Machine Learning-Based Risk Management of Credit Sales in Small and Midsize Business

Sustaining and expanding the finances of small and midsize companies (SMBs) depends on efficient credit risk management. This study redefines credit risk assessment for SMBs via the use of machine learning (ML), hence introducing a disruptive methodology. The all-inclusive approach includes feature selection, preprocessing, data collecting, and the use of ML models, with an emphasis on behavioral insights integration and real-world applicability. The results imply that Random Forests and other machine learning models are superior at predicting credit risk, which may lead to a sea shift in the way SMBs handle credit risk. Improving the research's practical implications involves applying models to actual credit risk management systems and incorporating insights from behavioral economics. Possible future research directions include studying how models adapt dynamically, using different types of data, enhancing explain ability via XAI, and fostering collaborative efforts to develop industry-specific best practices. By outlining the ins and outs of credit sales, this research helps small and medium-sized companies (SMBs) adjust and remain resilient in the face of changing market conditions.