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Artículos de revistas sobre el tema "Claim detection"

Autor: Grafiati

Publicado: 10 de marzo de 2023

Última modificación: 31 de julio de 2025

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1

Sai Santosh Goud Bandari. "Machine Learning (ML) based Anomaly Detection in Insurance Industries." Journal of Information Systems Engineering and Management 10, no. 32s (2025): 13–21. https://doi.org/10.52783/jisem.v10i32s.5182.

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Handling claims presents significant difficulties for the insurance sector particularly in cases of duplicate claims, missing information, and false claims. Conventional manual techniques are prone to mistakes and inefficiencies, which substantially raises running expenses. This work presents an automated machine learning (ML) based solution for these problems. DBSCAN Clustering, Isolation Forest Classifier, and Random Forest Classifier are three specific ML techniques applied here. Early intervention is possible with the Random Forest Classifier as it can detect claims with lacking proof. Whi

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2

K., P. PORKODI. "FRAUD CLAIM DETECTION USING SPARK." IJIERT - International Journal of Innovations in Engineering Research and Technology 4, no. 2 (2017): 10–13. https://doi.org/10.5281/zenodo.1462257.

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<strong>Objective:To reduce the fraud claims in health insurances companies and to improve outcomes in health care industry Analysis:In the existing system,Apache hadoop and Apache hive is used for processing data,it is a batch processing syste m. In proposed system,Apache spark is used for processing streaming data. Findings:EHR record is used as data source,it contains unique id for patients across world,so it is very easy to detect fraud claim with help of patientid. Apache spark processing streaming data on regular basis. But in existing system Apache hadoop and Apache hive takes hours of

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3

Agarwal, Shashank. "An Intelligent Machine Learning Approach for Fraud Detection in Medical Claim Insurance: A Comprehensive Study." Scholars Journal of Engineering and Technology 11, no. 09 (2023): 191–200. http://dx.doi.org/10.36347/sjet.2023.v11i09.003.

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Medical claim insurance fraud poses a significant challenge for insurance companies and the healthcare system, leading to financial losses and reduced efficiency. In response to this issue, we present an intelligent machine- learning approach for fraud detection in medical claim insurance to enhance fraud detection accuracy and efficiency. This comprehensive study investigates the application of advanced machine learning algorithms for identifying fraudulent claims within the insurance domain. We thoroughly evaluate several candidate algorithms to select an appropriate machine learning algorit

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4

Prakosa, Hendri Kurniawan, and Nur Rokhman. "Anomaly Detection in Hospital Claims Using K-Means and Linear Regression." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 15, no. 4 (2021): 391. http://dx.doi.org/10.22146/ijccs.68160.

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BPJS Kesehatan, which has been in existence for almost a decade, is still experiencing a deficit in the process of guaranteeing participants. One of the factors that causes this is a discrepancy in the claim process which tends to harm BPJS Kesehatan. For example, by increasing the diagnostic coding so that the claim becomes bigger, making double claims or even recording false claims. These actions are based on government regulations is including fraud. Fraud can be detected by looking at the anomalies that appear in the claim data.This research aims to determine the anomaly of hospital claim

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5

Goutham, Bilakanti. "Enhancing Claim Processing Efficiency with Generative AI." International Journal of Leading Research Publication 3, no. 1 (2022): 1–11. https://doi.org/10.5281/zenodo.15196823.

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The use of Generative AI in claim processing, via diversified intake channels, including emails, faxed submissions, and intake channels that are call-center in nature. Under traditional claim processing, there is always a great amount of manual labor in obtaining, validating, and processing information vis-a-vis claims, which results in inefficiencies and delays. Advanced AI models such as NLP and GANs are used to automate data extraction, detection of anomalies, and decision-making, thereby reducing the processing time and the operational cost of processing claims. Automated intelligence incr

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6

IKUOMOLA, A. J., and O. E. Ojo. "AN EFFECTIVE HEALTH CARE INSURANCE FRAUD AND ABUSE DETECTION SYSTEM." Journal of Natural Sciences Engineering and Technology 15, no. 2 (2017): 1–12. http://dx.doi.org/10.51406/jnset.v15i2.1662.

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Due to the complexity of the processes within healthcare insurance systems and the large number of participants involved, it is very difficult to supervise the systems for fraud. The healthcare service providers’ fraud and abuse has become a serious problem. The practices such as billing for services that were never rendered, performing unnecessary medical services and misrepresenting non-covered treatment as covered treatments etc. not only contribute to the problem of rising health care expenditure but also affect the health of the patients. Traditional methods of detecting health care fraud

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7

Faseela, V. S., and Dr.P.Thangam. "A Review on Health Insurance Claim Fraud Detection." International Journal of Engineering Research & Science 4, no. 9 (2018): 26–28. https://doi.org/10.5281/zenodo.1441226.

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<strong><em>Abstract—</em></strong> <em>The anomaly or outlier detection is one of the applications of data mining. The major use of anomaly or outlier detection is fraud detection. </em><em>Health care fraud leads to substantial losses of money each year in many countries. Effective fraud detection is important for reducing the cost of Health care system. This paper reviews the various approaches used for detecting the fraudulent activities in Health insurance claim data. The approaches reviewed in this paper are Hierarchical Hidden Markov Models and Non Negative Matrix Factorization. T

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8

Nortey, Ezekiel N. N., Reuben Pometsey, Louis Asiedu, Samuel Iddi, and Felix O. Mettle. "Anomaly Detection in Health Insurance Claims Using Bayesian Quantile Regression." International Journal of Mathematics and Mathematical Sciences 2021 (February 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/6667671.

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Research has shown that current health expenditure in most countries, especially in sub-Saharan Africa, is inadequate and unsustainable. Yet, fraud, abuse, and waste in health insurance claims by service providers and subscribers threaten the delivery of quality healthcare. It is therefore imperative to analyze health insurance claim data to identify potentially suspicious claims. Typically, anomaly detection can be posited as a classification problem that requires the use of statistical methods such as mixture models and machine learning approaches to classify data points as either normal or

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9

Siva, Krishna Jampani. "Fraud Detection in Insurance Claims Using AI." Journal of Scientific and Engineering Research 6, no. 1 (2019): 302–10. https://doi.org/10.5281/zenodo.14637405.

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The insurance industry has faced issues with fraudulent claims, which have resulted in financial losses and operational inefficiencies. Integrating Artificial Intelligence offers a transformative way of detecting fraud by analyzing patterns in claim histories and customer profiles, along with external datasets. The use of AI-driven techniques, such as machine learning algorithms, natural language processing, and anomaly detection models, now allows insurers to detect fraud with greater precision and efficiency. These systems use supervised and unsupervised learning methods for outlier detectio

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10

Sagar, Amula Arun. "Insurance Fraud Detection Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 7 (2025): 1799–804. https://doi.org/10.22214/ijraset.2025.73264.

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The detection of fraudulent claims has become a significant challenge in the insurance industry, where manual review processes and rule-based systems often fall short in identifying complex, evolving fraud patterns. This project presents a datadriven approach to fraud detection using a real-world insurance dataset composed of 1000 policyholder records, with features including customer demographics, claim details, incident types, and vehicle information. The study employs supervised machine learning algorithms—Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost)—to classify insu

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11

Bakeyalakshmi, P., and S. K. Mahendran. "Enhanced replica detection scheme for efficient analysis of intrusion detection in MANET." International Journal of Engineering & Technology 7, no. 1.1 (2017): 565. http://dx.doi.org/10.14419/ijet.v7i1.1.10169.

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Nowadays, detection scheme of intrusion is placing a major role for efficient access and analysis in Mobile Ad-hoc network (MANET). In the past, the detection scheme of Intrusion was used to identify the efficiency of the network and in maximum systems it performs with huge rate of false alarm. In this paper, an Effective approach of the Enhanced Replica Detection scheme (ERDS) based on Sequential Probability Ratio Test (SPRT) is proposed to detect the malicious actions and to have a secure path without claim in an efficient manner. Also, provides strategies to avoid attacker and to provide se

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12

Bokaei, Mohammad Hadi, Minoo Nassajian, Mojgan Farhoodi, and Mona Davoudi Shamsi. "Claim Detection in Persian Twitter Posts." International Journal of Information and Communication Technology Research 16, no. 3 (2024): 25–34. https://doi.org/10.61186/itrc.16.3.25.

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13

Abumansour, Amani S., and Arkaitz Zubiaga. "Check-worthy claim detection across topics for automated fact-checking." PeerJ Computer Science 9 (May 16, 2023): e1365. http://dx.doi.org/10.7717/peerj-cs.1365.

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An important component of an automated fact-checking system is the claim check-worthiness detection system, which ranks sentences by prioritising them based on their need to be checked. Despite a body of research tackling the task, previous research has overlooked the challenging nature of identifying check-worthy claims across different topics. In this article, we assess and quantify the challenge of detecting check-worthy claims for new, unseen topics. After highlighting the problem, we propose the AraCWA model to mitigate the performance deterioration when detecting check-worthy claims acro

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14

Annaboina, Krishna, Samala Prasoona, Chada Ashritha, and Pesara Chakradhar Reddy. "Fraud Detection in Medical Insurance Claim Systems using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40522.

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Fraud detection in medical insurance claim systems is crucial for preserving healthcare service integrity and minimizing financial losses. This study explores the application of Support Vector Machines (SVM) enhanced by GridSearchCV for hyperparameter optimization, aiming to detect fraudulent claims effectively. The research methodology involves preprocessing a comprehensive medical insurance claims dataset, focusing on extensive feature selection and engineering to improve model performance. GridSearchCV is utilized to conduct an exhaustive search over specified parameter ranges, identifying

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15

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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16

Goutham, Bilakanti. "Automated Healthcare Claim Processing with AWS AI." International Journal of Leading Research Publication 5, no. 1 (2024): 1–12. https://doi.org/10.5281/zenodo.15196969.

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The integration of artificial intelligence (AI) and cloud-based solutions is revolutionizing healthcare claims processing by enhancing efficiency, accuracy, and security. This study explores an AWS-powered system that leverages Amazon Textract, AWS Step Functions, and Amazon Recognition to automate document extraction, validation, and fraud detection. By eliminating manual processes, the AI-driven system significantly reduces claim processing time, minimizes errors, and ensures compliance with regulatory standards. Machine learning algorithms improve data classification and enhance decision-ma

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17

Wicaksono, Ridwan Lazuardy Bimo, and Aletta Divna Valensia Rohman. "Analysis Automobile Insurance Fraud Claim Using Decision Tree and Random Forest Method." International Journal of Global Operations Research 5, no. 4 (2024): 231–38. https://doi.org/10.47194/ijgor.v5i4.337.

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Insurance fraud, particularly in the automobile sector, poses significant financial risks to insurance companies. This study aims to analyze fraudulent claims in automobile insurance using Decision Tree and Random Forest methods. A dataset consisting of 10,000 entries was utilized, containing variables such as vehicle type, claim amount, and claim status. The Decision Tree method was employed for its interpretability, while Random Forest was used for its superior accuracy. Results indicated that the Random Forest model outperformed the Decision Tree model, achieving an accuracy of 51.37% compa

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18

Saravanakumar, V. "Detecting and Preventing Fraud in Insurance Claims by using Artificial Intelligence." ComFin Research 13, S1-i1-Mar (2025): 161–65. https://doi.org/10.34293/commerce.v13is1-i1-mar.8673.

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Insurance companies have been suffered financial losses as a result of fraudulent activities. People from Insurance industry believe that fraud exists anywhere and they are being discovered across lines of business. Fraud in insurance claim is a significant issue for insurance companies and it has gotten worse in recent years. Most of the people recognized that traditional fraud detection methods are not enough to detecting and preventing fraud claims in insurance. This paper explores the current trends in insurance sector particularly identifying fraudulent activities in claims and utilize ar

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19

Subbian, Rajkumar Govindaswamy. "Enhancing Operational Efficiency in Claims Processing Through Technology." Asian Journal of Research in Computer Science 18, no. 3 (2025): 456–66. https://doi.org/10.9734/ajrcos/2025/v18i3604.

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AIM: To analyze the impact of Technology-Driven Claims Automation, with a focus on real-time fraud detection and enhancing accuracy in claims intake and validation. This study explores how advanced technologies such as AI, machine learning, and automation streamline claims workflows, reduce processing time, and enhance decision-making accuracy while mitigating fraudulent activities in real-time. Study Design: A quasi-experimental design was employed to assess the effectiveness of Technology-Driven Claims Automation. The study analyzed pre- and post-implementation performance metrics, such as C

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20

Liu, Yuxuan, Hongda Sun, Wenya Guo, et al. "BiDeV: Bilateral Defusing Verification for Complex Claim Fact-Checking." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 541–49. https://doi.org/10.1609/aaai.v39i1.32034.

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Complex claim fact-checking performs a crucial role in disinformation detection. However, existing fact-checking methods struggle with claim vagueness, specifically in effectively handling latent information and complex relations within claims. Moreover, evidence redundancy, where non-essential information complicates the verification process, remains a significant issue. To tackle these limitations, we propose Bilateral Defusing Verification (BiDeV), a novel fact-checking working-flow framework integrating multiple role-played LLMs to mimic the human-expert fact-checking process. BiDeV consis

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21

Shreedharan, Krishna Kumar. "Automated Claims Processing in Guidewire ClaimCenter: Enhancing Efficiency and Accuracy in the Insurance Industry." Asian Journal of Research in Computer Science 18, no. 3 (2025): 232–38. https://doi.org/10.9734/ajrcos/2025/v18i3589.

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Aims: This study explores the benefits, challenges, and future trends in implementation of automated claims processing in Guidewire ClaimCenter, a leading software platform for insurance providers and provides insights to help insurers who intend to implement automated claims processing in Guidewire ClaimCenter. Study Design: Mixed-methods approach, combining both qualitative and quantitative research to provide a comprehensive analysis of automation in Guidewire’s claims processing. Place and Duration of Study: Analysis between February 2024 and September 2024, based on data from North Americ

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22

Lomas, Dennis. "Representation of basic kinds: Not a case of evolutionary internalization of universal regularities." Behavioral and Brain Sciences 24, no. 4 (2001): 686–87. http://dx.doi.org/10.1017/s0140525x01500084.

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Shepard claims that “evolutionary internalization of universal regularities in the world” takes place. His position is interesting and seems plausible with regard to “default” motion detection and aspects of colour constancy which he addresses. However, his claim is not convincing with regard to object recognition. [Shepard]

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23

Rahayu, Tiny, Mia Rahma Tika, and Sapta Lestariyowidodo. "Analysis Of Outside Claim Fragmentation On BPJS Claims In Hospital." KESANS : International Journal of Health and Science 1, no. 1 (2021): 22–27. http://dx.doi.org/10.54543/kesans.v1i1.6.

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The Social Security Organizing Agency (BPJS) has provisions regardingfraud in which one form of fraud is the breakdown of service episodes that are not in accordance with medical indications (serviceunbundling or fragmentation)it is done by health care providers in Health Facilities Referral Follow-Up (FKRTL) the action is done intentionally, to get financial benefits from public relations. Health Insurance program in the National Social Security System through fraudulent acts that are not in accordance with the provisions of the laws and regulations. The purpose of this study is to analyze th

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24

Ncube, Hopewell Bongani, Belinda Mutunhu, Sibusisiwe Dube, and Kudakwashe Maguraushe. "Blockchain-Based Fraud Detection System for Healthcare Insurance Claims." European Conference on Innovation and Entrepreneurship 19, no. 1 (2024): 540–47. http://dx.doi.org/10.34190/ecie.19.1.2558.

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Healthcare fraud is a huge concern that affects not only the financial viability of insurance companies but also the well-being of patients who may receive compromised care due to fraudulent acts. Addressing this issue demands novel solutions that can detect and prevent fraudulent conduct in healthcare insurance claims. The project intends to establish an automated fraud detection system using blockchain technology, which has advantages such as security, transparency, and data immutability. By leveraging blockchain's decentralized ledger, the system creates a tamper-proof platform for processi

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Ricchetti-Masterson, Kristen, Molly Aldridge, John Logie, Nittaya Suppapanya, and Suzanne F. Cook. "Exploring Methods to Measure the Prevalence of Ménière's Disease in the US Clinformatics™ Database, 2010-2012." Audiology and Neurotology 21, no. 3 (2016): 172–77. http://dx.doi.org/10.1159/000441963.

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Recent studies on the epidemiology of the inner-ear disorder Ménière's disease (MD) use disparate methods for sample selection, case identification and length of observation. Prevalence estimates vary geographically from 17 to 513 cases per 100,000 people. We explored the impact of case detection strategies and observation periods in estimating the prevalence of MD in the USA, using data from a large insurance claims database. Using case detection strategies of ≥1, ≥2 and ≥3 ICD-9 claim codes for MD within a 1-year period, the 2012 prevalence estimates were 66, 27 and 14 cases per 100,000 peop

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26

Machireddy, Jeshwanth Reddy. "Machine Learning and Automation in Healthcare Claims Processing." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 6, no. 1 (2024): 686–701. https://doi.org/10.60087/jaigs.v6i1.335.

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Healthcare systems have evolved rapidly; driven by the need for efficient and accurate claims processing in order to reduce fraud, errors, and increase operational efficiency. “The existing traditional manual methods are error-prone, time-consuming and costlier in terms of administration. This chapter provides insight into how Machine Learning (ML) and Automation are fundamentally changing the way that healthcare claims are being managed in a revolutionary manner. We explore essential ML techniques in the form of predictive analytics, anomaly detection, natural language processing (NLP), and r

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27

Chethan, Chinthaprthi. "Automated Vehicle Damage Detection and Cost Estimator for Insurance Companies." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 2963–68. https://doi.org/10.22214/ijraset.2025.67989.

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Using state-of-the-art deep learning algorithms, the "Automated Vehicle Damage Detection & Cost Estimator for Insurance Companies" project seeks to transform the vehicle insurance industry by automating the damage assessment process. This AI-powered system uses YOLOv5, one of the most sophisticated object identification models available, to effectively analyse and evaluate vehicle damage, including that of two-wheelers and four-wheelers. The model can precisely identify, categorize, and assess various forms of damage, such as scratches, dents, cracks, and shattered pieces, because it has b

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28

Aditya, Kurniawan, Widodo Sri, and Maulindar Joni. "Fraud Detection in Health Insurance Claims Based on Artificial Intelligence (AI)." Engineering and Technology Journal 10, no. 02 (2025): 3735–39. https://doi.org/10.5281/zenodo.14788653.

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Fraud in health insurance claims has become a significant problem affecting the provision of healthcare globally. In addition to the financial losses incurred, patients who actually need medical treatment also suffer. This is because healthcare providers are not paid on time, as a result of delays in the manual scrutiny of their claims. Health insurance claim fraud is perpetrated through service providers, insurance customers, and insurance companies. The need for the development of a decision support system (DSS) for accurate claims processing that can automatically detect fraud is urgently n

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Subodh Nath Pushpak. "Quantum Machine Learning Technique for Insurance Claim Fraud Detection with Quantum Feature Selection." Journal of Information Systems Engineering and Management 10, no. 8s (2025): 750–56. https://doi.org/10.52783/jisem.v10i8s.1193.

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This paper demonstrates a novel use of quantum machine learning (QML) algorithms for detecting fraudulent activities in the home insurance sector. Utilizing actual data and IBM Quantum processors through the Qiskit software stack, the study introduces an innovative method for selecting quantum features that are specifically designed to accommodate the limitations of Near Intermediate Scale Quantum (NISQ) technology by using the Quantum Support Vector Machine (QSVM) in conjunction with traditional machine learning techniques. A comprehensive comparison was conducted to evaluate their effectiven

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Nelloru, Nageswara Rao. "AI-Orchestrated Claims Routing in Modernized Insurance Core Systems." European Journal of Computer Science and Information Technology 13, no. 47 (2025): 95–102. https://doi.org/10.37745/ejcsit.2013/vol13n4795102.

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This article explores the transformative impact of AI-orchestrated claims routing in modernized insurance core systems, focusing on the integration of machine learning models and automated decision engines. The article examines how AI-driven systems have revolutionized traditional claims processing through enhanced triage mechanisms, sophisticated business rules integration, and predictive analytics. The article demonstrates significant improvements in claims processing efficiency, fraud detection, and resource allocation through cloud-based architectures and API-driven integration. The articl

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31

Hapsari, Luthfia Nurma, and Nur Rokhman. "Anomaly Detection of Hospital Claim Using Support Vector Regression." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 18, no. 1 (2024): 1. http://dx.doi.org/10.22146/ijccs.91857.

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BPJS Kesehatan plays a crucial role in providing affordable access to healthcare services and reducing individual financial burdens. However, deficit issues can disrupt the sustainability of the program, making anomaly detection highly important to conduct. Previous research on unsupervised anomaly detection in BPJS Kesehatan revealed a limitation with Simple Linear Regression (SLR), which only accommodates linear relationships among independent variables and the target variable of BPJS Kesehatan claim values. Minister of Health Regulation No. 52 of 2016 identified eight influential non-linear

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Sufi, Fahim, and Musleh Alsulami. "Quantifying Truthfulness: A Probabilistic Framework for Atomic Claim-Based Misinformation Detection." Mathematics 13, no. 11 (2025): 1778. https://doi.org/10.3390/math13111778.

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The increasing sophistication and volume of misinformation on digital platforms necessitate scalable, explainable, and semantically granular fact-checking systems. Existing approaches typically treat claims as indivisible units, overlooking internal contradictions and partial truths, thereby limiting their interpretability and trustworthiness. This paper addresses this gap by proposing a novel probabilistic framework that decomposes complex assertions into semantically atomic claims and computes their veracity through a structured evaluation of source credibility and evidence frequency. Each a

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Harrag, Fouzi, and Mohamed Khalil Djahli. "Arabic Fake News Detection: A Fact Checking Based Deep Learning Approach." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 4 (2022): 1–34. http://dx.doi.org/10.1145/3501401.

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Fake news stories can polarize society, particularly during political events. They undermine confidence in the media in general. Current NLP systems are still lacking the ability to properly interpret and classify Arabic fake news. Given the high stakes involved, determining truth in social media has recently become an emerging research that is attracting tremendous attention. Our literature review indicates that applying the state-of-the-art approaches on news content address some challenges in detecting fake news’ characteristics, which needs auxiliary information to make a clear determinati

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Sahana, Munavalli, and M. Hatture Sanjeevakumar. "Fraud Detection in Healthcare System using Symbolic Data Analysis." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 9 (2021): 1–7. https://doi.org/10.35940/ijitee.H9269.0710921.

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In the era of digitization the frauds are found in all categories of health insurance. It is finished next to deliberate trickiness or distortion for acquiring some pitiful advantage in the form of health expenditures. Bigdata analysis can be utilized to recognize fraud in large sets of insurance claim data. In light of a couple of cases that are known or suspected to be false, the anomaly detection technique computes the closeness of each record to be fake by investigating the previous insurance claims. The investigators would then be able to have a nearer examination for the cases that have

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35

Glanz, J. "Papers Face Off Over Claim Of Neutrino Mass Detection." Science 269, no. 5231 (1995): 1671–72. http://dx.doi.org/10.1126/science.269.5231.1671.

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VIAENE, S., G. DEDENE, and R. DERRIG. "Auto claim fraud detection using Bayesian learning neural networks." Expert Systems with Applications 29, no. 3 (2005): 653–66. http://dx.doi.org/10.1016/j.eswa.2005.04.030.

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Sari, Panca Oktavia Candra, and Suharjito Suharjito. "Outlier Detection in Inpatient Claims Using DBSCAN and K-Means." JURNAL TEKNIK INFORMATIKA 15, no. 1 (2022): 1–10. http://dx.doi.org/10.15408/jti.v15i1.25682.

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Health insurance helps people to obtain quality and affordable health services. The claim billing process is manually input code to the system, this can lack of errors and be suspected of being fraudulent. Claims suspected of fraud are traced manually to find incorrect inputs. The increasing volume of claims causes a decrease in the accuracy of tracing claims suspected of fraud and consumes time and energy. As an effort to prevent and reduce the occurrence of fraud, this study aims to determine the pattern of data on the occurrence of fraud based on the formation of data groupings. Data was pr

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38

Sowah, Robert A., Marcellinus Kuuboore, Abdul Ofoli, et al. "Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs)." Journal of Engineering 2019 (September 2, 2019): 1–19. http://dx.doi.org/10.1155/2019/1432597.

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Fraud in health insurance claims has become a significant problem whose rampant growth has deeply affected the global delivery of health services. In addition to financial losses incurred, patients who genuinely need medical care suffer because service providers are not paid on time as a result of delays in the manual vetting of their claims and are therefore unwilling to continue offering their services. Health insurance claims fraud is committed through service providers, insurance subscribers, and insurance companies. The need for the development of a decision support system (DSS) for accur

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39

Aragani, Venu Madhav. "Revolutionizing Insurance Through AI and Data Analytics: Innovating Policy Underwriting and Claims Management for the Digital Era." FMDB Transactions on Sustainable Computer Letters 2, no. 3 (2024): 176–85. https://doi.org/10.69888/ftscl.2024.000243.

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This study examines how AI and data analytics can transform insurance. In particular, this study examines how AI may affect underwriting and claims administration. This study uses AI to improve underwriting accuracy, claim processing speed, fraud detection, and operating efficiency. An example dataset of insurance claims, underwriting reports, and customer satisfaction indicators will be used to measure AI’s impact on traditional insurance operations. It includes underwriting accuracy, claims-processing time, fraud detection, client happiness, and efficiency in conventional and AI-supported in

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Jenita, Mary Arockiam, and Claret Seraphim Pushpanathan Angelin. "MapReduce-iterative support vector machine classifier: novel fraud detection systems in healthcare insurance industry." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 756–69. https://doi.org/10.11591/ijece.v13i1.pp756-769.

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Fraud in healthcare insurance claims is one of the significant research challenges that affect the growth of the healthcare services. The healthcare frauds are happening through subscribers, companies and the providers. The development of a decision support is to automate the claim data from service provider and to offset the patient’s challenges. In this paper, a novel hybridized big data and statistical machine learning technique, named MapReduce based iterative support vector machine (MR-ISVM) that provide a set of sophisticated steps for the automatic detection of fraudulent claims i

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41

Ulianov MSc, PhD, Dr Policarpo Yoshin. "The CAT’s race: Who will claim this nobel prize?" Physics & Astronomy International Journal 8, no. 4 (2024): 210–15. http://dx.doi.org/10.15406/paij.2024.08.00350.

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This article explores the competitive scientific endeavor to detect the Cosmic FM Background (CFMB), a predicted radiation whose discovery could substantiate the Small Bang model and potentially challenge the prevailing Big Bang paradigm in cosmology. We delve into the theoretical foundation of CFMB, detailing methodologies for its detection and discussing the profound implications such a discovery would hold for astrophysics. The detection of CFMB would not only shift current cosmological theories but also pave the way for new understanding of the universe’s earliest moments.

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42

Le, Van Nhat Thang, Jae-Gon Kim, Yeon-Mi Yang, and Dae-Woo Lee. "Evaluating the Checklist for Artificial Intelligence in Medical Imaging (CLAIM)-Based Quality of Reports Using Convolutional Neural Network for Odontogenic Cyst and Tumor Detection." Applied Sciences 11, no. 20 (2021): 9688. http://dx.doi.org/10.3390/app11209688.

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This review aimed to explore whether studies employing a convolutional neural network (CNN) for odontogenic cyst and tumor detection follow the methodological reporting recommendations, the checklist for artificial intelligence in medical imaging (CLAIM). We retrieved the CNN studies using panoramic and cone-beam-computed tomographic images from inception to April 2021 in PubMed, EMBASE, Scopus, and Web of Science. The included studies were assessed according to the CLAIM. Among the 55 studies yielded, 6 CNN studies for odontogenic cyst and tumor detection were included. Following the CLAIM it

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Sangishetty, Akanksha. "Revolutionizing Insurance Fraud Detection: A Data-Driven Approach for Enhanced Accuracy and Efficiency." Revolutionizing Insurance Fraud Detection: A Data-Driven Approach for Enhanced Accuracy and Efficiency 8, no. 10 (2023): 9. https://doi.org/10.5281/zenodo.10033870.

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Fraudulent activities are increasingly prevalent across various sectors, imposing significant financial burdens on the insurance industry, estimated to cost billions annually. Insurance fraud, a deliberate and illicit act for financial gain, has emerged as a critical challenge faced by insurance companies worldwide. Often, the root cause of this issue can be traced back to shortcomings in the investigation of fraudulent claims. The repercussions of insurance fraud are extensive, leading to substantial financial losses and billions in avoidable expenses for the industry. This, in turn, necessit

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Atanasious, Mohab Mahdy Helmy, Valentina Becchetti, Alessandro Giuseppi, et al. "An Insurtech Platform to Support Claim Management Through the Automatic Detection and Estimation of Car Damage from Pictures." Electronics 13, no. 22 (2024): 4333. http://dx.doi.org/10.3390/electronics13224333.

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Claims management is a complex process through which an insurance company or responsible entity addresses and handles compensation requests from policyholders who have suffered damage or losses. This process entails several stages, including the notification of the claim, damage assessment, settlement of compensation, and, if necessary, dispute resolution. Fair, transparent and timely claims management is crucial for maintaining policyholders’ trust while also limiting the financial impact on the insurer. Technological innovations, such as the use of artificial intelligence and automation, are

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45

Mary Arockiam, Jenita, and Angelin Claret Seraphim Pushpanathan. "MapReduce-iterative support vector machine classifier: novel fraud detection systems in healthcare insurance industry." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 1 (2023): 756. http://dx.doi.org/10.11591/ijece.v13i1.pp756-769.

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<span>Fraud in healthcare insurance claims is one of the significant research challenges that affect the growth of the healthcare services. The healthcare frauds are happening through subscribers, companies and the providers. The development of a decision support is to automate the claim data from service provider and to offset the patient’s challenges. In this paper, a novel hybridized big data and statistical machine learning technique, named MapReduce based iterative support vector machine (MR-ISVM) that provide a set of sophisticated steps for the automatic detection of fraudulent cl

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46

Khadse, Prof D. B. "Health Care Provider Fraudulent Detection Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 09 (2024): 1–16. http://dx.doi.org/10.55041/ijsrem37654.

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Healthcare fraud is a serious problem that affects the financial health and trust in healthcare systems around the world. This research paper focuses on using machine learning to detect fraudulent activities by healthcare providers. We analyze large amounts of data from Medicare claims to find unusual patterns that may indicate fraud. By using different machine learning methods, such as decision trees and random forests, we create a model that can accurately separate legitimate claims from fraudulent ones. To tackle the challenge of imbalanced data, we apply techniques like oversampling, which

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S. Kaddi, Shweta, and Malini M. Patil. "Ensemble learning based health care claim fraud detection in an imbalance data environment." Indonesian Journal of Electrical Engineering and Computer Science 32, no. 3 (2023): 1686. http://dx.doi.org/10.11591/ijeecs.v32.i3.pp1686-1694.

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<p><span>Healthcare fraud has become a common encounter in the healthcare finance industry. The financial security of healthcare payers and providers is seriously impacted by healthcare fraud. When incorrect or exaggerated medical services are invoiced for reimbursement, fraudulent healthcare claims result. The effective operation of the healthcare system depends on the detection of such fraudulent actions. This paper develops a healthcare claim fraud detection method based on ensemble learning. Stack ensemble learning algorithm performance is compared to that of methods such as mu

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Berendt, Bettina, Peter Burger, Rafael Hautekiet, Jan Jagers, Alexander Pleijter, and Peter Van Aelst. "FactRank: Developing automated claim detection for Dutch-language fact-checkers." Online Social Networks and Media 22 (March 2021): 100113. http://dx.doi.org/10.1016/j.osnem.2020.100113.

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Patil, Vaishnavi. "Fraud Detection and Analysis for Insurance Claim Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 5559–65. http://dx.doi.org/10.22214/ijraset.2023.52875.

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Abstract: Insurance fraud is an illegal conduct that is done on purpose in order to profit financially. This iscurrently the most serious issue that numerous insurance companies throughout the world are facing. The majority of the time, one or more gaps in the investigation of false claims has been identified as the primary factor. As a result, the requirement to use computer tools to stop fraud activitiesincreased. Providing customers with a dependable and stable environment while significantly lowering fraud claims. We demonstrated the results of our research by automating the evaluation of

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Bach, Mirjana Pejić, Ksenija Dumičić, Berislav Žmuk, Tamara Ćurlin, and Jovana Zoroja. "Data mining approach to internal fraud in a project-based organization." International Journal of Information Systems and Project Management 8, no. 2 (2021): 81–101. http://dx.doi.org/10.12821/ijispm080204.

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Data mining is an efficient methodology for uncovering and extracting information from large databases, which is widely used in different areas, e.g., customer relation management, financial fraud detection, healthcare management, and manufacturing. Data mining has been successfully used in various fraud detection and prevention areas, such as credit card fraud, taxation fraud, and fund transfer fraud. However, there are insufficient researches about the usage of data mining for fraud related to internal control. In order to increase awareness of data mining usefulness in internal control, we

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