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A Novel Modified Binning and Logistics Regression to Handle Shifting in Credit Scoring

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  • Yusuf Priyo Anggodo

    (Bina Nusantara University)

  • Abba Suganda Girsang

    (Bina Nusantara University)

Abstract

The development of financial technology (Fintech) in emerging economies such as Indonesia has been rapid in the last few years, opening a great potential for loan businesses, from venture capital to micro and personal loans. To survive in such competitive markets, new companies need a robust credit-scoring model. However, building a reliable model requires large stable data. The challenge is that datasets are often small, covering only a few months (short-period datasets). Therefore, this study proposes a modified binning method, namely changing a variable’s values into two groups with the smallest distribution differences possible. Modified binning can maintain data trends to avoid future shifting. The simulation was conducted using a real dataset from Indonesian Fintech, comprising 44,917 borrower-level observations with 396 variables. To match the actual conditions, the first three months of data were allocated for modeling and the remaining for testing. Implementing modified binning and logistics regression to testing data results in a more stable score band than standard binning. Compared with other classifier methods, the proposed method obtained the best AUC results on the testing data (0.73). In addition, the proposed method is highly applicable as it can provide a straightforward explanation to upper management or regulators. It is practical to use in real-case financial technology with short-period problems.

Suggested Citation

  • Yusuf Priyo Anggodo & Abba Suganda Girsang, 2024. "A Novel Modified Binning and Logistics Regression to Handle Shifting in Credit Scoring," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2371-2403, June.
    • Handle: RePEc:kap:compec:v:63:y:2024:i:6:d:10.1007_s10614-023-10410-6
    DOI: 10.1007/s10614-023-10410-6
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    References listed on IDEAS

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    1. Andrea Bedin & Monica Billio & Michele Costola & Loriana Pelizzon, 2019. "Credit Scoring in SME Asset-Backed Securities: An Italian Case Study," JRFM, MDPI, vol. 12(2), pages 1-28, May.
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    3. Finlay, Steven, 2010. "Credit scoring for profitability objectives," European Journal of Operational Research, Elsevier, vol. 202(2), pages 528-537, April.
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    5. D J Hand, 2005. "Good practice in retail credit scorecard assessment," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1109-1117, September.
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