Unwrapping black box models A case study in credit risk
Author
Abstract
Suggested Citation
Note: 43
Download full text from publisher
References listed on IDEAS
- Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
- Lara Marie Demajo & Vince Vella & Alexiei Dingli, 2020. "Explainable AI for Interpretable Credit Scoring," Papers 2012.03749, arXiv.org.
- Giuseppe Cascarino & Mirko Moscatelli & Fabio Parlapiano, 2022. "Explainable Artificial Intelligence: interpreting default forecasting models based on Machine Learning," Questioni di Economia e Finanza (Occasional Papers) 674, Bank of Italy, Economic Research and International Relations Area.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Jorge Tejero, 2022. "Unwrapping black box models A case study in credit risk," Financial Stability Review, Banco de España, issue Autumn.
- Jorge Tejero, 2022. "Unwrapping black box models A case study in credit risk," Financial Stability Review, Banco de España, issue Autumn.
- Boris Ter-Avanesov & Homayoon Beigi, 2024. "MLP, XGBoost, KAN, TDNN, and LSTM-GRU Hybrid RNN with Attention for SPX and NDX European Call Option Pricing," Papers 2409.06724, arXiv.org, revised Oct 2024.
- Francesco Cusano & Giuseppe Marinelli & Stefano Piermattei, 2022. "Learning from revisions: an algorithm to detect errors in banks’ balance sheet statistical reporting," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4025-4059, December.
- Damiano Brigo & Xiaoshan Huang & Andrea Pallavicini & Haitz Saez de Ocariz Borde, 2021. "Interpretability in deep learning for finance: a case study for the Heston model," Papers 2104.09476, arXiv.org.
- Raffaele Marchi & Alessandro Moro, 2024.
"Forecasting Fiscal Crises in Emerging Markets and Low-Income Countries with Machine Learning Models,"
Open Economies Review, Springer, vol. 35(1), pages 189-213, February.
- Raffaele De Marchi & Alessandro Moro, 2023. "Forecasting fiscal crises in emerging markets and low-income countries with machine learning models," Temi di discussione (Economic working papers) 1405, Bank of Italy, Economic Research and International Relations Area.
- Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023.
"Big data forecasting of South African inflation,"
Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
- Byron Botha & Rulof Burger & Kevin Kotz & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," Working Papers 11022, South African Reserve Bank.
- Byron Botha & Kevin Kotze & Neil Rankin & Rulof P. Burger, 2022. "Big data forecasting of South African inflation," Working Papers 873, Economic Research Southern Africa.
- Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
- Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2023.
"Nowcasting consumer price inflation using high-frequency scanner data: Evidence from Germany,"
Discussion Papers
34/2023, Deutsche Bundesbank.
- Beck, Günter W. & Carstensen, Kai & Menz, Jan-Oliver & Schnorrenberger, Richard & Wieland, Elisabeth, 2024. "Nowcasting consumer price inflation using high-frequency scanner data: evidence from Germany," Working Paper Series 2930, European Central Bank.
- Daniel Stempel & Johannes Zahner, 2022. "DSGE Models and Machine Learning: An Application to Monetary Policy in the Euro Area," MAGKS Papers on Economics 202232, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
- Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.
- Pedro Guerra & Mauro Castelli, 2021. "Machine Learning Applied to Banking Supervision a Literature Review," Risks, MDPI, vol. 9(7), pages 1-24, July.
- Sonya Georgieva, 2023. "Application of Artificial Intelligence and Machine Learning in the Conduct of Monetary Policy by Central Banks," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 8, pages 177-199.
- Guner Altan & Server Demirci, 2022. "Credit Scoring on Cash Flow Table with Machine Learning: XGBoost Approach," Journal of Economic Policy Researches, Istanbul University, Faculty of Economics, vol. 9(2), pages 397-424, July.
- Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
- Rosaria Cerrone, 2023. "Are Artificial Intelligence and Machine Learning Shaping a New Risk Management Approach?," International Business Research, Canadian Center of Science and Education, vol. 16(12), pages 1-82, December.
- Sonan Memon, 2022. "Inflation in Pakistan: High-Frequency Estimation and Forecasting," PIDE-Working Papers 2022:12, Pakistan Institute of Development Economics.
- Antonietta di Salvatore & Mirko Moscatelli, 2024. "Improving survey information on household debt using granular credit databases," Questioni di Economia e Finanza (Occasional Papers) 839, Bank of Italy, Economic Research and International Relations Area.
- Douglas Kiarelly Godoy de Araujo, 2023.
"gingado: a machine learning library focused on economics and finance,"
IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: applications and tools, volume 59,
Bank for International Settlements.
- Douglas Kiarelly Godoy de Araujo, 2023. "gingado: a machine learning library focused on economics and finance," BIS Working Papers 1122, Bank for International Settlements.
- Andrés Alonso & José Manuel Carbó, 2022. "Accuracy of explanations of machine learning models for credit decisions," Working Papers 2222, Banco de España.
- Emer Owens & Barry Sheehan & Martin Mullins & Martin Cunneen & Juliane Ressel & German Castignani, 2022. "Explainable Artificial Intelligence (XAI) in Insurance," Risks, MDPI, vol. 10(12), pages 1-50, December.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bde:revist:y:2022:i:11:n:4. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bdegves.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.