Moscow, Russian Federation
graduate student
graduate student
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.
banking analytics, credit scoring, artificial intelligence, neural networks, machine learning, fraud detection, risk management, digitalization of banking, model interpretability, Big Data
1. Federal'nyy zakon “O personal'nyh dannyh” ot 27.07.2006 N 152-FZ (poslednyaya redakciya) \ Konsul'tantPlyus [WWW Document], n.d. URL https://www.consultant.ru/document/cons_doc_LAW_61801/ (data obrascheniya 01.06.2025).
2. Dong, G., Lai, K.K., Yen, J., 2010. Credit scorecard based on logistic regression with random coefficients. Procedia Comput. Sci., ICCS 2010 1, 2463–2468. https://doi.org/10.1016/j.procs.2010.04.278
3. Ghojogh, B., Crowley, M., 2019. Linear and Quadratic Discriminant Analysis: Tutorial. https://doi.org/10.48550/arXiv.1906.02590
4. Dumitrescu, E., Hué, S., Hurlin, C., Tokpavi, S., 2022. Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects. Eur. J. Oper. Res. 297, 1178–1192. https://doi.org/10.1016/j.ejor.2021.06.053
5. Blanco, A., Pino-Mejías, R., Lara, J., Rayo, S., 2013. Credit scoring models for the microfinance industry using neural networks: Evidence from Peru. Expert Syst. Appl. 40, 356–364. https://doi.org/10.1016/j.eswa.2012.07.051
6. Ala’raj, M., Abbod, M.F., Majdalawieh, M., 2021. Modelling customers credit card behaviour using bidirectional LSTM neural networks. J. Big Data 8, 69. https://doi.org/10.1186/s40537-021-00461-7
7. Agrawal, P., Chaudhary, D., Madaan, V., Zabrovskiy, A., Prodan, R., Kimovski, D., Timmerer, C., 2021. Automated bank cheque verification using image processing and deep learning methods. Multimed. Tools Appl. 80, 5319–5350. https://doi.org/10.1007/s11042-020-09818-1
8. Lin, T.-H., Jiang, J.-R., 2021. Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest. Mathematics 9, 2683. https://doi.org/10.3390/math9212683
9. Efimov, D., Xu, D., Kong, L., Nefedov, A., Anandakrishnan, A., 2020. Using generative adversarial networks to synthesize artificial financial datasets. https://doi.org/10.48550/arXiv.2002.02271
10. Ushanov, A.E., 2024. Novye Tehnologii V Ocenke Kreditosposobnosti Klientov Banka: Plyusy I Minusy. Azimut Nauchnyh Issledovaniy Ekonomika I Upravlenie 13, 158–162
