensemble machine learning: a comparative framework," Digital Finance, Springer, vol. 7(4), pages 1093-1118, December.
  3. 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.
  4. Xiang Ao & Jingxuan Zhang & Xinyu Zhao, 2026. "Dynamic Forecasting and Temporal Feature Evolution of Stock Repurchases in Listed Companies Using Attention-Based Deep Temporal Networks," Papers 2604.09650, arXiv.org.
  5. 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.
  6. Said Magomedov & Dean Fantazzini, 2025. "Modeling and Forecasting the Probability of Crypto-Exchange Closures: A Forecast Combination Approach," JRFM, MDPI, vol. 18(2), pages 1-20, January.
  7. 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.
  8. Rishabh Kumar & Adriano Koshiyama & Kleyton da Costa & Nigel Kingsman & Marvin Tewarrie & Emre Kazim & Arunita Roy & Philip Treleaven & Zac Lovell, 2023. "Deep learning model fragility and implications for financial stability and regulation," Bank of England Staff Working Paper series 1038, Bank of England.
  9. 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.
  10. Giudici, Paolo & Raffinetti, Emanuela, 2023. "SAFE Artificial Intelligence in finance," Finance Research Letters, Elsevier, vol. 56(C).
  11. 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.
  12. Xin Xu & Tao Ye & Jieying Gao & Dongxiao Chu, 2025. "The effect of green, supply chain factors in predicting China’s stock price crash risk: evidence from random forest model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(10), pages 23591-23614, October.
  13. Paraná, Edemilson, 2024. "AI as financial infrastructure?," SocArXiv ub92z, Center for Open Science.
  14. Hibbeln, Martin T. & Kopp, Raphael M. & Urban, Noah, 2026. "Predictive multiplicity, procedural multiplicity, and heterogeneous machine learning ensembles in recovery rate forecasting," Journal of Financial Stability, Elsevier, vol. 83(C).
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. Chacon, David Ugarte & Lee, Seohyun & Park, Jaehyuk, 2026. "An explainable machine learning model for consumer credit scoring in Mexico," Emerging Markets Review, Elsevier, vol. 71(C).
  20. 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.
  21. Babaei, Golnoosh & Giudici, Paolo & Raffinetti, Emanuela, 2023. "Explainable FinTech lending," Journal of Economics and Business, Elsevier, vol. 125.
  22. Dyakonova, Ludmila & Konstantinov, Alexey, 2024. "Approaches to risk analysis in the financial sector based on machine learning and artificial intelligence methods," MPRA Paper 122941, University Library of Munich, Germany.
  23. 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.
  24. 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.
  25. Johann Pfitzinger, 2021. "An Interpretable Neural Network for Parameter Inference," Papers 2106.05536, arXiv.org.
  26. 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.
  27. Da Wang & YingXue Zhou, 2024. "An innovative machine learning workflow to research China’s systemic financial crisis with SHAP value and Shapley regression," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-40, December.
  28. 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.
  29. Sullivan Hué, 2022. "GAM(L)A: An econometric model for interpretable machine learning," French Stata Users' Group Meetings 2022 19, Stata Users Group.
  30. Sahab Zandi & Noah Kostesku & Christophe Mues & Mar'ia 'Oskarsd'ottir & Cristi'an Bravo, 2026. "Communicating Credit Risk with Large Language Models: Evaluation of Explanations from Standard and Alternative Data-Based Models," Papers 2608.17715, arXiv.org.
  31. Rivalani Hlongwane & Kutlwano Ramabao & Wilson Mongwe, 2024. "A novel framework for enhancing transparency in credit scoring: Leveraging Shapley values for interpretable credit scorecards," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-20, August.
  32. Giudici, Paolo & Gramegna, Alex & Raffinetti, Emanuela, 2023. "Machine Learning Classification Model Comparison," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
  33. Georges, Christophre & Pereira, Javier, 2021. "Market stability with machine learning agents," Journal of Economic Dynamics and Control, Elsevier, vol. 122(C).
  34. 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.
  35. 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).
  36. Emmanuel Flachaire & Sullivan Hué & Sébastien Laurent & Gilles Hacheme, 2024. "Interpretable Machine Learning Using Partial Linear Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(3), pages 519-540, June.
  37. Branka Hadji Misheva & Joerg Osterrieder & Ali Hirsa & Onkar Kulkarni & Stephen Fung Lin, 2021. "Explainable AI in Credit Risk Management," Papers 2103.00949, arXiv.org.
  38. Aparna Gupta & Cheng Lu & Majeed Simaan & Mohammed J. Zaki, 2026. "When Positive Sentiment is not so Positive: Textual Analytics and Bank Failures," Computational Economics, Springer;Society for Computational Economics, vol. 68(1), pages 621-661, July.
  39. Sandeep Neela, 2026. "An Explainable Market Integrity Monitoring System with Multi-Source Attention Signals and Transparent Scoring," Papers 2601.15304, arXiv.org.
  40. Mustafa, Andy Ali & Lin, Ching-Yang & Kakinaka, Makoto, 2022. "Detecting market pattern changes: A machine learning approach," Finance Research Letters, Elsevier, vol. 47(PA).
  41. Sanjiv Das & Xin Huang & Soji Adeshina & Patrick Yang & Leonardo Bachega, 2023. "Credit Risk Modeling with Graph Machine Learning," INFORMS Joural on Data Science, INFORMS, vol. 2(2), pages 197-217, October.
  42. Bullock, Shaina S. & Bullock, David W. & Wilson, William W., 2023. "Short-Term Factors Influencing Corn Export Basis Values in the Pre- and Post-COVID Periods: A Comparison of Econometric and Machine Learning Approaches," 2023 Conference, April 24-25, 2023, St. Louis, Missouri 379019, NCR-134/ NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
  43. 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.
  44. repec:osf:socarx:ub92z_v1 is not listed on IDEAS
  45. 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.
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