Abstract
This study aims to analyze text data obtained from Turkish phone calls to manage credit risk in the banking sector and predict whether customers will fulfill their payment promises. Data cleaning was identified as a critical step to improve the quality of the texts, and various natural language processing (NLP) techniques were used. The model was built using a two-layer LSTM architecture, starting with a Self-Embedding layer, and achieved approximately 80% accuracy on the test data. The findings indicate that customers who break their payment promises often cite personal life issues such as health problems, family issues, financial difficulties, and religious beliefs to ensure reliability. These results demonstrate the importance of text data in the banking sector, the applicability of different embedding methods to Turkish datasets, and their advantages and disadvantages. Furthermore, the model built using data obtained from customer conversations can help predict credit risk more accurately and contribute to improving call center processes. Automating data cleaning processes and developing speech-to-text translation tools are recommended for future studies.