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Keywords

Artificial Intelligence, Deep Neural Networks, LightGBM, Credit Risk, Banking Sector, Prediction Accuracy

Document Type

Article

Abstract

Credit risk assessment for customers represents one of the core foundations that support the stability of the financial system and promote sustainable economic growth. It plays a crucial role in guiding financing decisions within financial institutions by enabling the evaluation of customers’ ability to assume and manage financial risk. This capability enhances the efficiency of resource allocation and contributes to achieving a balanced approach between economic development and institutional financial safety.

However, the process of credit risk assessment is inherently complex due to the interplay of numerous factors, including customers’ financial indicators and behavioral attributes. This complexity necessitates the use of advanced analytical tools that can process large volumes of financial and historical credit data with high precision, thereby facilitating more informed and reliable credit decisions.

In this regard, artificial intelligence (AI) has emerged as a transformative tool, demonstrating high effectiveness in improving the accuracy of credit risk evaluation. AI’s ability to swiftly analyses vast and complex datasets significantly enhances decision-making processes while reducing the probability of human error.

This research proposes the development of a comprehensive scientific model based on AI techniques for assessing customer credit risk. Specifically, the LightGBM algorithm was applied, yielding an accuracy rate of 92%, while a deep neural network model achieved an accuracy of 87%. These findings illustrate the substantial potential of AI in enhancing credit risk management mechanisms and reinforcing the financial resilience of institutions. The study further underscores the importance of adopting advanced technological solutions to address contemporary financial challenges and to support the pursuit of long-term economic sustainability.

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