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Credit scoring by incorporating dynamic networked information

Author

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  • Li, Yibei
  • Wang, Ximei
  • Djehiche, Boualem
  • Hu, Xiaoming

Abstract

In this paper, the credit scoring problem is studied by incorporating networked information, where the advantages of such incorporation are investigated theoretically in two scenarios. Firstly, a Bayesian optimal filter is proposed to provide risk prediction for lenders assuming that published credit scores are estimated merely from structured financial data. Such prediction can then be used as a monitoring indicator for the risk management in lenders’ future decisions. Secondly, a recursive Bayes estimator is further proposed to improve the precision of credit scoring by incorporating the dynamic interaction topology of clients. It is shown theoretically that under the proposed evolution framework, the designed estimator has a higher precision than any efficient estimator, and the mean square errors are strictly smaller than the Cramér–Rao lower bound for clients within a certain range of scores. Finally, simulation results for a special case illustrate the feasibility and effectiveness of the proposed algorithms.

Suggested Citation

  • Li, Yibei & Wang, Ximei & Djehiche, Boualem & Hu, Xiaoming, 2020. "Credit scoring by incorporating dynamic networked information," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1103-1112.
    • Handle: RePEc:eee:ejores:v:286:y:2020:i:3:p:1103-1112
    DOI: 10.1016/j.ejor.2020.03.078
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    2. Chen, Shunqin & Guo, Zhengfeng & Zhao, Xinlei, 2021. "Predicting mortgage early delinquency with machine learning methods," European Journal of Operational Research, Elsevier, vol. 290(1), pages 358-372.
    3. Georgiou, K. & Domazakis, G.N. & Pappas, D. & Yannacopoulos, A.N., 2021. "Markov chain lumpability and applications to credit risk modelling in compliance with the International Financial Reporting Standard 9 framework," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1146-1164.
    4. Silva, Diego M.B. & Pereira, Gustavo H.A. & Magalhães, Tiago M., 2022. "A class of categorization methods for credit scoring models," European Journal of Operational Research, Elsevier, vol. 296(1), pages 323-331.

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