Incorporating Feature Interactions and Contrastive Learning for Credit Prediction

The efficacy of credit risk assessment models is pivotal to the risk management capacity of financial institutions. Traditional credit risk models often suffer from inadequate predictive accuracy due to overlooked feature combinations and weak supervisory signals. Addressing these limitations, we present a novel approach for credit default prediction that integrates feature interactions and contrastive learning. Specifically, we introduce second-order interactions atop standard linear models to achieve low-order feature interplay. Concurrently, the integration of deep neural networks and attention mechanisms facilitates the learning of concealed high-order features, thus enhancing the model’s non-linear modeling capabilities and illuminating latent feature associations. Further, to ameliorate the issues of noise and diminished supervisory signals, we embed slight noise in feature embeddings for data augmentation and construct contrastive views, ultimately refining feature quality. To attest to the effectiveness of our approach, we conducted experiments on two real-world datasets, benchmarking against eight predictive methods including LR, XGBoost, and FiBiNET. The results unequivocally demonstrate the superior performance of our method across various metrics, underscoring its promise and excellence in the realm of credit risk assessment.