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Credit Scoring by Incorporating Dynamic Networked Information

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

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 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 Cram\'er-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

  • Yibei Li & Ximei Wang & Boualem Djehiche & Xiaoming Hu, 2019. "Credit Scoring by Incorporating Dynamic Networked Information," Papers 1905.11795, arXiv.org, revised Oct 2019.
    • Handle: RePEc:arx:papers:1905.11795
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    Cited by:

    1. Shi, Yong & Qu, Yi & Chen, Zhensong & Mi, Yunlong & Wang, Yunong, 2024. "Improved credit risk prediction based on an integrated graph representation learning approach with graph transformation," European Journal of Operational Research, Elsevier, vol. 315(2), pages 786-801.
    2. Wang, Zhongyi & Tian, Yuhang & Li, Sihan & Xiao, Jin, 2025. "A secure cross-silo collaborative method for imbalanced credit scoring," European Journal of Operational Research, Elsevier, vol. 326(2), pages 357-373.
    3. 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.
    4. 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.
    5. Tang, Xinyin & Feng, Chong & Zhu, Jianping & He, Minna, 2022. "How Can We Learn from Borrowers’ Online Behaviors? The Signal Effect of Borrowers’ Platform Involvement on Their Credit Risk," SocArXiv qga8j, Center for Open Science.
    6. Shiqi Fang & Zexun Chen & Jake Ansell, 2024. "Peer-induced Fairness: A Causal Approach for Algorithmic Fairness Auditing," Papers 2408.02558, arXiv.org, revised Sep 2024.
    7. 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.
    8. Luisa Roa & Andr'es Rodr'iguez-Rey & Alejandro Correa-Bahnsen & Carlos Valencia, 2021. "Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data," Papers 2102.09974, arXiv.org.
    9. VanMeeter, Mallory & Kugley, Shannon & Dierksheide, Elizabeth & McDaniel, Mark, 2025. "Community leadership in system transformation: A realist review of strategies for effective partnership between communities of color and public systems impacting children and families," Children and Youth Services Review, Elsevier, vol. 175(C).

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