IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v4y2022i4p50-935d975842.html
   My bibliography  Save this article

Predicting Credit Scores with Boosted Decision Trees

Author

Listed:
  • João A. Bastos

    (Lisbon School of Economics and Management (ISEG) and CEMAPRE/REM, Universidade de Lisboa, 1200-781 Lisboa, Portugal)

Abstract

Credit scoring models help lenders decide whether to grant or reject credit to applicants. This paper proposes a credit scoring model based on boosted decision trees, a powerful learning technique that aggregates several decision trees to form a classifier given by a weighted majority vote of classifications predicted by individual decision trees. The performance of boosted decision trees is evaluated using two publicly available credit card application datasets. The prediction accuracy of boosted decision trees is benchmarked against two alternative machine learning techniques: the multilayer perceptron and support vector machines. The results show that boosted decision trees are a competitive technique for implementing credit scoring models.

Suggested Citation

  • João A. Bastos, 2022. "Predicting Credit Scores with Boosted Decision Trees," Forecasting, MDPI, vol. 4(4), pages 1-11, November.
    • Handle: RePEc:gam:jforec:v:4:y:2022:i:4:p:50-935:d:975842
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/4/4/50/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-9394/4/4/50/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wiginton, John C., 1980. "A Note on the Comparison of Logit and Discriminant Models of Consumer Credit Behavior," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 15(3), pages 757-770, September.
    2. João Bastos, 2014. "Ensemble Predictions of Recovery Rates," Journal of Financial Services Research, Springer;Western Finance Association, vol. 46(2), pages 177-193, October.
    3. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    4. Zhou, Jing & Li, Wei & Wang, Jiaxin & Ding, Shuai & Xia, Chengyi, 2019. "Default prediction in P2P lending from high-dimensional data based on machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    5. Reichert, Alan K & Cho, Chien-Ching & Wagner, George M, 1983. "An Examination of the Conceptual Issues Involved in Developing Credit-scoring Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 101-114, April.
    6. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
    7. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. "Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
    Full references (including those not matched with items on IDEAS)

    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.
    1. Bastos, Joao, 2007. "Credit scoring with boosted decision trees," MPRA Paper 8034, University Library of Munich, Germany.
    2. Paleologo, Giuseppe & Elisseeff, André & Antonini, Gianluca, 2010. "Subagging for credit scoring models," European Journal of Operational Research, Elsevier, vol. 201(2), pages 490-499, March.
    3. Liu, Yi & Yang, Menglong & Wang, Yudong & Li, Yongshan & Xiong, Tiancheng & Li, Anzhe, 2022. "Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 79(C).
    4. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Forecast bankruptcy using a blend of clustering and MARS model - Case of US banks," Post-Print halshs-01314553, HAL.
    5. Casado Yusta, Silvia & Nœ–ez Letamendía, Laura & Pacheco Bonrostro, Joaqu’n Antonio, 2018. "Predicting Corporate Failure: The GRASP-LOGIT Model || Predicci—n de la quiebra empresarial: el modelo GRASP-LOGIT," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 294-314, Diciembre.
    6. Adnan Dželihodžić & Dženana Đonko & Jasmin Kevrić, 2018. "Improved Credit Scoring Model Based on Bagging Neural Network," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1725-1741, November.
    7. repec:diw:diwwpp:dp416 is not listed on IDEAS
    8. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    9. Carlos Serrano-Cinca & Begoña Gutiérrez-Nieto & Nydia M. Reyes, 2013. "A Social Approach to Microfinance Credit Scoring," Working Papers CEB 13-013, ULB -- Universite Libre de Bruxelles.
    10. Trivedi, Shrawan Kumar, 2020. "A study on credit scoring modeling with different feature selection and machine learning approaches," Technology in Society, Elsevier, vol. 63(C).
    11. Juan Laborda & Seyong Ryoo, 2021. "Feature Selection in a Credit Scoring Model," Mathematics, MDPI, vol. 9(7), pages 1-22, March.
    12. Ting Sun & Miklos A. Vasarhelyi, 2018. "Predicting credit card delinquencies: An application of deep neural networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 25(4), pages 174-189, October.
    13. Li Gan & Roberto Mosquera, 2008. "An Empirical Study of the Credit Market with Unobserved Consumer Typers," NBER Working Papers 13873, National Bureau of Economic Research, Inc.
    14. Lessmann, Stefan & Baesens, Bart & Seow, Hsin-Vonn & Thomas, Lyn C., 2015. "Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research," European Journal of Operational Research, Elsevier, vol. 247(1), pages 124-136.
    15. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    16. Moro, Russ & Härdle, Wolfgang Karl & Aliakbari, Saeideh & Hoffmann, Linda, 2011. "Forecasting corporate distress in the Asian and Pacific region," SFB 649 Discussion Papers 2011-023, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    17. Linhui Wang & Jianping Zhu & Chenlu Zheng & Zhiyuan Zhang, 2024. "Incorporating Digital Footprints into Credit-Scoring Models through Model Averaging," Mathematics, MDPI, vol. 12(18), pages 1-15, September.
    18. Ahmed Almustfa Hussin Adam Khatir & Marco Bee, 2022. "Machine Learning Models and Data-Balancing Techniques for Credit Scoring: What Is the Best Combination?," Risks, MDPI, vol. 10(9), pages 1-22, August.
    19. Huei-Wen Teng & Michael Lee, 2019. "Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-27, September.
    20. Liu, Wanan & Fan, Hong & Xia, Meng, 2023. "Tree-based heterogeneous cascade ensemble model for credit scoring," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1593-1614.
    21. Koen W. de Bock, 2017. "The best of two worlds: Balancing model strength and comprehensibility in business failure prediction using spline-rule ensembles," Post-Print hal-01588059, HAL.

    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:gam:jforec:v:4:y:2022:i:4:p:50-935:d:975842. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.