A Deep Learning Based Hybrid Model Development for Enhanced Credit Score Prediction

Abstract

This paper presents a hybrid deep learning model that combines Deep Neural Networks (DNNs) and Recurrent Neural Networks (RNNs) to enhance credit score predictions, especially for people who have a short credit history. In these situations, traditional credit scoring techniques frequently fall short, misclassifying creditworthy applicants and costing lenders money. Neural network models and ensemble methods are used in the model's data preparation to find intricate patterns. Metrics demonstrating the hybrid RNN+DNN model's superior performance over standalone models include an AUC-ROC score of 0.7971 and enhanced outcomes via stratified K-fold cross-validation. The hybrid model also achieves high sensitivity, specificity, and accuracy. LSTM units, dense layers, batch size, epochs, L2 regularization, and dropout rates are all part of the model architecture. Although the study was successful, it had limitations that pertain to interpretability, computing requirements and dataset quality. To guarantee accuracy and equity in credit assessment, future research should concentrate on refining hyperparameters, increasing computational effectiveness, and verifying the model using actual credit scoring systems.