THE EFFECTIVENESS OF ARTIFICIAL INTELLIGENCE AND BIG DATA TECHNOLOGIES IN THE CREDIT LENDING PROCESS

The rapid development of artificial intelligence (AI) and big data technologies has significantly transformed the credit lending process in modern financial systems. This study examines the effectiveness of integrating AI-driven algorithms and big data analytics in improving credit risk assessment, decision-making accuracy, and operational efficiency. Traditional lending systems often rely on limited financial data and manual evaluation, which can lead to biases and inefficiencies. In contrast, AI and big data enable real-time processing of vast and diverse datasets, enhancing predictive accuracy and reducing default risks. The research employs both quantitative and qualitative methodologies to evaluate the performance of AI-based credit systems compared to conventional approaches. Empirical findings demonstrate that AI-driven models improve loan approval speed, minimize human error, and enhance customer segmentation. The study concludes that adopting these technologies leads to more inclusive, efficient, and reliable credit systems, while also highlighting potential risks such as data privacy concerns and algorithmic bias.