Multi-Modal Insurance Risk Prediction
Keywords:
Deep Neural Networks, Multi-Modal Learning, Risk Prediction, Model Capacity Control, Overfitting Mitigation, Capsule Networks, Graph-Based Learning, Sparse Graph Data, Modality-Specific Fusion, Insurance Default Prediction, Customer Churn Prediction, Representation Learning, Embedding Techniques, Neural Interpretability, Balanced Model Design, Heterogeneous Data Fusion, Predictive Performance Optimization, Structural Learning, Model Stability, Explainable Risk Models.Abstract
Deeper neural networks have achieved strong performance across a wide range of prediction problems by fusing heterogeneous data with increasing model capacity. Neither extreme of the modelling spectrum, however, is desirable in practice: too little capacity and excessive stability limit predictive efficacy, while unnecessarily high dimensionality wastes rich data without yielding meaningful gains. In risk prediction tasks, where model interpretability carries particular importance, it is worth investigating whether performance is better served by increasing depth and model complexity, or by the opposite approach — intelligently balanced, modality-specific fusion.
We present a novel multi-modal architecture in which capsule networks probe sparse graph data to capture important inter-node relationships, while a conventional architecture leverages learned embeddings for the remaining stages of the pipeline. Applied to the prediction of insurance default and churn, our results show that careful fusion combined with deliberately minimizing model capacity to guard against overfitting ultimately yields better test performance. Consistent, complementary validation together with neural interpretability analysis supports the proposed architecture, which strikes a balance between model depth and sensible control of risk in prediction tasks.
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