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Variable reduction, sample selection bias and bank retail credit scoring

Author

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  • Marshall, Andrew
  • Tang, Leilei
  • Milne, Alistair

Abstract

This paper investigates the effect of including the customer loan approval process to the estimation of loan performance and explores the influence of sample selection bias in predicting the probability of default. The bootstrap variable reduction technique is applied to reduce the variable dimension for a large data-set drawn from a major UK retail bank. The results show a statistically significant correlation between the loan approval and performance processes. We further demonstrate an economically significant improvement in forecasting performance when taking into account sample selection bias. We conclude that financial institutions can obtain benefits by correcting for sample selection bias in their credit scoring models.

Suggested Citation

  • Marshall, Andrew & Tang, Leilei & Milne, Alistair, 2010. "Variable reduction, sample selection bias and bank retail credit scoring," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 501-512, June.
    • Handle: RePEc:eee:empfin:v:17:y:2010:i:3:p:501-512
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    References listed on IDEAS

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    Cited by:

    1. Tsukahara, Fábio Yasuhiro & Kimura, Herbert & Sobreiro, Vinicius Amorim & Zambrano, Juan Carlos Arismendi, 2016. "Validation of default probability models: A stress testing approach," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 70-85.
    2. Bücker, Michael & van Kampen, Maarten & Krämer, Walter, 2013. "Reject inference in consumer credit scoring with nonignorable missing data," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 1040-1045.
    3. Galema, Rients, 2020. "Credit rationing in P2P lending to SMEs: Do lender-borrower relationships matter?," Journal of Corporate Finance, Elsevier, vol. 65(C).
    4. Yaseen Ghulam & Sophie Hill, 2017. "Distinguishing between Good and Bad Subprime Auto Loans Borrowers: The Role of Demographic, Region and Loan Characteristics," Review of Economics & Finance, Better Advances Press, Canada, vol. 10, pages 49-62, November.
    5. Monir El Annas & Badreddine Benyacoub & Mohamed Ouzineb, 2023. "Semi-supervised adapted HMMs for P2P credit scoring systems with reject inference," Computational Statistics, Springer, vol. 38(1), pages 149-169, March.
    6. Jiang, Jinglin & Liao, Li & Lu, Xi & Wang, Zhengwei & Xiang, Hongyu, 2021. "Deciphering big data in consumer credit evaluation," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 28-45.
    7. Rogelio A. Mancisidor & Michael Kampffmeyer & Kjersti Aas & Robert Jenssen, 2019. "Deep Generative Models for Reject Inference in Credit Scoring," Papers 1904.11376, arXiv.org, revised Sep 2021.
    8. Gabriela Kuvikova, 2015. "Loans for Better Living: The Role of Informal Collateral," CERGE-EI Working Papers wp541, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    9. Thi Mai Luong, 2020. "Selection Effects of Lender and Borrower Choices on Risk Measurement, Management and Prudential Regulation," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 3-2020.
    10. Yaseen Ghulam & Kamini Dhruva & Sana Naseem & Sophie Hill, 2018. "The Interaction of Borrower and Loan Characteristics in Predicting Risks of Subprime Automobile Loans," Risks, MDPI, vol. 6(3), pages 1-21, September.

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