A Comparative Study of Education Loan Approval Automation Process Using Machine Learning

This study elaborates on the use of the ‘Random Forest’ algorithm, which is a supervised machine learning algorithm that can be deployed in regression as well as classification problems and has been deployed for prediction of loan approval. The study utilizes the data of Indian customers who want to avail loans for educational purposes from the Indian banks, which are the main loan lending agencies in India. The dataset has been curated from Indian customers who have applied for education loans from the Indian banks. Features of the dataset have been selected by reviewing the application forms of various Indian banks. From the standpoint of Indian banks, the current study shows the potential of machine learning algorithms such as the decision tree classification algorithm, with a particular emphasis on the random forest method, in predicting education loan eligibility with a high accuracy of 90.6%. The model has been evaluated using the following most pertinent metrics in the classification problem: accuracy, recall, F1 score, and precision. Significantly, our approach eliminates the need for credit scores, making it an ideal solution for education loan applicants who typically have limited or no credit history. To the best of our knowledge, there is a significant gap in the literature because no study has particularly examined the prediction of education loan approval in the context of Indian banks. Our research fills this critical void, providing valuable insights for stakeholders in the Indian education loan sector. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.