The article is devoted to a comparative analysis of traditional and neural network approaches in banking analytics. It examines key tasks of financial institutions, including credit scoring, customer churn prediction, fraud detection, and credit risk assessment. The advantages and limitations of classical statistical and machine learning methods are outlined, along with the modern capabilities of neural architectures — from multilayer perceptrons and recurrent networks to convolutional models, autoencoders, and generative adversarial networks. Special attention is paid to practical applications of artificial intelligence in both international and Russian banks, as well as to the challenges related to automation of credit decisions, data security, and model interpretability. Based on the review, the study substantiates the relevance of a hybrid approach that combines the accuracy of digital technologies with expert evaluation, thus enhancing credit processes and strengthening the competitive position of banks.