Enhancing transparency and fairness in automated credit decisions: an explainable novel hybrid machine learning approach.

ABSTRACT: This paper uses a generalised stacking method to introduce a novel hybrid model that combines a one-dimensional convolutional neural network 1DCNN with extreme gradient boosting XGBoost. We compared the predictive accuracies of the proposed hybrid architecture with three conventional algorithms-1DCNN, XGBoost and logistic regression (LR) using a dataset of over twenty thousand peer-to-peer (P2P) consumer credit observations. By leveraging the SHAP algorithm, the research provides a detailed analysis of feature importance, contributing to the model's predictions and offering insights into the overall and individual significance of different features. The findings demonstrate that the hybrid model outperforms the LR, XGBoost and 1DCNN models in terms of classification accuracy. Furthermore,

SUBMITTER: Nwafor CN 

PROVIDER: S-EPMC11502870 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature