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Andres Alonso

Personal Details

First Name:Andres
Middle Name:
Last Name:Alonso
Suffix:
RePEc Short-ID:pal1095
[This author has chosen not to make the email address public]
https://www.bde.es/investigador/en/menu/people/research_staff_a/alonso-robisco--andres.html

Affiliation

Banco de España

Madrid, Spain
http://www.bde.es/
RePEc:edi:bdegves (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Andrés Alonso & José Manuel Carbó, 2022. "Accuracy of explanations of machine learning models for credit decisions," Working Papers 2222, Banco de España.
  2. Andrés Alonso & José Manuel Carbó, 2021. "Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation," Working Papers 2105, Banco de España.
  3. Andrés Alonso & José Manuel Carbó, 2020. "Machine learning in credit risk: measuring the dilemma between prediction and supervisory cost," Working Papers 2032, Banco de España.
  4. Andrés Alonso & José Manuel Marqués, 2019. "Innovación financiera para una economía sostenible," Occasional Papers 1916, Banco de España.
  5. Andrés Alonso & José Manuel Marqués, 2019. "Financial innovation for a sustainable economy," Occasional Papers 1916, Banco de España.

Articles

  1. Alonso-Robisco, Andres & Carbó, José Manuel, 2023. "Analysis of CBDC narrative by central banks using large language models," Finance Research Letters, Elsevier, vol. 58(PC).
  2. 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.
  3. Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Andrés Alonso & José Manuel Carbó, 2021. "Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation," Working Papers 2105, Banco de España.

    Cited by:

    1. Edward I. Altman & Marco Balzano & Alessandro Giannozzi & Stjepan Srhoj, 2023. "Revisiting SME default predictors: The Omega Score," Journal of Small Business Management, Taylor & Francis Journals, vol. 61(6), pages 2383-2417, November.
    2. Andrés Alonso & José Manuel Carbó, 2022. "Accuracy of explanations of machine learning models for credit decisions," Working Papers 2222, Banco de España.
    3. Pedro Guerra & Mauro Castelli, 2021. "Machine Learning Applied to Banking Supervision a Literature Review," Risks, MDPI, vol. 9(7), pages 1-24, July.
    4. Ryuichiro Hashimoto & Kakeru Miura & Yasunori Yoshizaki, 2023. "Application of Machine Learning to a Credit Rating Classification Model: Techniques for Improving the Explainability of Machine Learning," Bank of Japan Working Paper Series 23-E-6, Bank of Japan.
    5. 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.

  2. Andrés Alonso & José Manuel Carbó, 2020. "Machine learning in credit risk: measuring the dilemma between prediction and supervisory cost," Working Papers 2032, Banco de España.

    Cited by:

    1. Wosnitza, Jan Henrik, 2022. "Calibration alternatives to logistic regression and their potential for transferring the dispersion of discriminatory power into uncertainties of probabilities of default," Discussion Papers 04/2022, Deutsche Bundesbank.
    2. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    3. Andrés Alonso & José Manuel Carbó, 2022. "Accuracy of explanations of machine learning models for credit decisions," Working Papers 2222, Banco de España.
    4. Pedro Guerra & Mauro Castelli, 2021. "Machine Learning Applied to Banking Supervision a Literature Review," Risks, MDPI, vol. 9(7), pages 1-24, July.
    5. Andrés Alonso & José Manuel Carbó, 2021. "Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation," Working Papers 2105, Banco de España.
    6. Faraz Ahmed & Kehkashan Nizam & Zubair Sajid & Sunain Qamar & Ahsan, 2024. "Striking a Balance: Evaluating Credit Risk with Traditional and Machine Learning Models," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(3), pages 30-35.
    7. Valter T. Yoshida Jr & Alan de Genaro & Rafael Schiozer & Toni R. E. dos Santos, 2023. "A Novel Credit Model Risk Measure: does more data lead to lower model risk in credit scoring models?," Working Papers Series 582, Central Bank of Brazil, Research Department.
    8. 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.
    9. Zixue Zhao & Tianxiang Cui & Shusheng Ding & Jiawei Li & Anthony Graham Bellotti, 2024. "Resampling Techniques Study on Class Imbalance Problem in Credit Risk Prediction," Mathematics, MDPI, vol. 12(5), pages 1-27, February.
    10. Dimitrios Nikolaidis & Michalis Doumpos, 2022. "Credit Scoring with Drift Adaptation Using Local Regions of Competence," SN Operations Research Forum, Springer, vol. 3(4), pages 1-28, December.
    11. 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.
    12. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    13. Pedro Guerra & Mauro Castelli & Nadine Côrte-Real, 2022. "Approaching European Supervisory Risk Assessment with SupTech: A Proposal of an Early Warning System," Risks, MDPI, vol. 10(4), pages 1-23, March.

  3. Andrés Alonso & José Manuel Marqués, 2019. "Innovación financiera para una economía sostenible," Occasional Papers 1916, Banco de España.

    Cited by:

    1. Esther Ortiz-Martínez & Salvador Marín-Hernández, 2020. "European Financial Services SMEs: Language in Their Sustainability Reporting," Sustainability, MDPI, vol. 12(20), pages 1-20, October.

  4. Andrés Alonso & José Manuel Marqués, 2019. "Financial innovation for a sustainable economy," Occasional Papers 1916, Banco de España.

    Cited by:

    1. Cristina Chueca Vergara & Luis Ferruz Agudo, 2021. "Fintech and Sustainability: Do They Affect Each Other?," Sustainability, MDPI, vol. 13(13), pages 1-19, June.
    2. Randall E. Duran & Peter Tierney, 2023. "Fintech Data Infrastructure for ESG Disclosure Compliance," JRFM, MDPI, vol. 16(8), pages 1-19, August.
    3. Ricardo Gimeno & Fernando Sols, 2020. "Incorporating sustainability factors into asset management," Financial Stability Review, Banco de España, issue Autumn.
    4. Clara Isabel González Martínez, 2021. "Overview of global and European institutional sustainable finances initiatives," Economic Bulletin, Banco de España, issue 3/2021.
    5. 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.
    6. Clara Isabel González Martínez, 2021. "The role of central banks in combating climate change and developing sustainable finance," Economic Bulletin, Banco de España, issue 3/2021.

Articles

  1. 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.

    Cited by:

    1. Faraz Ahmed & Kehkashan Nizam & Zubair Sajid & Sunain Qamar & Ahsan, 2024. "Striking a Balance: Evaluating Credit Risk with Traditional and Machine Learning Models," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(3), pages 30-35.
    2. González, Marta Ramos & Ureña, Antonio Partal & Fernández-Aguado, Pilar Gómez, 2023. "Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Ryuichiro Hashimoto & Kakeru Miura & Yasunori Yoshizaki, 2023. "Application of Machine Learning to a Credit Rating Classification Model: Techniques for Improving the Explainability of Machine Learning," Bank of Japan Working Paper Series 23-E-6, Bank of Japan.
    4. Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).

  2. Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).

    Cited by:

    1. Cosma, Simona & Rimo, Giuseppe & Torluccio, Giuseppe, 2023. "Knowledge mapping of model risk in banking," International Review of Financial Analysis, Elsevier, vol. 89(C).
    2. F. Bolivar & Miguel A. Duran & A. Lozano-Vivas, 2024. "Business Model Contributions to Bank Profit Performance: A Machine Learning Approach," Papers 2401.12334, arXiv.org.
    3. 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).

More information

Research fields, statistics, top rankings, if available.

Statistics

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NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 5 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-BIG: Big Data (3) 2020-11-16 2021-03-15 2022-08-29. Author is listed
  2. NEP-CMP: Computational Economics (3) 2020-11-16 2021-03-15 2022-08-29. Author is listed
  3. NEP-RMG: Risk Management (3) 2020-11-16 2021-03-15 2021-03-29. Author is listed
  4. NEP-ENV: Environmental Economics (2) 2019-10-07 2021-03-29. Author is listed
  5. NEP-PAY: Payment Systems and Financial Technology (2) 2020-11-16 2022-08-29. Author is listed
  6. NEP-BAN: Banking (1) 2020-11-16
  7. NEP-FMK: Financial Markets (1) 2020-11-16
  8. NEP-SBM: Small Business Management (1) 2021-03-29

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