Early-Stage Consumer Credit Risk Detection Using Multimodal Deep Learning and Behavioral AI

Abstract

Early identification of consumer credit risk is increasingly important in digital lending environments where conventional credit scores may not adequately capture rapidly changing borrower conditions and behavioral signals. This paper proposes a multimodal deep learning and Behavioral Artificial Intelligence framework for detecting early-stage consumer credit deterioration by integrating structured financial information, transactional sequences, repayment behavior, digital interaction patterns, and unstructured textual or contextual signals. The framework employs modality-specific feature extraction followed by attention-based multimodal fusion to identify nonlinear relationships and temporal changes associated with emerging repayment risk. Behavioral representations are continuously updated to distinguish persistent risk from temporary fluctuations, while explainability mechanisms support interpretation of risk-driving factors. The proposed approach is designed to complement conventional credit scoring by providing dynamic early-warning indicators rather than relying exclusively on historical credit records. The framework addresses challenges involving heterogeneous data, class imbalance, temporal drift, privacy, interpretability, and responsible use of alternative data. The study establishes a research structure for evaluating predictive performance, behavioral sensitivity, multimodal contribution, and operational applicability in consumer lending.