Smart risk prediction: The rise of Bayesian models in finance

Nidhal Ziadi Ellouze (), Nesrine Gafsi () and Nihel Ziadi Ben Fadhe ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 10, 83-103

Abstract: This research aims to develop predictive models to estimate the probability of bank customer default based on socio-economic factors. Relying on Bayesian inference frameworks, the study implements both Naive Bayes classifiers and Bayesian Neural Networks (BNNs) to improve prediction accuracy. The methodology is grounded in Bayes’ theorem and conditional independence, with practical implementation using Python libraries such as scikit-learn and TensorFlow Probability. Following rigorous preprocessing and model training on financial datasets, the results show effective segmentation of customers according to their credit risk levels. The models enable personalized financial product recommendations, including tailored interest rates and guarantees. The findings demonstrate the statistical robustness of Bayesian approaches and their ability to deliver interpretable solutions for credit risk assessment. This approach supports strategic decision-making by aligning banking offers with individual risk profiles, ultimately contributing to risk mitigation and enhanced customer relationship management.

Keywords: Bayesian inference; Bayesian neural networks (BNNs); Credit risk prediction; Customer segmentation; Naive Bayes classifier. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://learning-gate.com/index.php/2576-8484/article/view/10344/3362 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:9:y:2025:i:10:p:83-103:id:10344

Access Statistics for this article

More articles in Edelweiss Applied Science and Technology from Learning Gate
Bibliographic data for series maintained by Melissa Fernandes ().