Modeling Consumer Financial Behavior through Temporal Risk Embeddings for Credit Default Prediction

Accurate prediction of credit default risk remains a critical challenge in financial systems due to the increasing complexity of consumer behavior and dynamic transaction patterns. Traditional credit scoring models rely heavily on static features and fail to capture temporal dependencies and evolving behavioral signals, leading to suboptimal risk assessment. Recent deep learning approaches improve predictive performance but often overlook fine-grained behavioral transitions and early warning indicators embedded in sequential financial data. To address this gap, this study proposes a novel Temporal Risk Embedding framework that models consumer financial behavior through sequential representation learning. The methodology integrates Long Short-Term Memory (LSTM) networks with Transformer-based attention mechanisms to encode temporal dependencies, while behavioral embeddings capture spending trends, EMI repayment patterns, and credit utilization dynamics. A risk state transition module further models behavioral shifts leading to default. Experiments are conducted on a real-world credit dataset combined with synthetic behavioral sequences to simulate diverse financial profiles. The proposed model achieves an accuracy of 96.8%, precision of 95.9%, recall of 96.3%, and F1-score of 96.1%, outperforming baseline models such as Random Forest, XGBoost, and standalone LSTM. The results demonstrate the effectiveness of temporal behavioral embeddings in identifying early default signals and enhancing predictive robustness.