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2.
PLoS One ; 19(6): e0304153, 2024.
Article En | MEDLINE | ID: mdl-38861514

The study examines the relationship between the corporate social responsibility (CSR) investments of a food firm, an activist's incentive to target the firm to uncover and deter fraudulent behavior, and the firm's incentive to commit food fraud. Specifically, we develop a game theoretic model to analyze the strategic interaction between a food firm that decides whether to provide a credence food attribute and whether to misrepresent the quality of its product, and an activist who decides whether to monitor the firm and launch a campaign to uncover and remove false/misleading quality claims. We further examine the effect of CSR and the activist's presence on the level of quality the firm provides. We derive the conditions under which an activist will find it optimal to monitor the firm to uncover fraudulent quality claims and the firm will find it optimal to misrepresent its product quality. Analytical results show that the greater the firm's CSR investments, the less likely it is that the activist will find it optimal to monitor the firm, and the more likely it is that the firm will find it optimal to misrepresent its product quality. Results also show that the firm is more likely to misrepresent its product quality when its effectiveness in contesting the activist's campaign is relatively high, and more likely to actually provide a high-quality product when the cost of the credence attribute is relatively low.


Fraud , Social Responsibility , Fraud/economics , Fraud/prevention & control , Humans , Food/economics , Food Industry/economics , Game Theory
3.
BMC Public Health ; 24(1): 1564, 2024 Jun 11.
Article En | MEDLINE | ID: mdl-38862992

BACKGROUND: Smuggling health goods given the importance and critical nature of health services should be undeniably addressed and controlled by all countries. This issue is especially more widespread in developing countries with more damaging consequences. This paper therefore aims to identify and analyze the challenges of preventing smuggling of health goods in Iran. METHOD: Within this qualitative study, we conducted face-to-face, semi-structured interviews with 30 purposefully recruited key informants and stakeholders in the detection, prevention, and combating of health goods smuggling. Each interview was analyzed thematically, using an inductive approach to generate codes, then categorized and presented in the form of main themes and sub-themes. Maxqda 11 assisted in coding, analysis, and data management. RESULTS: Three main themes emerged representing the challenges of prevention of smuggling in Iran in the areas of anti-smuggling policy development, including categories of inefficient policy and plan, and failure to reach agenda; policy implementation; categorized into actors, resources and instruments, and implementation guarantee; and finally monitoring and evaluation; including, procedures and practices, and the role of surveyors. CONCLUSION: Prevention of smuggling health goods proves to be a highly complex, challenging, and multi-faceted practice. Therefore, strengthening policy-making, regulatory frameworks, and facilitation functions about smuggling, counterfeiting, and corruption should be promoted in parallel.


Qualitative Research , Iran , Humans , Interviews as Topic , Drug Trafficking/prevention & control , Policy Making , Counterfeit Drugs , Fraud/prevention & control , Health Policy
4.
Glob Public Health ; 19(1): 2350649, 2024 Jan.
Article En | MEDLINE | ID: mdl-38752422

Pharmaceutical sector corruption undermines patient access to medicines by diverting public funds for private gain and exacerbating health inequities. This paper presents an analysis of UN Convention Against Corruption (UNCAC) compliance in seven countries and examines how full UNCAC adoption may reduce corruption risks within four key pharmaceutical decision-making points: product approval, formulary selection, procurement, and dispensing. Countries were selected based on their participation in the Medicines Transparency Alliance and the WHO Good Governance for Medicines Programme. Each country's domestic anti-corruption laws and policies were catalogued and analysed to evaluate their implementation of select UNCAC Articles relevant to the pharmaceutical sector. Countries displayed high compliance with UNCAC provisions on procurement and the recognition of most public sector corruption offences. However, several countries do not penalise private sector bribery or provide statutory protection to whistleblowers or witnesses in corruption proceedings, suggesting that private sector pharmaceutical dispensing may be a decision-making point particularly vulnerable to corruption. Fully implementing the UNCAC is a meaningful first step that countries can take reduce pharmaceutical sector corruption. However, without broader commitment to cultures of transparency and institutional integrity, corruption legislation alone is likely insufficient to ensure long-term, sustainable pharmaceutical sector good governance.


Drug Industry , United Nations , Humans , Drug Industry/legislation & jurisprudence , Private Sector , Fraud/prevention & control , Public Sector
5.
J Elder Abuse Negl ; 36(3): 291-309, 2024 Jun.
Article En | MEDLINE | ID: mdl-38706249

Death anxiety arousal, provoked by anticipating self-nonexistence, may be used as a fraud tactic by scammers on older adults; however, little is known about how it affects older adults' decision making when confronted with a scam and the mechanisms underlying these effects. This study used a questionnaire survey and experimental design to examine them. In Study 1, 307 older adults in China completed questionnaires. The results showed a positive link between death anxiety and vulnerability to fraud, partially mediated by materialism. In Study 2, 82 older adults in China were randomly assigned to the mortality salience group and control group to examine whether death anxiety arousal can increase older adults' vulnerability to fraud and the mediating role of materialism. The results indicated that death anxiety and materialism increase the risk of consumer products and services fraud; therefore, targeting these risk factors might protect older adults from fraud.


Anxiety , Attitude to Death , Fraud , Humans , Aged , Male , Female , China/epidemiology , Aged, 80 and over , Surveys and Questionnaires , Middle Aged
6.
Sci Rep ; 14(1): 11884, 2024 05 24.
Article En | MEDLINE | ID: mdl-38789503

Healthcare fraud, waste and abuse are costly problems that have huge impact on society. Traditional approaches to identify non-compliant claims rely on auditing strategies requiring trained professionals, or on machine learning methods requiring labelled data and possibly lacking interpretability. We present Clais, a collaborative artificial intelligence system for claims analysis. Clais automatically extracts human-interpretable rules from healthcare policy documents (0.72 F1-score), and it enables professionals to edit and validate the extracted rules through an intuitive user interface. Clais executes the rules on claim records to identify non-compliance: on this task Clais significantly outperforms two baseline machine learning models, and its median F1-score is 1.0 (IQR = 0.83 to 1.0) when executing the extracted rules, and 1.0 (IQR = 1.0 to 1.0) when executing the same rules after human curation. Professionals confirm through a user study the usefulness of Clais in making their workflow simpler and more effective.


Artificial Intelligence , Humans , Fraud , Machine Learning , Delivery of Health Care , Insurance Claim Review
7.
JAMA ; 331(19): 1612-1613, 2024 05 21.
Article En | MEDLINE | ID: mdl-38669040

This Medical News article discusses a KFF poll about the public's exposure to and beliefs about inaccurate health information, as well as media use and trust in sources.


Health Communication , Physicians , Public Health Practice , Humans , Deception , Fraud/legislation & jurisprudence , United States , Trust , Health Communication/standards , Communication , Politics
8.
BMC Med Inform Decis Mak ; 24(1): 112, 2024 Apr 26.
Article En | MEDLINE | ID: mdl-38671513

BACKGROUND: Healthcare programs and insurance initiatives play a crucial role in ensuring that people have access to medical care. There are many benefits of healthcare insurance programs but fraud in healthcare continues to be a significant challenge in the insurance industry. Healthcare insurance fraud detection faces challenges from evolving and sophisticated fraud schemes that adapt to detection methods. Analyzing extensive healthcare data is hindered by complexity, data quality issues, and the need for real-time detection, while privacy concerns and false positives pose additional hurdles. The lack of standardization in coding and limited resources further complicate efforts to address fraudulent activities effectively. METHODOLGY: In this study, a fraud detection methodology is presented that utilizes association rule mining augmented with unsupervised learning techniques to detect healthcare insurance fraud. Dataset from the Centres for Medicare and Medicaid Services (CMS) 2008-2010 DE-SynPUF is used for analysis. The proposed methodology works in two stages. First, association rule mining is used to extract frequent rules from the transactions based on patient, service and service provider features. Second, the extracted rules are passed to unsupervised classifiers, such as IF, CBLOF, ECOD, and OCSVM, to identify fraudulent activity. RESULTS: Descriptive analysis shows patterns and trends in the data revealing interesting relationship among diagnosis codes, procedure codes and the physicians. The baseline anomaly detection algorithms generated results in 902.24 seconds. Another experiment retrieved frequent rules using association rule mining with apriori algorithm combined with unsupervised techniques in 868.18 seconds. The silhouette scoring method calculated the efficacy of four different anomaly detection techniques showing CBLOF with highest score of 0.114 followed by isolation forest with the score of 0.103. The ECOD and OCSVM techniques have lower scores of 0.063 and 0.060, respectively. CONCLUSION: The proposed methodology enhances healthcare insurance fraud detection by using association rule mining for pattern discovery and unsupervised classifiers for effective anomaly detection.


Data Mining , Fraud , Insurance, Health , Humans , United States
9.
Nat Food ; 5(4): 293-300, 2024 Apr.
Article En | MEDLINE | ID: mdl-38575840

Sustainability, humidity sensing and product origin are important features of food packaging. While waste generated from labelling and packaging causes environmental destruction, humidity can result in food spoilage during delivery and counterfeit-prone labelling undermines consumer trust. Here we introduce a food label based on a water-soluble nanocomposite ink with a high refractive index that addresses these issues. By patterning the nanocomposite ink using nanoimprint lithography, the resultant metasurface shows bright and vivid structural colours. This method makes it possible to quickly and inexpensively create patterns on large surfaces. A QR code is also developed that can provide up-to-date information on food products. Microprinting hidden in the QR code protects against counterfeiting, cannot be physically detached or replicated and may be used as a humidity indicator. Our proposed food label can reduce waste while ensuring customers receive accurate product information.


Food Labeling , Food Packaging , Water , Food Packaging/standards , Food Labeling/legislation & jurisprudence , Water/chemistry , Nanocomposites/chemistry , Ink , Solubility , Humidity , Fraud/prevention & control
11.
Subst Use Misuse ; 59(8): 1261-1270, 2024.
Article En | MEDLINE | ID: mdl-38503716

Background: The COVID-19 pandemic has accelerated and amplified the use of virtual research methods. While online research has several advantages, it also provides greater opportunity for individuals to misrepresent their identities to fraudulently participate in research for financial gain. Participant deception and fraud have become a growing concern for virtual research. Reports of deception and preventative strategies have been discussed within online quantitative research, particularly survey studies. Though, there is a dearth of literature surrounding these issues pertaining to qualitative studies, particularly within substance use research. Results: In this commentary, we detail an unforeseen case study of several individuals who appeared to deliberately misrepresent their identities and information during participation in a virtual synchronous qualitative substance use study. Through our experiences, we offer strategies to detect and prevent participant deception and fraud, as well as challenges to consider when implementing these approaches. Conclusions: Without general awareness and protective measures, the integrity of virtual research methods remains vulnerable to inaccuracy. As online research continues to expand, it is essential to proactively design innovative solutions to safeguard future studies against increasingly sophisticated deception and fraud.


COVID-19 , Deception , Fraud , Qualitative Research , Substance-Related Disorders , Humans , Fraud/prevention & control , COVID-19/prevention & control , Substance-Related Disorders/prevention & control
13.
Food Chem ; 446: 138893, 2024 Jul 15.
Article En | MEDLINE | ID: mdl-38432137

Modern food chain supply management necessitates the dire need for mitigating food fraud and adulterations. This holistic review addresses different advanced detection technologies coupled with chemometrics to identify various types of adulterated foods. The data on research, patent and systematic review analyses (2018-2023) revealed both destructive and non-destructive methods to demarcate a rational approach for food fraud detection in various countries. These intricate hygiene standards and AI-based technology are also summarized for further prospective research. Chemometrics or AI-based techniques for extensive food fraud detection are demanded. A systematic assessment reveals that various methods to detect food fraud involving multiple substances need to be simple, expeditious, precise, cost-effective, eco-friendly and non-intrusive. The scrutiny resulted in 39 relevant experimental data sets answering key questions. However, additional research is necessitated for an affirmative conclusion in food fraud detection system with modern AI and machine learning approaches.


Food , Fraud , Food Safety , Food Contamination/analysis
14.
PLoS One ; 19(3): e0294537, 2024.
Article En | MEDLINE | ID: mdl-38446831

Credit card fraud is a significant problem that costs billions of dollars annually. Detecting fraudulent transactions is challenging due to the imbalance in class distribution, where the majority of transactions are legitimate. While pre-processing techniques such as oversampling of minority classes are commonly used to address this issue, they often generate unrealistic or overgeneralized samples. This paper proposes a method called autoencoder with probabilistic xgboost based on SMOTE and CGAN(AE-XGB-SMOTE-CGAN) for detecting credit card frauds.AE-XGB-SMOTE-CGAN is a novel method proposed for credit card fraud detection problems. The credit card fraud dataset comes from a real dataset anonymized by a bank and is highly imbalanced, with normal data far greater than fraud data. Autoencoder (AE) is used to extract relevant features from the dataset, enhancing the ability of feature representation learning, and are then fed into xgboost for classification according to the threshold. Additionally, in this study, we propose a novel approach that hybridizes Generative Adversarial Network (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE) to tackle class imbalance problems. Our two-phase oversampling approach involves knowledge transfer and leverages the synergies of SMOTE and GAN. Specifically, GAN transforms the unrealistic or overgeneralized samples generated by SMOTE into realistic data distributions where there is not enough minority class data available for GAN to process effectively on its own. SMOTE is used to address class imbalance issues and CGAN is used to generate new, realistic data to supplement the original dataset. The AE-XGB-SMOTE-CGAN algorithm is also compared to other commonly used machine learning algorithms, such as KNN and Light GBM, and shows an overall improvement of 2% in terms of the ACC index compared to these algorithms. The AE-XGB-SMOTE-CGAN algorithm also outperforms KNN in terms of the MCC index by 30% when the threshold is set to 0.35. This indicates that the AE-XGB-SMOTE-CGAN algorithm has higher accuracy, true positive rate, true negative rate, and Matthew's correlation coefficient, making it a promising method for detecting credit card fraud.


Algorithms , Dietary Supplements , Fraud/prevention & control , Knowledge , Machine Learning
15.
J Elder Abuse Negl ; 36(3): 227-250, 2024 Jun.
Article En | MEDLINE | ID: mdl-38389208

Older adults are thought to be more susceptible to scams, yet understanding the relationship between chronological age and victimization is limited by underreporting. This study avoids underreporting bias by merging four longitudinal databases of Americans (N = 1.33 million) who paid money in response to mail scams over 20 years. We investigate the risk of repeat victimization and victimization by multiple scam types over the life course. Victims in their 70s and 80s are 9% more likely to experience another victimization incident than those in their 50s. Those age 18 to 29 are 24% less likely to experience another victimization incident. Relative to adults in their 50s, the odds of victimization by multiple scams are greater for those in their 60s and 70s, but lower for those 80 + . This study demonstrates the research potential in using scammers' data to understand patterns of victimization. Fraud prevention efforts should target older individuals who are at higher risk of repeat victimization and suffer greater losses as a result.


Crime Victims , Humans , Crime Victims/statistics & numerical data , Aged , Adult , Middle Aged , Female , Male , Adolescent , Aged, 80 and over , United States , Young Adult , Elder Abuse/statistics & numerical data , Fraud/statistics & numerical data , Longitudinal Studies , Age Factors
16.
Diagn Pathol ; 19(1): 31, 2024 Feb 12.
Article En | MEDLINE | ID: mdl-38347621

This letter concerns retracted papers published in the Journal of Diagnostic Pathology, where my name was misused as the author or corresponding author without my permission or knowledge. Considering that all misconducts were directed by an author during initial manuscripts' submissions, I opened a case in Iran's Cyber Police (FATA) to unravel the true identity of the submitting author. After Cyber Police's report revealed the true identity of the submitting author, the court started a thorough investigation and finally convicted the submitting author for identity fraud and data forgery through creating and using fake email addresses.


Scientific Misconduct , Humans , Iran , Judgment , Fraud
18.
Front Public Health ; 12: 1339177, 2024.
Article En | MEDLINE | ID: mdl-38410668

Background: The fundamental medical insurance fund, often referred to as the public's "life-saving fund," plays a crucial role in both individual well-being and the pursuit of social justice. Medicare fraudulent claims reduce "life-saving money" to "Tang's monk meat", undermining social justice and affecting social stability. Methods: We utilized crawler technology to gather textual data from 215 cases involving fraudulent health insurance claims. Simultaneously, statistical data spanning 2018 to 2021 was collected from the official websites of the China Medical Insurance Bureau and Anhui Medical Insurance Bureau. The collected data underwent comprehensive analysis through Excel, SPSS 26.0 and R4.2.1. Differential Auto-Regressive Moving Average Model (ARIMA (p, d, q)) was used to fit the fund safety forecast model, and test the predictive validity of the forecast model on the fund security data from July 2021 to October 2023 (the fund security data of Anhui Province from September 2021 to October 2023). Results: The outcomes revealed that fraudulent claims by health insurance stakeholders adversely impact the equity of health insurance funds. Furthermore, the risk management practices of Medicare fund administrators influence the publication of fraudulent claims cases. Notably, differences among Medicare stakeholders were observed in the prevalence of fraudulent claims. Additionally, effective governance of fraudulent claims risks was found to have a positive impact on the overall health of healthcare funds. Moreover, the predictive validity of the forecast model on the national and Anhui province's fund security data was 92.86% and 100% respectively. Conclusion: We propose four recommendations for the governance of health insurance fraudulent claims risk behaviors. These recommendations include strategies such as "combatting health insurance fraudulent claims to preserve the fairness of health insurance funds", "introducing initiatives for fraud risk governance and strengthening awareness of the rule of law", "focusing on designated medical institutions and establishing a robust long-term regulatory system", and "adapting to contemporary needs while maintaining a focus on long-term regulation".


Insurance, Health , Medicare , Aged , Humans , United States , Fraud , Referral and Consultation , China
20.
J Food Prot ; 87(4): 100251, 2024 Apr.
Article En | MEDLINE | ID: mdl-38403269

Globalization and the increasing complexity of supply chains have allowed food fraud to expand to a great extent. Some of the most serious effects of these deceitful activities are damage to a brand's reputation and trust, economic losses, and public health risks. The usual victims of food fraud are dairy, meat, fish, and seafood products, as well as fats/oils and alcoholic drinks. The purpose of this review paper is to present an updated analysis of the currently available anticounterfeit technologies and their application to the four most fraud-affected food supply chains. An assessment that was conducted to determine when the adoption of a combination of technologies could enhance food safety and brand protection is also provided. The obtained results indicate that electronic and data-driven technologies (RFID devices and digital traceability systems) are still in their infancy in the food sectors that are subjected the most to fraudulent activities. Research is necessary to develop innovative digital and physical technologies to "outsmart" such fraudsters and to prevent their illicit actions in the food sector.


Alcoholic Beverages , Food Safety , Animals , Food Supply , Meat/analysis , Fraud/prevention & control
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