Temporal Causal Inference Models for Early Warning Signals in Consumer Credit Deterioration

Abstract

Consumer credit deterioration poses significant risks to financial institutions, demanding effective early warning systems that can anticipate credit quality decline before default occurs. Traditional credit risk models often rely on correlation-based methods, which lack the ability to discern underlying causal relationships that drive temporal changes in borrower behavior. This paper proposes a novel approach leveraging temporal causal inference models to identify early warning signals of consumer credit deterioration. By integrating dynamic causal frameworks such as structural causal models and Granger causality with advanced temporal feature engineering, our methodology captures the evolving dependencies and confounding factors inherent in credit behavior over time. We evaluate our approach on real-world consumer credit datasets, demonstrating superior performance in early detection accuracy and lead time compared to conventional time series and machine learning models. Furthermore, we highlight the interpretability and practical relevance of extracted causal signals, providing actionable insights for credit risk managers. This work underscores the value of causal reasoning in temporal domains and offers a promising direction for developing robust, transparent, and proactive credit risk monitoring systems.