AI-driven Insurance Risk Assessment and Policy Recommendation: Integrated Application of Machine Learning and Explainable AI

This article introduces an insurance risk assessment and policy recommendation system that integrates machine learning, explainable AI, and large language models. Through the collaboration of a three-layer technology stack, it achieves precise risk quantification, transparent decision interpretation, and personalized insurance product recommendations, addressing pain points in traditional insurance such as low efficiency of manual underwriting, inaccurate risk identification, and opaque decision-making, and providing a reference technical solution for the intelligent transformation of the insurance industry.

Traditional insurance relies on manual underwriting, experience-based judgment, and standardized products, facing issues such as low efficiency, poor customer experience, and inaccurate risk identification. Risk assessment is a core link, but traditional methods rely on limited structured data and subjective experience, making it difficult to fully capture risks. With the development of big data and AI technologies, insurtech is reshaping industry models, and decision transparency and interpretability are key requirements for AI applications.

The system adopts a layered decoupling architecture: the data layer is responsible for heterogeneous data integration, cleaning, and feature engineering; the model layer includes risk assessment models (algorithms such as gradient boosting trees) and explainable modules (SHAP/LIME technologies); the interaction layer introduces large language models to convert outputs into natural language explanations and enable conversational interaction, enhancing user experience.

Risk assessment models are trained based on historical claim data, and feature engineering combines business knowledge to select key features (age, health indicators, driving records, etc.). Model selection needs to balance accuracy and interpretability, and ensemble methods such as gradient boosting trees are commonly used. After training, time-series cross-validation, A/B testing, and fairness audits are used to ensure the model is stable and reliable.

Interpretability is key to gaining trust. SHAP technology calculates feature contribution, providing case-specific (e.g., how driving violations affect risk scores) and global (feature importance ranking) explanations. Large language models convert technical numerical values into easy-to-understand natural language, helping users understand the basis for decisions.

Recommendations need to consider risk coverage adequacy (matching customers' main risks), affordability (aligning with payment capacity), and personalization (combining customer preferences). The system identifies protection gaps through risk portraits, dynamically adjusts recommendation strategies, and conversational interaction further optimizes recommendation effects.

Deployment faces challenges such as data privacy (protection of sensitive information), fairness (avoiding amplification of historical biases), regulatory compliance (meeting transparency requirements), and model drift (continuous performance monitoring). Countermeasures such as data encryption, fairness audits, reserved regulatory interfaces, and regular retraining should be adopted.

The system demonstrates the application potential of AI in the insurance industry, and future developments will focus on real-time risk assessment, multimodal AI, federated learning, etc. However, AI should be used as an enhancement tool, and principles of interpretability, transparency, and fairness must be adhered to ensure the sustainable development of smart insurance. This project provides a starting point for technical references in the industry.