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  4. Application of the Bayesian approach to modeling credit risks

Application of the Bayesian approach to modeling credit risks

MMC.
2024;
Volume 11, Number 4
: pp. 1025–1034
https://doi.org/10.23939/mmc2024.04.1025
Received: March 20, 2024
Revised: November 15, 2024
Accepted: November 17, 2024

Senyk A. P., Manziy O. S., Ohloblin P. E., Senyk Y. A., Krasiuk O. P.  Application of the Bayesian approach to modeling credit risks.  Mathematical Modeling and Computing. Vol. 11, No. 4, pp. 1025–1034 (2024)

Authors:
  1. A. P. Senyk
  2. O. S. Manziy
  3. P. E. Ohloblin
  4. Y. A. Senyk
  5. O. P. Krasiuk
1
Lviv Polytechnic National University
2
Lviv Polytechnic National University
3
Lviv Polytechnic National University
4
Lviv Forestry University of Ukraine
5
Hetman Petro Sahaidachnyi National Army Academy

A computer model for analyzing, evaluating, and forecasting bank credit risks has been developed.  Utilizing a Bayesian network (BN) and established parameter estimation methods, this model was implemented in the Python programming language.  It predicts the probability that a borrower may fail to meet financial obligations, such as repaying a loan.

mathematical methods
data analysis
graph
probabilistic parameters
machine learning
credit risk models
Bayesian network
Bayesian approach
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