Dimensionality‐Aware Credit Scoring With Hybrid Feature Selection and Simplified Graph Convolutional Networks

Peer‐to‐peer (P2P) lending is an emerging Financial Technology (FinTech) innovation that allows direct loans between individuals and businesses, avoiding traditional intermediaries. Contemporarily, loan defaults become a significant risk for financial institutions, which prompts the need for better default risk prediction models. Literature includes salient issues including feature selection, separation of outliers, and the effective integration of graph‐based neural networks towards enhancing such a prediction model. This work proposes a novel hybrid feature selection method combining Boruta with minimum Redundancy Maximum Relevance (mRMR) for an enhanced credit default prediction. The selected feature set is then tested against tree‐based models, which are particularly effective because they can model nonlinear relationships and interactions between variables. However, tree‐based models cannot capture complex dependencies very well and fail to benefit from inherent structural relationships in data. Next, we develop a new approach using k‐Nearest Neighbors (kNN), GraphSAGE and, Simplified Graph Convolution Networks (SGCN). In addition, we use Isolation Forest, an unsupervised anomaly detection method, to distinguish clean and noisy data so that the proposed model can be grounded in credible and relevant subsets of data. The experimentation is performed on the complete Lending Club and Prosper Datasets (including their inlier and outlier subsets). The findings using the Lending Club Dataset highlight the hybrid feature selection method as an effective and robust strategy for improving SGCN performance, especially in the presence of diverse and noisy data. Comparison study shows that the proposed approach improves the metrics such as accuracy, F1‐score, and AUC, upto 88.99%, 88.61%, and 96.37%, respectively. It ensures that the proposed approach outperforms existing many approaches on multiple subsets of data. It enhances the robustness of the proposel model and suited very well for credit default prediction in P2P lending.