Artificial Intelligence Based Customer Risk Classification for Receivables Management of Businesses

This study is carried out with the aim of developing and implementing artificial intelligence-based receivables management systems for businesses. A model is created to predict customers' debt payment situations. In the study, invoice data of a company named QF_CARIRAPOR is utilized. The features table is created in Apache druid and risk scoring label is made manually according to set rules. Then, various machine learning models such as XGBoost, Random Forest are implemented on MindsDB platform. The classified risk score is visualized with the Streamlit user interface using the results created in MindsDB. Among the applied models, XGBoost has resulted in the highest classification accuracy of 98.8 %. The findings reveal the potential to increase the effectiveness of receivables management processes by applying machine learning models.