Machine Learning-Based Insurance Claim Prediction Using Customer Behavioral and Policy Data: A Conceptual Framework for Intelligent Risk Assessment and Decision Support

Vollständiger Abstract

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The insurance industry is undergoing a profound digital transformation driven by advances in machine learning (ML), artificial intelligence (AI), big data analytics, and cloud computing. Traditional claim prediction models, which primarily rely on demographic and historical claim information, often struggle to capture the complex behavioral patterns and dynamic risk factors that influence claim occurrence and severity. Recent developments in customer analytics, telematics, Internet of Things (IoT) devices, wearable technologies, and digital customer interactions have created opportunities to develop more accurate, adaptive, and explainable predictive models. This paper presents a comprehensive conceptual analysis of machine learning-based insurance claim prediction using customer behavioral and policy data. Drawing upon recent research published between 2018 and 2026, the study critically examines the evolution of predictive techniques, including Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, Gradient Boosting, Extreme Gradient Boosting (XGBoost), LightGBM, CatBoost, Artificial Neural Networks, and Deep Learning architectures. Beyond algorithmic performance, the paper evaluates emerging issues such as data quality, class imbalance, model interpretability, algorithmic fairness, privacy preservation, regulatory compliance, and deployment challenges in real-world insurance environments. A conceptual framework is proposed to illustrate the integration of customer behavioral characteristics, policy attributes, and advanced machine learning models into an intelligent decision-support system for claim prediction. The framework highlights the importance of explainable AI, continuous model monitoring, and human-in-the-loop decision-making to enhance transparency and operational trust. The study contributes to the literature by synthesizing recent developments, identifying critical research gaps, and outlining future research directions involving federated learning, graph neural networks, multimodal data fusion, causal machine learning, and generative AI. The findings provide valuable insights for researchers, insurance practitioners, and policymakers seeking to improve underwriting accuracy, fraud detection, claims management, and customer-centric risk assessment while supporting sustainable and data-driven insurance ecosystems. Keywords: Machine Learning, Insurance Claim Prediction, Customer Behavioral Analytics, Policy Data, Explainable Artificial Intelligence (XAI), Risk Assessment, Predictive Analytics