APPLICATION OF DEEP LEARNING FOR CREDIT RISK PREDICTION IN THE BANKING SECTOR

Abstract

The article examines the application of deep learning methods for credit risk prediction in the banking sector. Credit risk management is one of the key tasks of financial institutions, as effective assessment of borrowers' creditworthiness allows for minimizing financial losses and improving the stability of the banking system. Traditional risk assessment methods, such as logistic regression, discriminant analysis, and scoring models, have several limitations, including a restricted ability to process large volumes of heterogeneous data and difficulty in identifying nonlinear relationships between financial variables.

Deep learning offers new opportunities to enhance risk prediction accuracy through multi-layered neural networks capable of automatically extracting essential features from input data. This article analyzes contemporary approaches to the use of artificial neural networks in the financial sector, including convolutional (CNN), recurrent (RNN), and transformer-based models. The advantages of deep learning over traditional statistical methods are demonstrated, particularly its ability to analyze unstructured data such as textual comments, transactional activity, and behavioral characteristics of borrowers.

The study results show that the use of neural networks allows banks to increase credit risk assessment accuracy by 10–20% compared to traditional models. Additionally, deep learning facilitates the reduction of default rates, the automation of credit scoring, and more efficient allocation of financial resources. At the same time, the article addresses the challenges of implementing such technologies in the banking sector, including the explainability of models, the need to comply with regulatory requirements (GDPR, PCI DSS), and the high computational complexity of deep learning algorithms.

Based on the conducted analysis, the feasibility of integrating deep learning into credit risk management is justified. Recommendations for the optimal use of artificial intelligence technologies in banking practice are provided. The obtained results can be utilized to improve existing credit scoring systems and serve as a foundation for further research in the field of financial process automation and the implementation of innovative financial technologies.