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A novel method for credit scoring based on feature transformation and ensemble model

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Listed:
  • Li, Hongxiang
  • Feng, Ao
  • Lin, Bin
  • Su, Houcheng
  • Liu, Zixi
  • Duan, Xuliang
  • Pu, Haibo
  • Wang, Yifei

Abstract

Credit scoring is a very critical task for banks and other financial institutions, and it has become an important evaluation metric to distinguish potential defaulting users. In this paper, we propose a credit score prediction method based on feature transformation and ensemble model, which is essentially a cascade approach. The feature transformation process consisting of boosting trees (BT) and auto-encoders (AE) is employed to replace manual feature engineering and to solve the data imbalance problem. For the classification process, this paper designs a heterogeneous ensemble model by weighting the factorization machine (FM) and deep neural networks (DNN), which can efficiently extract low-order intersections and high-order intersections. Comprehensive experiments were conducted on two standard datasets and the results demonstrate that the proposed approach outperforms existing credit scoring models in accuracy.

Suggested Citation

  • Li, Hongxiang & Feng, Ao & Lin, Bin & Su, Houcheng & Liu, Zixi & Duan, Xuliang & Pu, Haibo & Wang, Yifei, 2021. "A novel method for credit scoring based on feature transformation and ensemble model," Santa Cruz Department of Economics, Working Paper Series qt3v33k65c, Department of Economics, UC Santa Cruz.
    • Handle: RePEc:cdl:ucscec:qt3v33k65c
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    References listed on IDEAS

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    1. Fahmida E. Moula & Chi Guotai & Mohammad Zoynul Abedin, 2017. "Credit default prediction modeling: an application of support vector machine," Risk Management, Palgrave Macmillan, vol. 19(2), pages 158-187, May.
    2. Lang, Jan Hannes & Peltonen, Tuomas A. & Sarlin, Peter, 2018. "A framework for early-warning modeling with an application to banks," Working Paper Series 2182, European Central Bank.
    3. Caruso, G. & Gattone, S.A. & Fortuna, F. & Di Battista, T., 2021. "Cluster Analysis for mixed data: An application to credit risk evaluation," Socio-Economic Planning Sciences, Elsevier, vol. 73(C).
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