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Relevant bibliographies by topics / Predictive Analytics In Insurance

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Academic literature on the topic 'Predictive Analytics In Insurance'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "Predictive Analytics In Insurance"

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Aas, Kjersti, Arthur Charpentier, Fei Huang, and Ronald Richman. "Insurance analytics: prediction, explainability, and fairness." Annals of Actuarial Science 18, no. 3 (2024): 535–39. https://doi.org/10.1017/s1748499524000289.

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AbstractThe expanding application of advanced analytics in insurance has generated numerous opportunities, such as more accurate predictive modeling powered by machine learning and artificial intelligence (AI) methods, the utilization of novel and unstructured datasets, and the automation of key operations. Significant advances in these areas are being made through novel applications and adaptations of predictive modeling techniques for insurance purposes, while, concurrently, rapid advances in machine learning methods are being made outside of the insurance sector. However, these innovations

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Quan, Zhiyu, and Emiliano A. Valdez. "Predictive analytics of insurance claims using multivariate decision trees." Dependence Modeling 6, no. 1 (2018): 377–407. http://dx.doi.org/10.1515/demo-2018-0022.

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AbstractBecause of its many advantages, the use of decision trees has become an increasingly popular alternative predictive tool for building classification and regression models. Its origins date back for about five decades where the algorithm can be broadly described by repeatedly partitioning the regions of the explanatory variables and thereby creating a tree-based model for predicting the response. Innovations to the original methods, such as random forests and gradient boosting, have further improved the capabilities of using decision trees as a predictive model. In addition, the extensi

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Researcher. "HEALTHCARE DATA ANALYTICS: LEVERAGING PREDICTIVE ANALYTICS FOR IMPROVED PATIENT OUTCOMES." International Journal of Computer Engineering and Technology (IJCET) 15, no. 6 (2024): 548–65. https://doi.org/10.5281/zenodo.14197001.

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Predictive analytics has emerged as a transformative force in modern healthcare, revolutionizing patient care management by integrating artificial intelligence and machine learning technologies. This comprehensive article examines the implementation, challenges, and outcomes of predictive analytics across healthcare facilities worldwide. The article explores diverse data sources, including electronic health records (EHRs), wearable technology, insurance claims, genomic information, and patient-reported outcomes, highlighting their role in improving clinical decision-making. Advanced analy

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Kasula, Yashwanth, Revathi Kadali, Rohan Sai Vurenuka, and G. Uma Devi. "Predictive Analytics for Airline Delay." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 6876–81. https://doi.org/10.22214/ijraset.2025.69999.

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Abstract: Flight delays have emerged as a critical challenge in civil aviation, causing substantial economic impacts across airlines and related industries. Accurate prediction of flight delays is increasingly valuable for airline operations, airport resource management, insurance risk assessment, and passenger planning. The complexity of delay factors characterized by their non-linear relationships and regional variations presents significant modeling challenges. This paper addresses limitations in existing prediction frameworks by introducing a novel flight delay prediction model with enhanc

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Researcher. "PREDICTIVE ANALYTICS FOR HEALTHCARE INSURANCE RISK ASSESSMENT USING ENSEMBLE LEARNING MODELS." INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS 5, no. 1 (2024): 1–21. https://doi.org/10.5281/zenodo.14598759.

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Accurate healthcare insurance risk assessment is essential in designing costeffective and personalized insurance plans. The study proposes the Dynamic Ensemble Risk Stratification Algorithm (DERISA), a new approach using advanced ensemble learning techniques for predictive analytics in healthcare insurance. With Random Forest, Gradient Boosting Machine (GBM), and XGBoost models integrated within a dynamically weighted ensemble framework, DERISA predicts insurance risks with high precision. Feature engineering techniques such as PCA and mutual information are followed to extract and optimize re

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Moradi, Mohsen, and Seyed Mohammad Fateminejad. "Sharing and Analyzing Data to Reduce Insurance Fraud." Journal of Management and Accounting Studies 5, no. 03 (2019): 96–100. http://dx.doi.org/10.24200/jmas.vol5iss03pp96-100.

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Insurance fraud is a multi-billion-dollar problem. Fraudulent practices occur frequently and often repeatedly. Fraud can be detected and prevented if appropriate data is collected, analyzed and shared among insurance companies.Methodology:Appropriate decision support and analytics can be developed to routinize fraud detection. Creating these decision support capabilities involves addressing managerial, technological, and data ownership issues.This article examines these issues in the context of using new data sources and predictive analytics to both reduce insurance fraud and improve customer

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Rakesh Maltumkar. "Transforming Insurance Risk Management through Advanced Data Analytics." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2311–21. https://doi.org/10.32628/cseit251112242.

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This comprehensive article explores the transformative impact of advanced data analytics on insurance risk management. The article examines how modern analytical approaches, including machine learning, natural language processing, and predictive modeling, are revolutionizing traditional insurance operations. The article investigates various technical implementations across fraud detection, claims processing, customer segmentation, and risk assessment. The article covers data integration challenges, real-time processing architectures, and scalability solutions while exploring the business impac

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Lohani, Shashank, Nimisha Asthana, and Mohammad Osama. "Data Analytics in Insurance Product Management." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 6, no. 1 (2024): 594–99. https://doi.org/10.60087/jaigs.v6i1.288.

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Data analytics as a part of insurance product management is revolutionizing the industry because with huge and constantly increasing piles of customer and claims data at their fingertips, insurers can make better decisions and improve many aspects of their operations. This paper discusses how the adaptation of risk models and artificial intelligence models helps insurers to improve evaluation criteria and policy premiums, as well as predict the occurrence of claims with a high degree of certainty. Challenging customer segments can be detected using big data analytics, which helps insurers bett

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Rajkumar, Govindaswamy Subbian. "Technology Driven Intelligent Risk & Fraud Assessment in Insurance." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 2 (2025): 686–93. https://doi.org/10.5281/zenodo.14928754.

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Technology Driven Intelligent Risk & Fraud Assessment in Insurance focuses on leveraging artificial intelligence (AI), machine learning (ML), blockchain, and predictive analytics to improve risk assessment and combat fraud. The study highlights the role of AI-driven predictive analytics, deep learning algorithms, blockchain for transparency, and automation to enhance accuracy, reduce fraudulent activities, and streamline insurance workflows. The approach analyzed real-world case study demonstrated the successful integration of these technologies into Guidewire ClaimCenter and PolicyCenter,

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Sivakumar, K. "AI for Business Transformation: on the Target Customers Group and Market." ComFin Research 13, S1-i1-Mar (2025): 220–23. https://doi.org/10.34293/commerce.v13is1-i1-mar.8683.

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Today’s trade or business development requires the implementation of various types of analytical methods, including artificial intelligence, i.e. landscape analytics, complexityanalytics, descriptive analytics, predictive analytics, and prescriptive analytics, support consumer services and business market development.Retaining consumers among competing companies;Eliminating fraud and risk; Handling market intelligence;Sense technology in business, applying AI for marketing.The role of artificial intelligence in insurance and financial institutions is becoming essential. Artificial intelligence

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Dissertations / Theses on the topic "Predictive Analytics In Insurance"

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Wex, Felix [Verfasser], and Dirk [Akademischer Betreuer] Neumann. "Coordination strategies and predictive analytics in crisis management = Koordinationsstrategien und Predictive Analytics im Krisenmanagement." Freiburg : Universität, 2013. http://d-nb.info/1114829102/34.

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Beligianni, Foteini. "Streaming Predictive Analytics on Apache Flink." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-171355.

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Data analysis and predictive analytics today are driven by large scale dis- tributed deployments of complex pipelines, guiding data cleaning, model training and evaluation. A wide range of systems and tools provide the basic abstractions for building such complex pipelines for offline data processing, however, there is an increasing demand for providing support for incremental models over unbounded streaming data. In this work, we focus on the prob- lem of modelling such a pipeline framework and providing algorithms that build on top of basic abstractions, fundamental to stream processing. We

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Bheda, Anuj. "Predictive analytics of active learning based education." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113509.

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Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 113-115).<br>Learning Analytics (LA) is defined as the collection, measurement, and analysis of data related to student performance such that the feedback from the analytical insights can be used to optimize student learning and improve student outcomes. Blended Learning (BL) is a teaching paradigm that involves a mix of face-to-face interactions in a classroom based setting along wi

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Neupane, Bijay. "Predictive Data Analytics for Energy Demand Flexibility." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-236309.

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The depleting fossil fuel and environmental concerns have created a revolutionary movement towards the installation and utilization of Renewable Energy Sources (RES) such as wind and solar energy. The RES entails challenges, both in regards to the physical integration into a grid system and regarding management of the expected demand. The flexibility in energy demand can facilitate the alignment of the supply and demand to achieve a dynamic Demand Response (DR). The flexibility is often not explicitly available or provided by a user and has to be analyzed and extracted automatically from hist

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Coles, James B. "Cross-channel predictive analytics for retail distribution decisions." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111531.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2017.<br>Thesis: S.M. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, in conjunction with the Leaders for Global Operations Program at MIT, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 91-93).<br>Distribution demand forecasting at Zara currently considers historical sales of products modified by expert knowledge input

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Naidoo, Jefrey Subramoney. "Forecasting recessions: The convergence of information and predictive analytics." THE UNIVERSITY OF ALABAMA, 2011. http://pqdtopen.proquest.com/#viewpdf?dispub=3439830.

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Wickramasuriya, Dilranjan S. "Predictive Analytics in Cardiac Healthcare and 5G Cellular Networks." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6980.

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This thesis proposes the use of Machine Learning (ML) to two very distinct, yet compelling, applications – predicting cardiac arrhythmia episodes and predicting base station association in 5G networks comprising of virtual cells. In the first scenario, Support Vector Machines (SVMs) are used to classify features extracted from electrocardiogram (EKG) signals. The second problem requires a different formulation departing from traditional ML classification where the objective is to partition feature space into constituent class regions. Instead, the intention here is to identify temporal pattern

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Wolbert, Marie. "Predictive analytics for inventory in a sporting goods organization." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81027.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Global Operations Program at MIT, 2013.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 46).<br>Inventory management for retail companies has become increasingly more important in recent years as competition grows and new supply chain models are implemented. Inventory levels have implications on not only the financial side of the business, but also on brand

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Alroomi, Azzam J. M. A. H. "Essays in forecasting financial markets with predictive analytics techniques." Thesis, Bangor University, 2018. https://research.bangor.ac.uk/portal/en/theses/essays-in-forecasting-financial-markets-with-predictive-analytics-techniques(a1bc7d33-04b0-416d-82ea-fbefd19da7ff).html.

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This PhD dissertation comprises four essays on forecasting financial markets with unsupervised predictive analytics techniques, most notably time series extrapolation methods and artificial neural networks. Key objectives of the research were reproducibility and replicability, which are fundamental principles in management science and, as such, the implementation of all of the suggested algorithms has been fully automated and completely unsupervised in R. As with any predictive analytics exercise, computational intensiveness is a significant challenge and criterion of performance and, thus, bo

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Pienaar, Celia. "Machine learning in predictive analytics on judicial decision-making." Master's thesis, Faculty of Science, 2021. http://hdl.handle.net/11427/33925.

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Legal professionals globally are under pressure to provide ‘more for less' – not an easy challenge in the era of big data, increasingly complex regulatory and legislative frameworks and volatile financial markets. Although largely limited to information retrieval and extraction, Machine Learning applications targeted at the legal domain have to some extent become mainstream. The startup market is rife with legal technology providers with many major law firms encouraging research and development through formal legal technology incubator programs. Experienced legal professionals are expected to

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Books on the topic "Predictive Analytics In Insurance"

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Kumar, Vijay, and Mangey Ram, eds. Predictive Analytics. CRC Press, 2020. http://dx.doi.org/10.1201/9781003083177.

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McCarthy, Richard V., Mary M. McCarthy, and Wendy Ceccucci. Applying Predictive Analytics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-83070-0.

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McCarthy, Richard V., Mary M. McCarthy, Wendy Ceccucci, and Leila Halawi. Applying Predictive Analytics. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14038-0.

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Stephen, Coggeshall, ed. Foundations of predictive analytics. CRC Press, 2012.

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Acito, Frank. Predictive Analytics with KNIME. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45630-5.

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Paczkowski, Walter R. Predictive and Simulation Analytics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31887-0.

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Seifert, Matthias, ed. Judgment in Predictive Analytics. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30085-1.

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author, Chaouchi Mohamed, and Jung Tommy author, eds. Predictive analytics for dummies. John Wiley & Sons, Inc., 2014.

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Klinger, Zita. Predictive Analytics im Controlling. Springer Fachmedien Wiesbaden, 2025. https://doi.org/10.1007/978-3-658-47146-0.

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Chorianopoulos, Antonios. Effective CRM using predictive analytics. John Wiley & Sons Inc., 2015.

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Book chapters on the topic "Predictive Analytics In Insurance"

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Lai, Tze Leung, and Haipeng Xing. "Surveillance and predictive analytics." In Data Science and Risk Analytics in Finance and Insurance. CRC Press, 2024. http://dx.doi.org/10.1201/9781315117041-8.

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Araz, Özgür M., and David L. Olson. "Risk Measures in Finance and Insurance." In Risk and Predictive Analytics in Business with R. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003562399-3.

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Scriney, Michael, Dongyun Nie, and Mark Roantree. "Predicting Customer Churn for Insurance Data." In Big Data Analytics and Knowledge Discovery. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59065-9_21.

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Sharma, Hitesh Kumar, Tanupriya Choudhury, and Teoh Teik Toe. "Machine Learning Based Predictive Analytics: A Use Case in Insurance Sector." In Decision Intelligence Analytics and the Implementation of Strategic Business Management. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-82763-2_14.

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Tkachenko, Roman, Ivan Izonin, Michal Greguš ml., Pavlo Tkachenko, and Ivanna Dronyuk. "Committee of the SGTM Neural-Like Structures with Extended Inputs for Predictive Analytics in Insurance." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27355-2_9.

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Kanjalkar, Jyoti, Kaivalya Aole, Abu Ansari, Harshal Abak, Aarya Tiwari, and Pramod Kanjalkar. "Analysis of Regular Machine Learning and Ensemble Learning Approaches for Term Insurance Prediction in Banking Data." In Data Management, Analytics and Innovation. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3245-6_14.

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Schawel, Christian, and Fabian Billing. "Predictive Analytics." In Top 100 Management Tools. Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-18917-4_68.

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Kenny, Peter. "Predictive Analytics." In Better Business Decisions from Data. Apress, 2014. http://dx.doi.org/10.1007/978-1-4842-0184-8_23.

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Cetintemel, Ugur. "Predictive Analytics." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_80669-2.

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Seitz, Janine. "Predictive Analytics." In Marktforschung der Zukunft - Mensch oder Maschine? Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-14539-2_5.

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Conference papers on the topic "Predictive Analytics In Insurance"

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Mahesh, Muppidi, B. Sasi Priyatham, G. SaiKumar, and K. Venkat Varshith Reddy. "Predictive Analytics for Medical Insurance Premiums." In 2025 5th International Conference on Pervasive Computing and Social Networking (ICPCSN). IEEE, 2025. https://doi.org/10.1109/icpcsn65854.2025.11034884.

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Jing, Junwen, Yinuo Du, Weijie Jin, and Qian Xie. "Machine Learning Predictive Analytics for Insurance Premiums." In 2024 International Conference on Interactive Intelligent Systems and Techniques (IIST). IEEE, 2024. http://dx.doi.org/10.1109/iist62526.2024.00142.

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Qi, Feiyue, Wanlian Chen, Ziyi Guo, Yiwen Dai, Haoyu Wang, and Minghui Ma. "Research on Catastrophe Insurance Strategy based on AHP-EWM and Grey Prediction Model." In 2024 3rd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI). IEEE, 2024. https://doi.org/10.1109/icdacai65086.2024.00044.

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Paulson, Joel. "Predictive Analytics for Chemical Processes." In 2024 American Control Conference (ACC). IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10644159.

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Kaushik, Priyanka, Saurabh Pratap Singh Rathore, Anand Singh Bisen, and Rachna Rathore. "Enhancing Insurance Claim Fraud Detection Through Advanced Data Analytics Techniques." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752284.

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Jain, Nishi, Saif O. Husain, Shanky Goyal, S. Hariharasudhan, Melvin Victor, and Manjula. "Predictive Analytics for Network Traffic Management." In 2024 IEEE International Conference on Communication, Computing and Signal Processing (IICCCS). IEEE, 2024. http://dx.doi.org/10.1109/iicccs61609.2024.10763554.

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P, Devikarthika, J. Nasakth, and Amsaveni K. "Optimizing Energy Consumption through Predictive Analytics." In 2025 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE). IEEE, 2025. https://doi.org/10.1109/iccrtee64519.2025.11053070.

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Xiong, Lu. "Comparative Study of Predictive Analytics Algorithms and Tools on Property and Casualty Insurance Solvency Prediction." In ICBIM 2020: 2020 The 4th International Conference on Business and Information Management. ACM, 2020. http://dx.doi.org/10.1145/3418653.3418663.

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Anand, Sangeeta, and Sumeet Sharma. "LONG-TERM CARE INSURANCE (LTCI) SYSTEMS MODERNIZATION USING CLOUD-BASED DATA ANALYTICS." In 12th International Conference on Signal Processing. Academy & Industry Research Collaboration, 2025. https://doi.org/10.5121/csit.2025.151107.

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Changing demographics, growing healthcare expenses, and better standards for digital service supply have long-term care insurance (LTCI) firms under more and more pressure to modernise. Standard long-term care insurance plans fall short in managing claims, assessing risks, ensuring policy compliance, or providing consumers with decent customer service. Usually, several data storage, human procedures, and outdated technology lead to these issues. These restrictions deliver more specialised, premium treatment, increase running expenses, and complicate response. This essay explores how employing

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Vij, Renu, and Hena Nazir. "Leveraging AI & MI for Predictive Analytics in Stress Level of Private Insurance Employees : Comprehensive Review." In 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI). IEEE, 2023. http://dx.doi.org/10.1109/icaiihi57871.2023.10489524.

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Reports on the topic "Predictive Analytics In Insurance"

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LaRaine Ingram, Keisha. Applied Sales Predictive Analytics for Business Development. Vilnius Business College, 2024. http://dx.doi.org/10.57005/ab.2024.1.2.

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In the dynamic business environment, leveraging predictive analytics for sales optimization and business development has become crucial for achieving sustained growth. As the e-commerce landscape continues to evolve, many e-businesses must harness the power of predictive analytics to anticipate sales trends and optimize business development strategies. This paper explores the application of sales predictive analytics, focusing on its role in forecasting sales, optimizing resource allocation, and enhancing customer relationship management. The application of predictive analytics in sales foreca

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Dereddy, Akhil Reddy. Predictive Analytics for Employee Attrition & Performance. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-1146.

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Agarwal, Vivek, James Smith, and Veryl Kirkpatrick. Predictive Data Analytics Framework Using Advanced Test Reactor Acoustic Data. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1897716.

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Bergman, Peter, Elizabeth Kopko, and Julio Rodriguez. Using Predictive Analytics to Track Students: Evidence from a Seven-College Experiment. National Bureau of Economic Research, 2021. http://dx.doi.org/10.3386/w28948.

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Kaur, Tarunpreet. Factors affecting health insurance premiums: Explorative and predictive analysis. Iowa State University, 2018. http://dx.doi.org/10.31274/cc-20240624-711.

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Criner, Nichole Marie, Manuel Salmeron, Xin Zhang, Shirley J. Dyke, Julio A. Ramirez, and Benjamin Eric Wogen. Predictive Analytics for Quantifying the Long-Term Costs of Defects During Bridge Construction. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317615.

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During the lifecycle of a bridge, deterioration of the concrete deck originates from many sources, e.g., corrosion due to water infiltration in conjunction with chlorides from the use of de-icing salts. Such deterioration may be affected by any one of the following six actions relevant to a bridge from conception to demolition: design, construction, in-service conditions, maintenance, repair and rehabilitation, and replacement. Many researchers have studied the relationships between these sources and their consequences. However, the relationship between construction defects and inspection prac

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Jafarpour, Behnam. Enabling Efficient Surveillance, Control, and Automation of Geothermal Operations with Advanced Predictive Analytics. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2311787.

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Goldsmith, Stephen, Susan Crawford, and Benjamin Weinryb Grohsgal. Innovations in Public Service Delivery: Issue No. 4: Predictive Analytics: Driving Improvements Using Data. Inter-American Development Bank, 2016. http://dx.doi.org/10.18235/0000421.

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Fickas, Stephen. Green Waves, Machine Learning, and Predictive Analytics: Making Streets Better for People on Bikes. Transportation Research and Education Center (TREC), 2021. http://dx.doi.org/10.15760/trec.264.

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Weinryb Grohsgal, Benjamin, Stephen Goldsmith, and Susan Crawford. Innovations in Public Service Delivery: Issue No. 4: Predictive Analytics: Driving Improvements Using Data. Inter-American Development Bank, 2016. http://dx.doi.org/10.18235/0007971.

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Predictive analytics, although widely utilized in the private sector, still stir uncertainty and concern on the part of public sector leaders. Daily, however, some national or subnational government makes a dramatic breakthrough in how it delivers services as a result of collecting and analyzing data. This paper examines how officials can use properly analyzed historical data to look for patterns and trends to reorganize the way they deliver services, anticipate future events, and often even prevent potential problems.

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