[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1728":3,"related-1728":70},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":22,"classification":23,"search_phrases":52,"slug":53,"view_count":15,"doi":54,"paper":55,"created_at":69},1728,"Machine Learning-Based Insurance Claim Prediction Using Customer Behavioral and Policy Data: A Conceptual Framework for Intelligent Risk Assessment and Decision Support","https:\u002F\u002Fdoi.org\u002F10.55041\u002Fijcope.v2i8.304","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","保险行业正经历由机器学习（ML）、人工智能（AI）、大数据分析和云计算进步所驱动的深刻数字化转型。传统的理赔预测模型主要依赖人口统计信息和历史理赔记录，往往难以捕捉影响理赔发生及其严重程度的复杂行为模式和动态风险因素。客户分析、远程信息处理、物联网（IoT）设备、可穿戴技术以及数字化客户互动领域的最新发展，为开发更精准、更具适应性且可解释的预测模型创造了机遇。本文基于客户行为数据和保单数据，对基于机器学习的保险理赔预测进行了全面的概念性分析。研究借鉴2018年至2026年间发表的最新文献，批判性地审视了预测技术的演进，涵盖逻辑回归、决策树、随机森林、支持向量机、梯度提升、极端梯度提升（XGBoost）、LightGBM、CatBoost、人工神经网络及深度学习架构等。除算法性能外，本文还评估了数据质量、类别不平衡、模型可解释性、算法公平性、隐私保护、监管合规性以及现实保险环境中部署挑战等新兴议题。研究提出一个概念性框架，用以说明如何将客户行为特征、保单属性与先进的机器学习模型整合到智能理赔预测决策支持系统中。该框架强调了可解释人工智能（XAI）、持续模型监控以及人在回路（human-in-the-loop）决策对于增强透明度和运营信任的重要性。本研究通过综合最新进展、识别关键研究空白，并勾勒涉及联邦学习、图神经网络、多模态数据融合、因果机器学习及生成式人工智能的未来研究方向，为相关文献作出贡献。研究结论为研究人员、保险从业者及政策制定者提供了有价值的洞见，有助于提升承保精度、欺诈检测、理赔管理及以客户为中心的风险评估，同时支撑可持续的、数据驱动的保险生态系统。\n\n关键词：机器学习；保险理赔预测；客户行为分析；保单数据；可解释性",null,"International Journal of Creative and Open Research in Engineering and Management","2026-09-03T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"主题为保险理赔预测，与三农、农业信息化无关，不进入精选。",[19],{"name":10,"url":6},[21],"保险科技",[],{"status":24,"locked":14,"version":25,"method":26,"evidence":27},"needs_review","2026-10-22.2","rules",{"candidates":28,"agricultural_evidence":14,"reason":51},[29,42],{"code":30,"score":31,"supported":32,"title":33,"abstract":36,"tags":41},"I2",25,true,[34,35],"machine learning","decision support",[37,38,34,39,40],"深度学习","机器学习","deep learning","artificial intelligence",[],{"code":43,"score":44,"supported":32,"title":45,"abstract":46,"tags":50},"I5",9,[],[47,48,49],"物联网","internet of things","IoT",[],"农业相关性需要复核",[],"MachineLearning-BasedInsuranceClaimPredi-1728","10.55041\u002Fijcope.v2i8.304",{"doi":54,"openalex_id":56,"authors":57,"venue":10,"cited_by_count":15,"oa_url":60,"card":61,"direction":67,"ingested_from":68},"W7207605013",[58],{"name":59,"orcid":9},"Pragadeesh Roopchander","https:\u002F\u002Fijcope.org\u002Fwp-content\u002Fuploads\u002F2026\u002F09\u002FMachine-Learning-Based-Insurance-Claim-Prediction-Using-CustomerBehavioral-and-Policy-Data-A-Conceptual-Framework-for-Intelligent-RiskAssessment-and-Decision-Support.pdf",{"tldr":62,"method":63,"finding":64,"direction":65,"opportunity":66},"提出基于机器学习和客户行为数据的保险理赔预测概念框架，用于智能风险评估。","综述2018-2026年研究，比较多种ML算法，提出集成行为、保单和ML的决策框","强调可解释AI、持续监控和人机协同，以提升理赔预测的透明度和信任。","其他","可探索将联邦学习、图神经网络等应用于农业保险理赔预测，结合农业物联网数据，实现精准风险评估。","智慧农业 \u002F 农业物联网","openalex","2026-09-05T23:30:11.381488Z",{"total":15,"page":71,"page_size":71,"items":72},1,[]]