Prediction of Banking Credit Risk Using Logistic Regression and The Artificial Neural Network Models: A Case Study of English Banks

In this comprehensive study, we delve into the utilization of Logistic Regression (LR) and Artificial Neural Networks (ANN) for predicting credit risk in the English banking sector over the period from 2021 to 2023. Through an in-depth analysis of quarterly financial and non-financial data from various banks, this research aims to discern which predictive modeling technique provides more accuracy and reliability. The comparison between LR and ANN models offers significant insights into their capabilities and limitations, potentially guiding future risk management and decision-making processes in banking. This study also addresses the importance of advanced analytical methods in improving the predictiveness of financial risks, thus contributing to the enhancement of banking operations and the promotion of financial stability.