A Powerful Algorithm for e-Commerce Credit Risk Analysis

It is critical to build a system and model for analysing the credit risk associated with e-commerce to decrease the chance of credit defaults occurring during online transactions. This approach examines a client's income, assets, negative transaction rate, reputation, and third-party credit evaluation findings to determine creditworthiness. The risk assessment index-based credit risk analysis model is built using the RB-XGBoost approach. The credit risk analysis algorithm's training process employs a data balancing technique to increase the system's capacity to forecast accurately. This method is designed to solve the problem of class imbalance in machine learning datasets. The training set is separated into groups at this point, and each group is compared to a specified threshold. The dataset is then made more representative by combining oversampling and undersampling. The system's capacity to analyse the credit risk presented by online shops is improved by balancing data subsets. Online retailers might utilise the proposed model and indexing approach to better assess their clients' creditworthiness and thereby reduce the number of consumers whose payments are late. By suggesting a unique way to address class imbalance in machine learning datasets, this study adds to the expanding body of work on credit risk assessment in online transactions.