Measurement of the Severity of Opportunistic Fraud in Personal Injury Insurance:Evidence from China

Zhou, Ji; Wang, Shanshan; Zhou, Jianbo; Xu, Yanli

doi:10.1080/1540496x.2016.1177787

Cited by 4 publications

(9 citation statements)

References 30 publications

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“…Patients file false claims alone or in collusion with friends or healthcare professionals to collect fraudulent medical reimbursement. (p) (Ekin et al, 2018;Yang, 2003) Opportunistic Fraud It is a case of opportunistic or occasional fraud, one in which "the reality of a claim is taken advantage of to introduce pre-existing or (Ribeiro et al, 2020;Zhou et al, 2016) Table 2 Manifestation of health insurance fraud by the provider (Continued)…”

Section: Mesoenvironment Factorsmentioning

confidence: 99%

“…A factor can show both signs simultaneously (+−), which means that its influence is ambivalent. In contrast, a double sign indicates that the study has an applied validation based on a method de experimentation or quasi experimentation (Goel, 2020) Predominant white race (Manocchia et al, 2012) Married, marital status (Zhou et al, 2016) Place of residence: more urbanized states (Musal, 2010;Ribeiro et al, 2020) (Goel, 2020) They have insured (Manocchia et al, 2012) Predominant English language In cities where multiple languages are spoken, the predominant language can influence HIF. (Manocchia et al, 2012)…”

Section: Collaborative Factorsmentioning

confidence: 99%

“…(Manocchia et al, 2012) (Liou et al, 2008) Risk of illness The risk score for illness is considered. (Manocchia et al, 2012) Ethics and morals The health insurance market is not immune to (Tseng, 2016) (Bourgeon & Picard, 2014;(Ribeiro et al, 2020 Dionne et al, 2009;Jou & Hebenton, 2007;Kumar et al, 2011;Tseng & Kang, 2015;Wang, 2014;Zhou et al, 2016) Duszak, 2011 Baker, 2013)…”

Section: Diagnosticsmentioning

confidence: 99%

“…(Ribeiro et al, 2020) (Lesch & Baker, 2013) Information asymmetry Asymmetric information occurs when one of the actors does not have the same information, this is reflected in the behaviours adopted by the different actors and their billing processes. (Kerschbamer & Sutter, 2017;Kumar et al, 2011;Ribeiro et al, 2020;Zhou et al, 2016) The decision of the adjusters The person who can decide when a settlement is approved or disapproved is one of the most decisive factors. (Tseng & Kang, 2015) Strengthening of capacities Training, seminars.…”

Section: Diagnosticsmentioning

confidence: 99%

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Fourteen years of manifestations and factors of health insurance fraud, 2006–2020: a scoping review

2021

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Background Healthcare fraud entails great financial and human losses; however, there is no consensus regarding its definition, nor is there an inventory of its manifestations and factors. The objective is to identify the definition, manifestations and factors that influence health insurance fraud (HIF). Methods A scoping review on health insurance fraud published between 2006 and 2020 was conducted in ACM, EconPapers, PubMed, ScienceDirect, Scopus, Springer and WoS. Results Sixty-seven studies were included, from which we identified 6 definitions, 22 manifestations (13 by the medical provider, 7 by the beneficiary and, 2 by the insurance company) and 47 factors (6 macroenvironmental, 15 mesoenvironmental, 20 microenvironmental, and 6 combined) associated with health insurance fraud. We recognized the elements of fraud and its dependence on the legal framework and health coverage. From this analysis, we propose the following definition: “Health insurance fraud is an act of deception or intentional misrepresentation to obtain illegal benefits concerning the coverage provided by a health insurance company”. Among the most relevant manifestations perpetuated by the provider are phantom billing, falsification of documents, and overutilization of services; the subscribers are identity fraud, misrepresentation of coverage and alteration of documents; and those perpetrated by the insurance company are false declarations of benefits and falsification of reimbursements. Of the 47 factors, 25 showed an experimental influence, including three in the macroenvironment: culture, regulations, and geography; five in the mesoenvironment: characteristics of provider, management policy, reputation, professional role and auditing; 12 in the microenvironment: sex, race, condition of insurance, language, treatments, chronic disease, future risk of disease, medications, morale, inequity, coinsurance, and the decisions of the claims-adjusters; and five combined factors: the relationships between beneficiary-provider, provider-insurance company, beneficiary-insurance company, managers and guānxi. Conclusions The multifactorial nature of HIF and the characteristics of its manifestations depend on its definition; Identifying the influence of the factors will support subsequent attempts to combat HIF.

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Section: Mesoenvironment Factorsmentioning

confidence: 99%

Section: Collaborative Factorsmentioning

confidence: 99%

Section: Diagnosticsmentioning

confidence: 99%

Section: Diagnosticsmentioning

confidence: 99%

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Fourteen years of manifestations and factors of health insurance fraud, 2006–2020: a scoping review

2021

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“…Efektivitas pengendalian internal ada lah proses untuk mencapai keandalan lapor an keuangan, kepatuhan hukum, operasi yang efektif dan efisien (Kummer, Singh, & Best, 2015;Lokanan, 2014). Pengukuran menggunakan 5 indikator yang diadopsi dari penelitian Zhou, Wang, Zhou, & Xu (2017) meliputi penerapan tanggung jawab dan wewenang, ipencatatan transaksi, kenda li sistem akuntansi dan fisik, pemantauan, serta evaluasi. Efektivitas pengendalian in ternal diukur dengan skala likert skor 15, data yang digunakan berupa data numerik yang diperoleh dari penjumlah total skor tiap indikator.…”

Section: Metodeunclassified

Pencegahan Fraud Di Pemerintah Desa

Wijayanti

Hanafi

2018

jamal

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Pencegahan Fraud pada Pemerintahan Desa. Tujuan pene litian ini adalah untuk menelaah pengaruh sejumlah variabel terkait individu dan budaya terhadap kecenderungan fraud. Metode yang di gunakan adalah analisis regresi dummy berganda dengan 40 perangkat desa di Kecamatan Sayung, Demak sebagai sampel. Temuan utama dari penelitian ini adalah pemupukan moralitas individu perangkat pemerin tah desa yang baik dapat mencegah kecenderungan fraud. Upaya pemu pukan moralitas individu dapat dilakukan melalui pembinaan kepada aparat perangkat desa, seperti peningkatan iman dan takwa, syukur, sabar, dan peningkatan kesalehan diri.

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Fraud Detection in Healthcare Insurance Claims Using Machine Learning

Nabrawi,

Alanazi

2023

Risks

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Healthcare fraud is intentionally submitting false claims or producing misinterpretation of facts to obtain entitlement payments. Thus, it wastes healthcare financial resources and increases healthcare costs. Subsequently, fraud poses a substantial financial challenge. Therefore, supervised machine and deep learning analytics such as random forest, logistic regression, and artificial neural networks are successfully used to detect healthcare insurance fraud. This study aims to develop a health model that automatically detects fraud from health insurance claims in Saudi Arabia. The model indicates the greatest contributing factor to fraud with optimal accuracy. The labeled imbalanced dataset used three supervised deep and machine learning methods. The dataset was obtained from three healthcare providers in Saudi Arabia. The applied models were random forest, logistic regression, and artificial neural networks. The SMOT technique was used to balance the dataset. Boruta object feature selection was applied to exclude insignificant features. Validation metrics were accuracy, precision, recall, specificity, F1 score, and area under the curve (AUC). Random forest classifiers indicated policy type, education, and age as the most significant features with an accuracy of 98.21%, 98.08% precision, 100% recall, an F1 score of 99.03%, specificity of 80%, and an AUC of 90.00%. Logistic regression resulted in an accuracy of 80.36%, 97.62% precision, 80.39% recall, an F1 score of 88.17%, specificity of 80%, and an AUC of 80.20%. ANN revealed an accuracy of 94.64%, 98.00% precision, 96.08% recall, an F1 score of 97.03%, a specificity of 80%, and an AUC of 88.04%. This predictive analytics study applied three successful models, each of which yielded acceptable accuracy and validation metrics; however, further research on a larger dataset is advised.

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Measurement of the Severity of Opportunistic Fraud in Personal Injury Insurance:Evidence from China

Cited by 4 publications

References 30 publications

Fourteen years of manifestations and factors of health insurance fraud, 2006–2020: a scoping review

Fourteen years of manifestations and factors of health insurance fraud, 2006–2020: a scoping review

Pencegahan Fraud Di Pemerintah Desa

Fraud Detection in Healthcare Insurance Claims Using Machine Learning

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