Smart risk prediction: The rise of Bayesian models in finance

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.