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Architecting Real-Time Risk Stratification in the Insurance Sector: A Deep Convolutional and Recurrent Neural Network Framework for Dynamic Predictive Modeling

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Severov Arseni Vasilievich ¹ , Artyom V. Smirnov ¹ ,

⁴ Department of Banking and Financial Markets, Financial University under the Government of the Russian Federation, Moscow, Russia

⁴ Institute of Financial Technology Moscow State University Moscow, Russia

Abstract

Objective: The insurance sector is rapidly transitioning from static, historical risk models to dynamic, real-time assessment frameworks. This study addresses the inherent limitations of traditional actuarial methods, which yield an average risk prediction accuracy of approximately 67.3%, particularly when confronted with high-dimensional, unstructured data. We aim to design and validate a novel Deep Learning (DL) architecture capable of performing real-time risk stratification and enabling hyper-personalized, dynamic policy pricing.

Methodology: A hybrid DL framework integrating Convolutional Neural Networks (CNNs) for unstructured claims data and Recurrent Neural Networks (RNNs) for sequential telematics and IoT sensor logs is proposed. The model combines features from these sub-architectures with traditional structured data to generate an instantaneous risk score. The framework's efficacy is comparatively analyzed against established Generalized Linear Models (GLMs). Furthermore, the critical dimension of interpretability is addressed through the integration of SHAP-based Explainable AI (XAI) to ensure regulatory compliance and consumer trust.

Results: The developed DL architecture demonstrated superior performance, achieving a risk prediction accuracy of approximately 89.4%. Quantifiable operational gains include a 43% increase in claims processing efficiency and a 27% improvement in fraudulent claims detection. Simulation results indicate a 45.3% improvement in loss ratio predictability under the dynamic pricing scheme, which is further supported by a 3.7-fold increase in pattern recognition success compared to conventional approaches.

Conclusion: The integration of a multi-modal Deep Learning framework facilitates a fundamental shift toward an individual-centric, risk-reflective insurance paradigm. While significant, the advancements necessitate a concentrated focus on robust ethical governance, particularly regarding algorithmic fairness and data privacy, to sustain the realized competitive advantage (evidenced by 38% increase in customer satisfaction metrics).

Keywords

Deep Learning, Real-Time Risk Assessment, Insurance Technology, Predictive Modeling, Telematics, Dynamic Pricing, Explainable AI

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Abstract

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