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Journal articles on the topic 'Predictive Analytics In Insurance'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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1

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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2

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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9

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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Chadha, Kawaljeet Singh. "Predictive Risk Modeling in P&C Insurance Using Guidewire DataHub and Power BI Embedded Analytics." International journal of networks and security 5, no. 02 (2025): 1–29. https://doi.org/10.55640/ijns-05-02-01.

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P&C insurers are increasingly pressured to identify and effectively predict risk. While traditional methods, such as actuarial models and manual assessments, are effective for identifying patterns in large-scale policy and claims data, they struggle to capture complex patterns, like resistance curves. This paper examines how predictive risk modelling can be implemented in practice using Guidewire DataHub and Power BI Embedded Analytics. Power BI is used for interactive visualization and real-time decision support, whereas Guidewire Data Hub is utilized as a centralized platform for storing

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Researcher. "CLOUD-POWERED PREDICTIVE ANALYTICS IN INSURANCE: ADVANCING RISK ASSESSMENT THROUGH AI INTEGRATION." International Journal of Engineering and Technology Research (IJETR) 9, no. 2 (2024): 195–206. https://doi.org/10.5281/zenodo.13757195.

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This article examines the transformative impact of predictive analytics, powered by cloud computing and artificial intelligence (AI), on risk assessment practices in the insurance industry. Through a comprehensive analysis of data from multiple insurance providers, we investigate how these technologies enhance the accuracy of risk predictions, improve customer segmentation, and enable proactive claims management. Our findings demonstrate a significant improvement in underwriting precision, with a 20% reduction in fraudulent claims and a 15% increase in customer retention rates among early adop

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Liu, Guanyu. "Research on Risk Assessment and Underwriting Decision Making Based on ARIMA Model." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 1253–62. http://dx.doi.org/10.62051/0eqdm845.

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Addressing the critical challenge of extreme weather events, this study introduces a comprehensive model for enhancing insurance underwriting strategies through predictive analytics and risk assessment methodologies. Utilizing an integrated approach that combines Autoregressive Integrated Moving Average (ARIMA) for forecasting extreme weather occurrences, Fuzzy Comprehensive Evaluation for assessing regional payment capabilities, and Monte Carlo simulations for detailed risk quantification, the re-search aims to refine the insurance industry's capacity for anticipating and mitigating financial

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Kumar, Manoj. "Predictive Analytics in Healthcare Insurance Revolutionizing Risk Management and Underwriting." Journal of Artificial Intelligence, Machine Learning and Data Science 2, no. 3 (2024): 1669–73. https://doi.org/10.51219/jaimld/manoj-kumar/372.

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15

Anand, Sangeeta. "AI-Based Predictive Analytics for Identifying Fraudulent Health Insurance Claims." International Journal of AI, BigData, Computational and Management Studies 4 (2023): 39–47. https://doi.org/10.63282/3050-9416.ijaibdcms-v4i2p106.

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Shravan Kumar Joginipalli. "Predictive analytics for catastrophic risk management: Leveraging telematics and IoT data in property insurance." International Journal of Science and Research Archive 5, no. 2 (2022): 387–91. https://doi.org/10.30574/ijsra.2022.5.2.0076.

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Catastrophic risk management in property insurance demands proactive strategies to mitigate losses from natural disasters such as hurricanes, wildfires, and floods. Traditional methods often lack real-time data integration, leading to delayed responses and suboptimal risk assessments. This paper proposes a predictive analytics framework that leverages telematics and IoT data to enhance catastrophic risk prediction and management. By integrating real-time sensor data, historical weather patterns, and geographic information systems (GIS), the framework employs machine learning models to forecast

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Barua, Tonmoy, and Sunanda Barua. "REVIEW OF DATA ANALYTICS AND INFORMATION SYSTEMS IN ENHANCING EFFICIENCY IN FINANCIAL SERVICES: CASE STUDIES FROM THE INDUSTRY." Global Mainstream Journal 1, no. 3 (2024): 1–13. http://dx.doi.org/10.62304/ijmisds.v1i3.160.

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This study explores the transformative impact of integrating data analytics and information systems on enhancing efficiency in the financial services industry. The research highlights significant improvements in operational efficiency, risk management, and customer satisfaction through detailed case studies of JPMorgan Chase, Allstate Insurance, BlackRock, and Bank of America. The findings reveal that AI-driven analytics tools at JPMorgan Chase led to a 30% reduction in fraud-related losses and a 20% increase in customer satisfaction. Through predictive analytics, Allstate Insurance achieved a

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18

Ibrahim Adedeji Adeniran, Christianah Pelumi Efunniyi, Olajide Soji Osundare, and Angela Omozele Abhulimen. "Advancements in predictive modeling for insurance pricing: Enhancing risk assessment and customer segmentation." International Journal of Management & Entrepreneurship Research 6, no. 8 (2024): 2835–48. http://dx.doi.org/10.51594/ijmer.v6i8.1469.

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This review paper explores the significant advancements in predictive modeling for insurance pricing, emphasizing its role in enhancing risk assessment and customer segmentation. The paper begins with an overview of the evolution of predictive modeling in the insurance industry, tracing the shift from traditional methods to modern, data-driven approaches powered by machine learning, artificial intelligence (AI), and big data. It highlights how these advancements have improved the accuracy of risk assessment, enabling insurers to develop more precise pricing strategies. The paper also discusses

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Xiong, Lu, Tingting Sun, and Randall Green. "Predictive analytics for 30-day hospital readmissions." Mathematical Foundations of Computing 5, no. 2 (2022): 93. http://dx.doi.org/10.3934/mfc.2021035.

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<p style='text-indent:20px;'>The 30-day hospital readmission rate is the percentage of patients who are readmitted within 30 days after the last hospital discharge. Hospitals with high readmission rates would have to pay penalties to the Centers for Medicare &amp; Medicaid Services (CMS). Predicting the readmissions can help the hospital better allocate its resources to reduce the readmission rate. In this research, we use a data set from a hospital in North Carolina during the years from 2011 to 2016, including 71724 hospital admissions. We aim to provide a predictive model that can

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20

Marciuc, Monica Andreea. "Predictive modeling for claims in automobile insurance." Virgil Madgearu Review of Economic Studies and Research 17, no. 2 (2024): 79–99. https://doi.org/10.24193/rvm.2024.17.118.

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The rise of advanced machine learning methods has revolutionized the landscape of predictive modeling in the automobile insurance sector. This paper presents the relevant literature review on the use of machine learning methods, including gradient boosting, random forests, and decision trees, to model claims in automobile insurance. By synthesizing findings from key studies, we conclude on the predictive performance of these methods compared to traditional actuarial models and identify emerging trends and challenges in this domain. Our analysis highlights how data-driven approaches enhance pri

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Rahman, Siddikur, Abu Sayem, Shariar Emon Alve, et al. "The role of AI, big data and predictive analytics in mitigating unemployment insurance fraud." International Journal of Business Ecosystem & Strategy (2687-2293) 6, no. 4 (2024): 253–70. https://doi.org/10.36096/ijbes.v6i4.679.

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The fraudulent claims for Unemployment Insurance (UI) have also risen massively in the United States especially during the onset of COVID-19 pandemic with billions of dollars that were lost. These approaches applied formerly in fraud detection and prevention have been challenged by new and advanced fraud systems. For this reason, AI, Big Data and Predictive Analytics are now crucial for improving fraud mitigation in UI programs. The aim of this research is to understand how far AI, Big Data and Predictive Analytics have been utilized, for how effective they are and the barriers they pose in ta

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22

Reddy Adavelli, Sateesh. "Beyond the Claims: Emerging AI Models and Predictive Analytics in Property & Casualty Insurance Risk Assessment." International Journal of Science and Research (IJSR) 13, no. 7 (2024): 1625–31. https://doi.org/10.21275/sr24077085515.

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23

Zahavi, Jacob. "Predictive analytics in the era of big data." Open Access Government 45, no. 1 (2025): 232–33. https://doi.org/10.56367/oag-045-11798.

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Predictive analytics in the era of big data Jacob Zahavi, from The Coller School of Management at Tel Aviv University, discusses the components of predictive analytics (PA) and the increasing complexity of PA problems in the world of Big Data. This is the era of Big Data. Big Data are datasets that are too large and complex to handle using traditional data-processing tools and software. Data nowadays comes in huge quantities (volume), in different forms (velocity), and in a multitude of formats (variety). Some also add ‘value’ to this list. While raw data doesn’t have a value of its own, it ca

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Chandran, Kavya. "Optimizing Healthcare Finance: A Predictive Modeling Approach to Medical Insurance Premium." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 3066–69. http://dx.doi.org/10.22214/ijraset.2024.59574.

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Abstract: This research delves into optimizing healthcare finance through predictive modeling to forecast medical insurance premiums accurately. By harnessing a robust dataset and integrating advanced analytics, this study meticulously constructs models that allow insurance companies to price their policies competitively, ensuring both profitability and fairness. Employing a variety of machine learning algorithms, including linear regression, decision trees, random forests, and gradient boosting, we thoroughly assess the influence of critical factors such as age, BMI, gender, and regional heal

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Pattanshetty, Renu. "Predictive analytics for “SMART”er hospitals and health care: Virtuality versus reality." Journal of Dr. YSR University of Health Sciences 13, no. 3 (2024): 201–4. http://dx.doi.org/10.4103/jdrysruhs.jdrysruhs_150_21.

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ABSTRACT Healthcare is a booming sector of economy in many countries including India. With the boom and the growth comes the challenges that include exorbitant costs, inefficiencies in the healthcare delivery, complexities involving the disease status, diagnosis, treatment, and recovery. Predictive Analytics” is an advanced form of analytics and has gained a scientific repute as the “BIG DATA” related to health care. It is a branch of data engineering that predicts true existences and/or probability by utilizing the existing data that would use the “data mining method” to predict two things th

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Vare, Ms Yogita Ashok. "Integrating Banking and Health Insurance for Secure Future." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–10. http://dx.doi.org/10.55041/ijsrem28065.

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Abstract -In an era marked by technological advancements and interconnected financial services, the integration of banking and health insurance emerges as a strategic approach to foster a secure future for individuals. This conceptual framework envisions a seamless synergy between the banking and health insurance sectors, leveraging data analytics, blockchain technology, and artificial intelligence to enhance customer experiences, mitigate risks, and promote overall financial well- being. The integration process involves the convergence of financial and health data, allowing for a more compreh

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Rajesh, Goyal. "Data-Driven Decision Making in Claims Management: Leveraging Predictive Analytics to Optimize Claim Trends and Processing Times." Journal of Advances in Developmental Research 12, no. 1 (2021): 1–17. https://doi.org/10.5281/zenodo.14851355.

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The abstract of this research paper offers a clear and compelling overview of the study's objectives, methodology, and key findings, focusing on the transformative potential of data-driven decision-making in insurance claims management. At its core, the research examines how the integration of predictive analytics—particularly through the use of machine learning models like Gradient Boosting Machines (GBM)—can significantly enhance operational efficiency in the claims process.The abstract introduces a comprehensive model that combines both internal and external data variables, such

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Courage Idemudia, Edith Ebele Agu, and Shadrack Obeng. "Analyzing how data analytics is used in detecting and preventing fraudulent health insurance claims." International Journal of Frontiers in Science and Technology Research 7, no. 1 (2024): 048–56. http://dx.doi.org/10.53294/ijfstr.2024.7.1.0045.

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Health insurance fraud poses significant financial and operational challenges, necessitating the implementation of advanced data analytics for effective detection and prevention. This review explores the various techniques employed in data analytics to identify and mitigate fraudulent health insurance claims. Descriptive analytics aids in uncovering patterns and anomalies in historical claims data, while predictive analytics leverages statistical models and machine learning to forecast potential fraud. Advanced techniques, including machine learning and artificial intelligence, facilitate real

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Alomair, Gadir. "Predictive performance of count regression models versus machine learning techniques: A comparative analysis using an automobile insurance claims frequency dataset." PLOS ONE 19, no. 12 (2024): e0314975. https://doi.org/10.1371/journal.pone.0314975.

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Accurate forecasting of claim frequency in automobile insurance is essential for insurers to assess risks effectively and establish appropriate pricing policies. Traditional methods typically rely on a Poisson distribution for modeling claim counts; however, this approach can be inadequate due to frequent zero-claim periods, leading to zero inflation in the data. Zero inflation occurs when more zeros are observed than expected under standard Poisson or negative binomial (NB) models. While machine learning (ML) techniques have been explored for predictive analytics in other contexts, their appl

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Julker, Nain. "Ai-driven CRM systems in insurance: Personalization at scale." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 2850–65. https://doi.org/10.5281/zenodo.14908920.

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The purpose of this research paper investigates artificial intelligence and data analytics phenomena which impact the financial services industry specifically in Customer Relationship Management systems implementation. This document examines contemporary CRM system development together with artificial intelligences in customer analytics and their practical and complex implementation challenges. This research explores how artificial intelligence enhances both personalization operations and customer information and decision-making through natural language processing and machine learning and pred

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Barry, Laurence, and Arthur Charpentier. "Personalization as a promise: Can Big Data change the practice of insurance?" Big Data & Society 7, no. 1 (2020): 205395172093514. http://dx.doi.org/10.1177/2053951720935143.

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The aim of this article is to assess the impact of Big Data technologies for insurance ratemaking, with a special focus on motor products.The first part shows how statistics and insurance mechanisms adopted the same aggregate viewpoint. It made visible regularities that were invisible at the individual level, further supporting the classificatory approach of insurance and the assumption that all members of a class are identical risks. The second part focuses on the reversal of perspective currently occurring in data analysis with predictive analytics, and how this conceptually contradicts the

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Abuzaid, Ali, and Iyad S. Alkrunz. "Forecasting Next Year's Health Insurance Claims Using Machine Learning Models." JITSI : Jurnal Ilmiah Teknologi Sistem Informasi 6, no. 2 (2025): 120–31. https://doi.org/10.62527/jitsi.6.2.443.

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This study explores the transformative potential of big data analytics in the realm of health insurance risk management. Focusing on data sourced from Highmark Health from 2015 to 2018, the research aims to evaluate the efficacy of advanced data manipulation techniques and machine learning models in enhancing predictive accuracy. The analysis involves a comprehensive examination of Health Maintenance Organization (HMO) and Preferred Provider Organization (PPO) plans, with rigorous data preparation processes such as cleaning, aggregation, feature engineering, and outlier handling to ensure mode

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Nandish Shivaprasad. "Data Modelling for Health Insurance Claims Analytics." Kuwait Journal of Advanced Computer Technology 2, no. 1 (2024): 01–12. https://doi.org/10.52783/kjact.258.

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This paper aims at discussing the analysis of health insurance claim through risk classification, fraudulence and cost prediction models. Combined with state-of-art data preprocessing and modelling techniques, insurers can better drive decision, minimize fraud, and better plan for financials. Logistic regression, random forest, gradient boost and models of similar category help in pattern analysis and cost of claim forecasting. They further effectiveness, equity and customer relations for implementing sound insurance that is sustainable. This work therefore emphatically speaks to the Bar on th

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Preetham, Reddy Kaukuntla. "Leveraging Predictive Analytics and Clustering for Personalized Financial Planning in Life Insurance: A Data-Driven Approach to Tailored Policyholder Advice." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 8, no. 3 (2020): 1–8. https://doi.org/10.5281/zenodo.14763332.

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The life insurance business is in the process of change in response to data techniques, which makes it possible to engage and advise customers more effectively. This paper aims at analyzing the use of predictive analytics and clustering for probable improvement in the financial forecasting of life insurance policyholders. The key methods looked at include k-means clustering, hierarchical clustering as well as regression models that sort the policyholders into segments by demography, spending power or major life events. These techniques are discussed in this paper with respect to the results th

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Julker Nain. "Ai-driven CRM systems in insurance: Personalization at scale." World Journal of Advanced Research and Reviews 23, no. 2 (2024): 2850. https://doi.org/10.30574/wjarr.2024.23.2.2523.

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The purpose of this research paper investigates artificial intelligence and data analytics phenomena which impact the financial services industry specifically in Customer Relationship Management systems implementation. This document examines contemporary CRM system development together with artificial intelligences in customer analytics and their practical and complex implementation challenges. This research explores how artificial intelligence enhances both personalization operations and customer information and decision-making through natural language processing and machine learning and pred

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Odetunde, Azeez, Bolaji Iyanu Adekunle, and Jeffrey Chidera Ogeawuchi. "Using Predictive Analytics and Automation Tools for Real-Time Regulatory Reporting and Compliance Monitoring." International Journal of Multidisciplinary Research and Growth Evaluation 3, no. 2 (2022): 650–61. https://doi.org/10.54660/.ijmrge.2022.3.2.650-661.

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In today’s complex and dynamic regulatory environment, financial and insurance institutions face increasing pressure to ensure compliance across multiple jurisdictions in real-time. The growing volume and sophistication of regulatory requirements necessitate the integration of advanced technological solutions to enhance the efficiency and effectiveness of compliance programs. This explores the use of predictive analytics and automation tools for real-time regulatory reporting and compliance monitoring. Predictive analytics harnesses large datasets and machine learning algorithms to anticipate

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Mueller, Erik, J. S. Onésimo Sandoval, Srikanth Mudigonda, and Michael Elliott. "A Cluster-Based Machine Learning Ensemble Approach for Geospatial Data: Estimation of Health Insurance Status in Missouri." ISPRS International Journal of Geo-Information 8, no. 1 (2018): 13. http://dx.doi.org/10.3390/ijgi8010013.

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Mainstream machine learning approaches to predictive analytics consistently prove their ability to perform well using a variety of datasets, although the task of identifying an optimally-performing machine learning approach for any given dataset becomes much less intuitive. Methods such as ensemble and transformation modeling have been developed to improve upon individual base learners and datasets with large degrees of variance. Despite the increased generalizability and flexibility of ensemble approaches, the cost often involves sacrificing inference for predictive ability. This paper introd

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Manakhari, Sushanth, and Yanzhen Qu. "Improving the Accuracy and Performance of Deep Learning Model by Applying Hybrid Grey Wolf Whale Optimizer to P&C Insurance Data." European Journal of Electrical Engineering and Computer Science 7, no. 4 (2023): 17–26. http://dx.doi.org/10.24018/ejece.2023.7.4.548.

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The insurance industry is based on risk calculations, high profits, and detailed information. The predictive models that insurance companies utilize allow insurance companies to make accurate decisions about the insurance sector. This research focuses on improving the accuracy of predicting customers of Property and Casualty (P&C) insurance. In this study, a reliable quantitative analytical big data method has been developed, and the Hybrid Grey Wolf and Whale Optimization (HGWWO) is utilized with Deep Learning Model for evaluating customer behavior of the customers of P&C insurance. T

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Senthil, P. "An Improved Gradient Boosted Algorithms Based Solutions Predictive Model (Trade)." Asian Journal of Managerial Science 5, no. 1 (2016): 30–40. http://dx.doi.org/10.51983/ajms-2016.5.1.1198.

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In this paper, we describe a general process on how to integrate different types of predictive models within an organization to fully leverage the benefits of predictive modeling. The three major predictive modeling applications discussed in this paper are marketing, pricing, and GBAAlgorithm models. These applications have been well applied and published over the past several years for the Property and Casualty Manufacturing Industry, but this paper and discussions focused on their individual application. We believe that significant value can be realized if they are fully integrated, offering

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Ramanamuni, Sandeep. "Risk Management in Digital Transformation Projects." International Journal of Multidisciplinary Research and Growth Evaluation. 2, no. 3 (2021): 567–70. https://doi.org/10.54660/.ijmrge.2021.2.3.567-570.

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The COVID-19 pandemic has disturbed global supply chains. It has pushed organizations to speed up their digital transformations toward resilience and continuity. This paper throws due light on the risk management practices in digital transformation projects while focusing on the adoption of advanced supply chain solutions. The paper discusses the key risk factors for supply chains that are operating in the post-pandemic era. This includes supply disruptions, cybersecurity threats, and integration challenges. The study argues that digital technologies, such as blockchain, IoT, AI, predictive an

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Chandra Prakash Kathroju. "Transforming Insurance: The Technical Convergence of AI, ML, and Big Data in Cloud Platforms." Journal of Computer Science and Technology Studies 7, no. 3 (2025): 946–56. https://doi.org/10.32996/jcsts.2025.7.3.106.

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The convergence of Artificial Intelligence, Machine Learning, and Big Data with cloud technology is fundamentally reshaping the insurance industry landscape. These technologies drive automation, predictive insights, and personalized customer experiences—essential factors for success in modern insurance markets. Cloud-based platforms enable insurers to harness these capabilities through scalable architectures that support sophisticated analytics workflows. This technical examination explores how these technologies are reengineering core insurance operations, from underwriting algorithms to clai

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Olawale, Habeeb Olatunji, Ngozi Joan Isibor, and Joyce Efekpogua Fiemotongha. "A Predictive Compliance Analytics Framework Using AI and Business Intelligence for Early Risk Detection." International Journal of Management and Organizational Research 2, no. 2 (2023): 190–95. https://doi.org/10.54660/ijmor.2023.2.2.190-195.

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In an era of escalating regulatory complexity and costly compliance breaches, this paper proposes a predictive compliance analytics framework that harnesses artificial intelligence and business intelligence tools to enable early risk detection in financial and insurance sectors. By integrating machine learning models with interactive dashboard platforms such as Tableau, SQL, and Python, the framework transforms traditional reactive compliance approaches into a proactive, data-driven system. The framework incorporates diverse data sources, advanced algorithms, and real-time visualization to ide

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Sumathy, Dr M., and Ms Bharathi M. "Insurtech\'s Role in Enhancing Healthcare Insurance Accessibility and Efficiency in India." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 1890–95. http://dx.doi.org/10.22214/ijraset.2024.65519.

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Abstract: Insurance Technology (Insurtech) is transforming healthcare this paper explores how innovative technologies such as digital on boarding, automated claims processing, wearable health trackers, and predictive analytics are reshaping the industry. These advancements have not only streamlined processes but also brought insurance services to underserved areas, offering personalized solutions that significantly enhance customer satisfaction. Technologies like telemedicine, block chain for secure transactions, and AI-driven risk analysis are playing a transformative role in improving transp

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Naveen Kondeti. "The Future of Insurance Technology: Leveraging AI for Transformation in Property and Casualty." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2919–26. https://doi.org/10.32628/cseit251112295.

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This article explores the transformative impact of Artificial Intelligence (AI) on the property and casualty (P&C) insurance sector, focusing on key technological advancements reshaping core insurance operations. This article examines how AI-driven solutions are revolutionizing traditional underwriting processes, enhancing claims management efficiency, and strengthening fraud detection capabilities. Through an analysis of cloud-based platforms like Guidewire, it investigates the integration challenges and opportunities in implementing AI solutions within existing insurance infrastructure.

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Bani, Pristiwanto, and Nurhayati Siregar. "Forensic Audit in Fraud Detection and Prevention for General Insurance Claims: A Literature Review." International Journal of Integrative Research 3, no. 3 (2025): 191–204. https://doi.org/10.59890/ijir.v3i3.422.

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This paper presents a comprehensive literature review on the application of forensic audit techniques to detect and prevent fraud in general insurance claims. The review encompasses studies published between 2020 and 2025, focusing on the evolving role of forensic auditing within the insurance industry. Our analysis reveals several key themes: 1) The increasing sophistication of insurance fraud schemes has necessitated more advanced forensic audit methods, including the integration of data analytics and artificial intelligence. 2) Forensic auditing plays a critical role in uncovering existing

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Dr.S., K. Manju Bargavi. "THE AI SHIELD: DETECTING FINANCIAL CRIMES IN REAL-TIME." International Journal of Advances in Engineering & Scientific Research 12, no. 2 (2025): 11–28. https://doi.org/10.5281/zenodo.15374618.

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<strong>ABSTRACT</strong> <em>As the number of digital transactions surges, so does financial fraud, revealing the shortcomings of traditional rule-based detection systems. Artificial Intelligence, with its ability to analyze real-time data and learn from patterns, has emerged as a powerful method for detecting and mitigating fraud. This paper discusses techniques driven by AI, including anomaly detection and predictive analytics, delves into challenges that need to be addressed (e.g., privacy and adversarial attacks), and highlights the current state of real-world applications across banking,

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Omotayo Bukola Adeoye, Chinwe Chinazo Okoye, Onyeka Chrisanctus Ofodile, Olubusola Odeyemi, Wilhelmina Afua Addy, and Adeola Olusola Ajayi-Nifise. "INTEGRATING ARTIFICIAL INTELLIGENCE IN PERSONALIZED INSURANCE PRODUCTS: A PATHWAY TO ENHANCED CUSTOMER ENGAGEMENT." International Journal of Management & Entrepreneurship Research 6, no. 3 (2024): 502–11. http://dx.doi.org/10.51594/ijmer.v6i3.840.

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The integration of Artificial Intelligence (AI) in the insurance sector has ushered in a new era of personalized insurance products, offering enhanced customer engagement and satisfaction. This review explores the transformative potential of AI in reshaping the landscape of insurance services, focusing specifically on the augmentation of customer engagement through personalized offerings. AI-driven algorithms and machine learning techniques enable insurers to analyze vast amounts of data with unprecedented speed and accuracy, facilitating the customization of insurance products to meet individ

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Xie, Shengkun, and Nathaniel Ho. "Unified Spatial Clustering of Territory Risk to Uncover Impact of COVID-19 Pandemic on Major Coverages of Auto Insurance." Risks 12, no. 7 (2024): 108. http://dx.doi.org/10.3390/risks12070108.

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This research delves into the fusion of spatial clustering and predictive modeling within auto insurance data analytics. The primary focus of this research is on addressing challenges stemming from the dynamic nature of spatial patterns in multiple accident year claim data, by using spatially constrained clustering. The spatially constrained clustering is implemented under hierarchical clustering with a soft contiguity constraint. It is highly desirable for insurance companies and insurance regulators to be able to make meaningful comparisons of loss patterns obtained from multiple reporting y

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Mahaboobsubani, Shaik. "Intelligent Automation for Insurance Claims Processing." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 7, no. 4 (2019): 1–9. https://doi.org/10.5281/zenodo.14352226.

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The transformational effect of intelligent automation through machine learning models on insurance claim processing. Traditional manually operated workflows within claims processing are always vulnerable to inefficiency, inaccuracies, and fraudulent cases. Intelligent automation overcomes these challenges by streamlining processes that offer rapid claim validation and more accurate detection of fraud cases. The research work illustrates an integrated claims-processing mechanism that leverages predictive analytics and anomaly detection to filter out fraudulent patterns. Accuracy rates, time-to-

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Researcher. "The Impact of Cloud and AI on Actuarial Science and Life Insurance Pricing Models." International Journal of Computer Science and Information Technology Research (IJCSITR) 5, no. 1 (2024): 55–66. https://doi.org/10.5281/zenodo.14645152.

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<em>The integration of cloud computing and artificial intelligence (AI) has significantly transformed actuarial science, particularly in life insurance pricing models. Traditional actuarial methods, often limited by static assumptions and computational constraints, are being replaced by dynamic, data-driven approaches that incorporate real-time customer behavior and external variables. AI-powered tools enable advanced risk assessment and predictive analytics, while cloud platforms provide scalable infrastructure for processing large datasets efficiently. This paper explores the evolution of ac

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