Credit scoring by incorporating dynamic networked information

Yibei Li, Ximei Wang, Boualem Djehiche, Xiaoming Hu

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1103-1112
Number of pages10
JournalEuropean Journal of Operational Research
Issue number3
StatePublished - Nov 1 2020


  • Bayesian inference
  • Credit scoring
  • Decision processes
  • Multi-agent systems
  • Networked information

ASJC Scopus subject areas

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management


Dive into the research topics of 'Credit scoring by incorporating dynamic networked information'. Together they form a unique fingerprint.

Cite this