create a website

Explainable AI for Credit Assessment in Banks. (2022). Westgaard, Sjur ; de Lange, Petter Eilif ; Vennerod, Christian Bakke ; Melsom, Borger.
In: JRFM.
RePEc:gam:jjrfmx:v:15:y:2022:i:12:p:556-:d:986356.

Full description at Econpapers || Download paper

Cited: 3

Citations received by this document

Cites: 28

References cited by this document

Cocites: 24

Documents which have cited the same bibliography

Coauthors: 0

Authors who have wrote about the same topic

Citations

Citations received by this document

  1. Credit Risk Assessment and Financial Decision Support Using Explainable Artificial Intelligence. (2024). Hameed, Ibrahim A ; Meena, V P ; Bahadur, Jitendra ; Jaraut, Praveen ; Balusamy, Balamurugan ; Grover, Veena ; Chaturvedi, Himakshi ; Nallakaruppan, M K.
    In: Risks.
    RePEc:gam:jrisks:v:12:y:2024:i:10:p:164-:d:1499310.

    Full description at Econpapers || Download paper

  2. Financial Distress Prediction in the Nordics: Early Warnings from Machine Learning Models. (2024). Risstad, Morten ; de Lange, Petter Eilif ; Moller, Mats ; Nylen-Forthun, Emil ; Abrahamsen, Nils-Gunnar Birkeland.
    In: JRFM.
    RePEc:gam:jjrfmx:v:17:y:2024:i:10:p:432-:d:1487547.

    Full description at Econpapers || Download paper

  3. The adverse impact of flight delays on passenger satisfaction: An innovative prediction model utilizing wide & deep learning. (2024). Ma, Xiaoqian ; Zhuang, Jun ; Song, Cen ; Ardizzone, Catherine.
    In: Journal of Air Transport Management.
    RePEc:eee:jaitra:v:114:y:2024:i:c:s0969699723001540.

    Full description at Econpapers || Download paper

References

References cited by this document

  1. [CrossRef] Benhamou, Eric, Jean-Jacques Ohana, David Saltiel, and Beatrice Guez. 2021. Explainable AI (XAI) Models Applied to Planning in Financial Markets. Available online: https://openreview.net/forum?id=mJrKRgYm2f1 (accessed on 1 November 2022).
    Paper not yet in RePEc: Add citation now
  2. [CrossRef] Bibal, Adrien, Michael Lognoul, Alexandre De Streel, and Benoît Frénay. 2021. Legal requirements on explainability in machine learning. Artificial Intelligence and Law 29: 149–69. [CrossRef] Breiman, Leo. 1998. Arcing classifier (with discussion and a rejoinder by the author). The Annals of Statistics 26: 801–49. [CrossRef] Brown, Iain, and Christophe Mues. 2012. An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications 39: 3446–53. [CrossRef] Bücker, Michael, Gero Szepannek, Alicja Gosiewska, and Przemyslaw Biecek. 2021. Transparency, auditability, and explainability of artificial intelligencemodels in credit scoring. Journal of the Operational Research Society 73: 70–90. [CrossRef] Bussmann, Niklas, Paolo Giudici, Dimitri Marinelli, and Jochen Papenbrock. 2020a. Explainable AI in Fintech Risk Management.
    Paper not yet in RePEc: Add citation now
  3. [CrossRef] Jolliffe, I. T. 1986. Principal Component Analysis and Factor Analysis. In Principal Component Analysis. New York: Springer, chap. 5.
    Paper not yet in RePEc: Add citation now
  4. Ariza-Garzón, Miller Janny, Javier Arroyo, Antonio Caparrini, and Maria-Jesus Segovia-Vargas. 2020. Explainability of a Artificial intelligenceGranting Scoring Model in Peer-to-Peer Lending. IEEE Access 8: 64873–90. [CrossRef] Bartlett, Peter, Yoav Freund, Wee Sun Lee, and Robert E. Schapire. 1998. Boosting the margin: A new explanation for the effectiveness of voting methods. The Annals of Statistics 26: 1651–86. [CrossRef] Basel Committee on Banking Supervention. 2006. International Convergence of Capital Measurement and Capital Standards. Available online: https://www.bis.org/publ/bcbs128.pdf (accessed on 1 November 2022).
    Paper not yet in RePEc: Add citation now
  5. Available online: https://ec.europa.eu/info/sites/default/files/commission-white-paper-artificialintelligence-feb2020_en.pdf (accessed on 11 May 2022).
    Paper not yet in RePEc: Add citation now
  6. Available online: https://www.garp.org/white-paper/explainable-machine-learning-models-of-consumer-credit-risk (accessed on 1 November 2022).
    Paper not yet in RePEc: Add citation now
  7. Bastos, João A., and Sara M. Matos. 2022. Explainable models of credit losses. European Journal of Operational Research 301: 386–94.

  8. Connelly, Lynne. 2020. Logistic regression. Medsurg Nursing 29: 353–54.
    Paper not yet in RePEc: Add citation now
  9. Davis, Randall, Andrew W. Lo, Sudhanshu Mishra, Arash Nourian, Manish Singh, Nicholas Wu, and Ruixun Zhang. 2022. Explainable Machine Learning Models of Consumer Credit Risk. Available from the Website of the Global Association of Risk Professionals.
    Paper not yet in RePEc: Add citation now
  10. European Commission. 2021a. Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts. English. Available online: https://eur-lex.europa.eu/resource.htAI?uri=cellar:e0649735-a372-11eb-9585-01aa75ed71a1.0001.02/DOC_1&format= PDF (accessed on 9 May 2022).
    Paper not yet in RePEc: Add citation now
  11. European Commission. 2021b. White Paper On Artificial Intelligence—A European Approach to Excellence and Trust. English.
    Paper not yet in RePEc: Add citation now
  12. Explainable Artificial intelligenceApproach as a Tool to Understand Factors Used to Select the Refractive Surgery Technique on the Expert Level. Translational Vision Science Technology 9: 8. [CrossRef] [PubMed] Young, H. Peyton. 1985. Monotonic solutions of cooperative games. International Journal of Game Theory 14: 65–72. [CrossRef] Zhang, Huan, Si Si, and Cho-Jui Hsieh. 2017. GPU-Acceleration for Large-Scale Tree Boosting. arXiv arXiv:1706.08359.
    Paper not yet in RePEc: Add citation now
  13. Freund, Yoav, and Robert E. Schapire. 1995. A desicion-theoretic generalization of on-line learning and an application to boosting. In Computational Learning Theory. Berlin/Heidelberg: Springer, pp. 23–37. ISBN 978-3-540-49195-8.
    Paper not yet in RePEc: Add citation now
  14. Freund, Yoav, and Robert E. Schapire. 1999. A Short Introduction to Boosting. Journal of Japanese Society for Artificial Intelligence 14: 771–80.
    Paper not yet in RePEc: Add citation now
  15. Frontiers in Artificial Intelligence 3: 26. [CrossRef] Bussmann, Niklas, Paolo Giudici, Dimitri Marinelli, and Jochen Papenbrock. 2020b. Explainable Machine Learning in Credit Risk Management. Computational Economics 57: 203–16. [CrossRef] Chen, Tianqi, and Carlos Guestrin. 2016. XGBoost: A Scalable Tree Boosting System. Paper presented at the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13–17; New York: ACM, vols. 13–17, pp. 785–94, ISBN 1450342329.
    Paper not yet in RePEc: Add citation now
  16. J. Risk Financial Manag. 2022, 15, 556 23 of 23 Gramegna, Alex, and Paolo Giudici. 2021. SHAP and LIME: An Evaluation of Discriminative Power in Credit Risk. Frontiers in Artificial Intelligence 4: 140. Available online: https://www.frontiersin.org/article/10.3389/frai.2021.752558 (accessed on 6 November 2022). [CrossRef] [PubMed] Hess, Aaron S., and John R. Hess. 2019. Logistic regression. Transfusion 59: 2197–98. [CrossRef] [PubMed] Hintze, Jerry L., and Ray D. Nelson. 1998. Violin Plots: A Box Plot-Density Trace Synergism. The American Statistician 52: 181–84.
    Paper not yet in RePEc: Add citation now
  17. Knowledge and Information Systems 41: 647–65. [CrossRef] Yang, Yimin, and Min Wu. 2021. Explainable Artificial intelligencefor Improving Logistic Regression Models. Paper presented at the 2021 IEEE 19th International Conference on Industrial Informatics (INDIN), Palma, Spain, July 21–23; pp. 1–6. [CrossRef] Yoo, Tae Keun, Ik Hee Ryu, Hannuy Choi, Jin Kuk Kim, In Sik Lee, Jung Sub Kim, Geunyoung Lee, and Tyler Hyungtaek Rim. 2020.
    Paper not yet in RePEc: Add citation now
  18. Lever, Jake, Martin Krzywinski, and Naomi Altman. 2016. Logistic regression. Nature Methods 13: 541–42. [CrossRef] Lundberg, Scott M., Gabriel G. Erion, and Su-In Lee. 2019. Consistent Individualized Feature Attribution for Tree Ensembles. arXiv arXiv:1802.03888.
    Paper not yet in RePEc: Add citation now
  19. Lundberg, Scott, and Su-In Lee. 2017. A unified approach to interpreting model predictions. arXiv arXiv:1705.07874.
    Paper not yet in RePEc: Add citation now
  20. Lundberg, Scott. 2018. How to Get SHAP Values of the Model Averaged by Folds? Available online: https://github.com/slundberg/ shap/issues/337#issuecomment-441710372 (accessed on 27 November 2021).
    Paper not yet in RePEc: Add citation now
  21. Misheva, Branka Hadji, Joerg Osterrieder, Ali Hirsa, Onkar Kulkarni, and Stephen Fung Lin. 2021. Explainable AI in Credit Risk Management. arXiv arXiv:2103.00949.

  22. Molnar, Christoph. 2019. Interpretable Machine Learning. A Guide for Making Black Box Models Explainable. SHAP (Shapley Additive Explanations): chap. 9.6. Available online: https://christophm.github.io/interpretableAI-book/shap.htAI (accessed on 6 November 2022).
    Paper not yet in RePEc: Add citation now
  23. Moscato, Vincenzo, Antonio Picariello, and Giancarlo Sperlí. 2021. A benchmark of machine learning approaches for credit score prediction. Expert Systems with Applications 165: 113986. [CrossRef] Niedzwiedz, Piotr. 2022. Neptune Optuna Hyperparamet Optimization. Available online: https://docs.neptune.ai/integrations-andsupported -tools/hyperparameteroptimization/optuna (accessed on 6 November 2022).
    Paper not yet in RePEc: Add citation now
  24. Nixon, Jeremy, Michael W. Dusenberry, Linchuan Zhang, Ghassen Jerfel, and Dustin Tran. 2019. Measuring Calibration in Deep Learning. Available online: https://arxiv.org/abs/1904.01685 (accessed on 6 November 2022). [CrossRef] Peng, Junfeng, Kaiqiang Zou, Mi Zhou, Yi Teng, Xiongyong Zhu, Feifei Zhang, and Jun Xu. 2021. An Explainable Artificial Intelligence Framework for the Deterioration Risk Prediction of Hepatitis Patients. Journal of Medical Systems 45: 61. [CrossRef] Quinto, Butch. 2020. Next-Generation Artificial intelligencewith Spark: Covers XGBoost, LightGBM, Spark NLP, Distributed Deep Learning with Keras, and More, 1st ed. New York: Apress. ISBN 9781484256695.
    Paper not yet in RePEc: Add citation now
  25. pp. 115–28. ISBN 978-14757-1904-8. [CrossRef] Ke, Guolin, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Advances in Neural Information Processing Systems. New York: Curran Associates, Inc., vol. 30.
    Paper not yet in RePEc: Add citation now
  26. Ribeiro, Marco Túlio, Sameer Singh, and Carlos Guestrin. 2016. “Why Should I Trust You?”: Explaining the Predictions of Any Classifier. arXiv arXiv:1602.04938.
    Paper not yet in RePEc: Add citation now
  27. Shapley, Lloyd S. 1953. Stochastic Games. Proceedings of the National Academy of Sciences 39: 1095–100. Available online: https: //www.pnas.org/content/39/10/1095.full.pdf (accessed on 6 November 2022). [CrossRef] Shrikumar, Avanti, Peyton Greenside, and Anshul Kundaje. 2019. Learning Important Features through Propagating Activation Differences. arXiv arXiv:1704.02685.
    Paper not yet in RePEc: Add citation now
  28. Strumbelj, Erik, and Igor Kononenko. 2013. Explaining prediction models and individual predictions with feature contributions.
    Paper not yet in RePEc: Add citation now

Cocites

Documents in RePEc which have cited the same bibliography

  1. An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors. (2025). Yang, Cai ; Abedin, Mohammad Zoynul ; Zhang, Hongwei ; Weng, Futian ; Hajek, Petr.
    In: Annals of Operations Research.
    RePEc:spr:annopr:v:347:y:2025:i:2:d:10.1007_s10479-023-05311-8.

    Full description at Econpapers || Download paper

  2. Business cycle and realized losses in the consumer credit industry. (2025). Roccazzella, Francesco ; Vrins, Frdric ; Distaso, Walter.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:323:y:2025:i:3:p:1024-1039.

    Full description at Econpapers || Download paper

  3. Credit Risk Assessment and Financial Decision Support Using Explainable Artificial Intelligence. (2024). Hameed, Ibrahim A ; Meena, V P ; Bahadur, Jitendra ; Jaraut, Praveen ; Balusamy, Balamurugan ; Grover, Veena ; Chaturvedi, Himakshi ; Nallakaruppan, M K.
    In: Risks.
    RePEc:gam:jrisks:v:12:y:2024:i:10:p:164-:d:1499310.

    Full description at Econpapers || Download paper

  4. Maritime Fuel Price Prediction of European Ports using Least Square Boosting and Facebook Prophet: Additional Insights from Explainable Artificial Intelligence. (2024). Ghosh, Indranil ; De, Arijit.
    In: Transportation Research Part E: Logistics and Transportation Review.
    RePEc:eee:transe:v:189:y:2024:i:c:s1366554524002771.

    Full description at Econpapers || Download paper

  5. Interpretable machine learning for creditor recovery rates. (2024). Fabozzi, Frank J ; Nazemi, Abdolreza.
    In: Journal of Banking & Finance.
    RePEc:eee:jbfina:v:164:y:2024:i:c:s0378426624001043.

    Full description at Econpapers || Download paper

  6. Bridging accuracy and interpretability: A rescaled cluster-then-predict approach for enhanced credit scoring. (2024). Kang, Ming-Hsuan ; Bai, Le-Chi ; Lee, I-Han ; Teng, Huei-Wen.
    In: International Review of Financial Analysis.
    RePEc:eee:finana:v:91:y:2024:i:c:s1057521923005215.

    Full description at Econpapers || Download paper

  7. What makes accidents severe! explainable analytics framework with parameter optimization. (2024). Moqbel, Murad ; Topuz, Kazim ; Abdulrashid, Ismail ; Ahmed, Abdulaziz.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:317:y:2024:i:2:p:425-436.

    Full description at Econpapers || Download paper

  8. 360 Degrees rumor detection: When explanations got some explaining to do. (2024). Van den Poel, Dirk ; Meire, Matthijs ; Bogaert, Matthias ; Schetgen, Lisa ; Janssens, Bram.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:317:y:2024:i:2:p:366-381.

    Full description at Econpapers || Download paper

  9. Explainability through uncertainty: Trustworthy decision-making with neural networks. (2024). Benoit, Dries F ; Thuy, Arthur.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:317:y:2024:i:2:p:330-340.

    Full description at Econpapers || Download paper

  10. Interpretable generalized additive neural networks. (2024). Weinzierl, Sven ; Zschech, Patrick ; Kraus, Mathias ; Tschernutter, Daniel.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:317:y:2024:i:2:p:303-316.

    Full description at Econpapers || Download paper

  11. Supervised feature compression based on counterfactual analysis. (2024). Piccialli, Veronica ; Morales, Dolores Romero ; Salvatore, Cecilia.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:317:y:2024:i:2:p:273-285.

    Full description at Econpapers || Download paper

  12. Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda. (2024). de Bock, Koen W ; Verbeke, Wouter ; Delen, Dursun ; Choi, Tsan-Ming ; Martens, David ; Lessmann, Stefan ; Vairetti, Carla ; Maldonado, Sebastian ; Baesens, Bart ; Sowiski, Roman ; de Caigny, Arno ; Boute, Robert N ; Weber, Richard ; Kraus, Mathias ; Oskarsdottir, Maria ; Coussement, Kristof.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:317:y:2024:i:2:p:249-272.

    Full description at Econpapers || Download paper

  13. KACDP: A Highly Interpretable Credit Default Prediction Model. (2024). Zhao, Jin ; Liu, Kun.
    In: Papers.
    RePEc:arx:papers:2411.17783.

    Full description at Econpapers || Download paper

  14. Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda. (2023). Boute, Robert N ; Weber, Richard ; Kraus, Mathias ; Oskarsdottir, Maria ; Coussement, Kristof ; de Bock, Koen W ; Verbeke, Wouter ; Delen, Dursun ; Choi, Tsan-Ming ; Martens, David ; Lessmann, Stefan ; Vairetti, Carla ; Maldonado, Sebastian ; Baesens, Bart ; Slowiski, Roman ; de Caigny, Arno.
    In: Post-Print.
    RePEc:hal:journl:hal-04219546.

    Full description at Econpapers || Download paper

  15. Interpretable high-stakes decision support system for credit default forecasting. (2023). Wang, Yong ; Zhang, Xuantao ; Sun, Weixin ; Li, Minghao.
    In: Technological Forecasting and Social Change.
    RePEc:eee:tefoso:v:196:y:2023:i:c:s0040162523005103.

    Full description at Econpapers || Download paper

  16. Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach. (2023). Fernandez-Aguado, Pilar Gomez ; Gonzalez, Marta Ramos ; Urea, Antonio Partal.
    In: Research in International Business and Finance.
    RePEc:eee:riibaf:v:64:y:2023:i:c:s0275531923000338.

    Full description at Econpapers || Download paper

  17. Determinants of non-performing loans: An explainable ensemble and deep neural network approach. (2023). Nwafor, Obumneme Zimuzor.
    In: Finance Research Letters.
    RePEc:eee:finlet:v:56:y:2023:i:c:s1544612323004567.

    Full description at Econpapers || Download paper

  18. Popularity, face and voice: Predicting and interpreting livestreamers retail performance using machine learning techniques. (2023). Xiong, Xiong ; Su, LI ; Yang, Fan.
    In: Papers.
    RePEc:arx:papers:2310.19200.

    Full description at Econpapers || Download paper

  19. American Option Pricing using Self-Attention GRU and Shapley Value Interpretation. (2023). Shen, Yanhui.
    In: Papers.
    RePEc:arx:papers:2310.12500.

    Full description at Econpapers || Download paper

  20. A Comprehensive Review on Financial Explainable AI. (2023). van der Heever, Wihan ; Yeo, Wei Jie ; Mao, Rui ; Satapathy, Ranjan ; Mengaldo, Gianmarco ; Cambria, Erik.
    In: Papers.
    RePEc:arx:papers:2309.11960.

    Full description at Econpapers || Download paper

  21. Modeling Inverse Demand Function with Explainable Dual Neural Networks. (2023). Chen, Zihan ; Cao, Zhiyu ; Feinstein, Zachary ; Mishra, Prerna ; Amini, Hamed.
    In: Papers.
    RePEc:arx:papers:2307.14322.

    Full description at Econpapers || Download paper

  22. Explainable AI for Credit Assessment in Banks. (2022). Westgaard, Sjur ; de Lange, Petter Eilif ; Vennerod, Christian Bakke ; Melsom, Borger.
    In: JRFM.
    RePEc:gam:jjrfmx:v:15:y:2022:i:12:p:556-:d:986356.

    Full description at Econpapers || Download paper

  23. Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam. (2022). Nguyen, Thanh Hien ; Tran, Kim Long ; Le, Hoang Anh.
    In: Data.
    RePEc:gam:jdataj:v:7:y:2022:i:11:p:160-:d:972099.

    Full description at Econpapers || Download paper

  24. Is It Possible to Forecast the Price of Bitcoin?. (2021). Goutte, Stéphane ; Chevallier, Julien ; Guegan, Dominique.
    In: Forecasting.
    RePEc:gam:jforec:v:3:y:2021:i:2:p:24-420:d:564101.

    Full description at Econpapers || Download paper

Coauthors

Authors registered in RePEc who have wrote about the same topic

Report date: 2025-10-04 19:07:08 || Missing content? Let us know

CitEc is a RePEc service, providing citation data for Economics since 2001. Last updated August, 3 2024. Contact: Jose Manuel Barrueco.