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.
In the present work, we define a stochastic model using machine learning techniques to generate random fields of some uncertain parameters. The proposed stochastic model is based on Bayesian inference and aims at reconstituting the parameters of interest and their credible intervals. The main goal of this work is to define a model that estimates the values of the uncertain parameters known only by their distribution probability functions and some observed spatial measurements. We note that this type of parameters may be associated with some mathematical models usually traduced by non-lin