Fraudulent Claims Research Articles

Green Disposable Packaging and Communication: The Implications of Bring-Your-Own-Container

Problem definition: A growing number of firms are encouraging consumers to participate in “bring-your-own-container” (BYOC) behavior in which consumers bring their own reusable packaging to purchase and consume products, thus reducing single-use packaging waste. In this paper, we study the environmental implications of a firm’s BYOC implementation when considering its disposable packaging choice and communication strategy. Methodology/results: We build a stylized model to study a firm’s joint decisions on BYOC, disposable packaging choice, and communication and their implications on the environment. Our main results follow. First, allowing BYOC reduces the firm’s incentive to make fraudulent green claims about its disposable product packaging; however, BYOC implementation may harm the overall environment while improving the firm’s profit, thereby creating a new form of greenwashing. Second, the adoption of third-party certification for green disposable packaging is an effective remedy to mitigate the negative environmental impact of BYOC. In addition, the environmental implications of adopting third-party certification (either voluntarily or because of government mandates) depend on the relationship between the environmental qualities of green disposable packaging and reusable packaging. Whereas it always benefits the environment when the firm’s green disposable packaging has better environmental performance, adopting certification may negatively impact the environment if consumers’ reusable packaging is greener. Furthermore, we find numerically that offering a price discount for BYOC may encourage the firm to adopt certification because of increased profitability, thereby leading to the aforementioned environmental implications. Managerial implications: We offer operational insights on how firms should make joint decisions on BYOC, disposable packaging choice, and communication. We also generate insights on how governments should regulate firms’ green claims when firms start to allow BYOC. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2021.0605 .

How to fund unemployment insurance with informality and false claims: Evidence from Senegal

This paper studies the welfare effects from the provision of unemployment insurance (UI) benefits in a context where formal workers represent only a small proportion of the labor market and informal workers can submit fraudulent claims for UI benefits. We model these features and allow for varying degrees of enforcement and different funding sources. We then estimate the model’s key parameters by conducting a custom labor force survey in Senegal. Our findings show that the liquidity gains are large and the moral hazard response to the UI benefits among workers is relatively small: an extra dollar of UI benefits yields a consumption-equivalent gain of 60–90 cents, which exceeds comparable estimates from U.S. calibrations by a factor of three to sixteen. We then show that the welfare gains depend on the program design: UI funded through payroll taxes delivers the greatest welfare gains but becomes infeasible when there are few formal workers and high rates of fraudulent claims. On the other hand, UI funded through consumption taxes delivers lower welfare gains but remains feasible with high informality and false claims.

Blockchain Applications in Cyber Liability Insurance

Blockchain technology is revolutionizing cyber liability insurance (CLI) by addressing key challenges in underwriting, risk assessment, and claims management. As cyber-attacks become more frequent and complex, the demand for effective CLI solutions has surged. Traditional insurance practices often fall short in this rapidly evolving landscape. Blockchain offers a decentralized, secure, and transparent approach, enhancing the accuracy of risk assessments and preventing fraudulent claims. By maintaining an immutable ledger of historical claims, blockchain allows for better comparison of new claims against past data. Additionally, smart contracts within blockchain frameworks can automate claims processing, reducing administrative tasks and speeding up resolutions. Blockchain also enables decentralized, peer-to-peer insurance platforms, allowing multiple insurers to pool resources and share risks in a transparent, efficient manner. This study explores how blockchain can transform CLI, improving efficiency and security across the industry.

Leveraging AI Algorithms to Combat Financial Fraud in the United States Healthcare Sector

Financial fraud is a major problem in the healthcare industry because it causes large financial losses and compromises the integrity and trust of healthcare systems. The intricacy and sophistication of contemporary fraudulent operations make conventional fraud detection techniques which rely on manual audits and rule-based systems increasingly inadequate. AI algorithms have become a viable way to improve financial fraud detection and prevention. Hence, this paper examines how AI algorithms can be used to detect and stop fraud in the healthcare industry, emphasizing how these algorithms could revolutionize fraud control procedures. This study suggests that AI algorithms greatly improve the identification of financial fraud in the healthcare industry by spotting intricate patterns and abnormalities frequently overlooked by already existing techniques. Machine learning models have proven to be highly accurate in predicting fraudulent claims and transactions. However, while AI provides numerous opportunities to improve fraud detection skills, its effective application necessitates resolving important issues, including ethical considerations, data governance, and model interpretability.

Winners always cheat? Regional identity and the myth of a rigged 2022 Philippine presidential election

This article draws its analysis from the results of an assessment conducted in October 2022 of the May 2022 presidential election. The 2022 presidential election in the Philippines is an historical turning point not only for marking the return of the Marcos family to the country's highest office but also due to the initial claim of electoral fraud that raised concerns about the integrity of automated polling in the country. In the context of the May 2022 election, our findings do not identify any clear demographic pattern linked to a lack of confidence or skepticism regarding the credibility of the election results. Instead, compelling evidence indicates that the belief in a rigged election is limited to a specific subset of voters aligned with the geographical support base of a defeated presidential candidate. The findings contribute to nuancing electoral dynamics in the Philippines in a number of ways. First, they dispel the myth of an impaired election floated by media and other international watchdogs immediately after the election. Second, the suggest that as citizens navigate their democratic responsibilities, their regional identity becomes a lens through which the fairness, transparency, and legitimacy of the electoral system are ultimately contested.

Theranos: the Ethics of Innovation and Deception in Healthcare Technology

The case of Theranos, a health technology company founded by Elizabeth Holmes, presents a profound ethical dilemma that exemplifies the consequences of misrepresentation in healthcare innovation. This analysis examines the company's fraudulent claims regarding its blood-testing technology, which promised groundbreaking results using minimal blood samples. Initially lauded for its potential, Theranos attracted significant investment and partnerships before facing growing scrutiny due to internal whistleblower accounts and investigative journalism. The ethical issues at the core of this case involve the deliberate falsification of leadership's technological capabilities, which leads to harm to patients and significant financial losses for investors. The methodology of this study involved a comprehensive review of academic literature identified through Google Scholar, applying inclusion criteria that focused on ethical analysis, corporate leadership, and regulatory oversight. The selected documents were analyzed using Atlas.ti to code critical themes such as corporate misrepresentation, stakeholder rights, and regulatory failures. Utilizing a multi-faceted ethical analysis—incorporating utilitarian, deontological, virtue ethics, and rights-based perspectives—this study explores the failures of corporate leadership in adhering to professional and ethical obligations. It critically evaluates the responses by stakeholders, including actions taken by regulatory bodies and law enforcement, and discusses the lessons learned for the broader health technology sector. The study concludes by recommending more robust internal governance, transparent leadership, and proactive regulatory oversight as essential measures to prevent future ethical lapses in the industry. The analysis contributes to understanding ethics' critical role in maintaining trust and accountability within healthcare innovation.The case of Theranos, a health technology company founded by Elizabeth Holmes, presents a profound ethical dilemma that exemplifies the consequences of misrepresentation in healthcare innovation. This analysis examines the company's fraudulent claims regarding its blood-testing technology, which promised groundbreaking results using minimal blood samples. Initially lauded for its potential, Theranos attracted significant investment and partnerships before facing growing scrutiny due to internal whistleblower accounts and investigative journalism. The ethical issues at the core of this case involve the deliberate falsification of leadership's technological capabilities, which leads to harm to patients and significant financial losses for investors. The methodology of this study involved a comprehensive review of academic literature identified through Google Scholar, applying inclusion criteria that focused on ethical analysis, corporate leadership, and regulatory oversight. The selected documents were analyzed using Atlas.ti to code critical themes such as corporate misrepresentation, stakeholder rights, and regulatory failures. Utilizing a multi-faceted ethical analysis—incorporating utilitarian, deontological, virtue ethics, and rights-based perspectives—this study explores the failures of corporate leadership in adhering to professional and ethical obligations. It critically evaluates the responses by stakeholders, including actions taken by regulatory bodies and law enforcement, and discusses the lessons learned for the broader health technology sector. The study concludes by recommending more robust internal governance, transparent leadership, and proactive regulatory oversight as essential measures to prevent future ethical lapses in the industry. The analysis contributes to understanding ethics' critical role in maintaining trust and accountability within healthcare innovation.

Fraud in genetic testing: Swindling the system

PurposeHealth care fraud comprises a sizable portion of US health care expenditure and inflicts undue burden on payers, patients, and the health care system overall. The genetic testing industry is rapidly growing, which propagates opportunities for health care fraud. Although federal organizations have highlighted it as an issue, there is limited research exploring genetic testing fraud. MethodsA retrospective review of federal websites, news articles, and a legal database resulted in 42 cases of fraud involving outpatient genetic testing published between February 2019 and December 2023. These cases were analyzed for themes via inductive conventional content analysis. ResultsThemes of fraudulent activity included submission of fraudulent claims, kickback or bribe payments, minimal or no contact with patients for whom testing was ordered for, inappropriate billing and documentation practices, and further actions to conceal fraud. Repercussions imposed on defendants included monetary penalty, imprisonment, business restrictions, and seizure of property. ConclusionHigh rates of medically inappropriate testing in fraud cases highlight the value of genetics experts in ordering or reviewing claims for genetic testing. Examining fraudulent activity in genetic testing can help providers identify and report fraud and provide awareness of optimal health care allocation in the genetic testing industry.

공공재정 부정이익환수제도에 관한 고찰

The Public Finance Recovery Act was enacted in April 2019 in addition to the disciplinary surcharge system for public officials under the National Public Officials Act and the Local Public Officials Act as subsidies, R&D expenses, allowances, etc. of the state, local governments, and public organizations continue to make false claims and excessive claims. In order to prohibit public financial fraudulent claims and to recover the full amount of fraudulent income, which is unearned income, a sanction surcharge of up to five times is imposed and recovered in consideration of the intention and habitability of fraudulent claims. In this paper, after examining the concept, scope, and legal nature of public finance fraud recovery, we examine the Improper Payment lnfonnation Act of 2002, the False Claims Act, the Federal Improper Payments Coordination Act of 2015, and the Payment Integrity Information Act of 2019 in the United States, and the Subsidy Appropriation Act of Japan to seek implications for Korea In addition, we examine legal issues related to the systematization of pre- and post-systems to reduce fraudulent profits in public finance payments, securing the effectiveness of the whistleblowing system for public finance subsidy recovery, and activating the anonymous proxy reporting system, and suggest improvement measures to enhance the effectiveness of the Public Finance Recovery Act. Before recovering the illicit profits of public finance payments, relevant administrative agencies and public institutions should actively operate programs to strengthen the risk assessment and eligibility review of illicit payments, and after disbursing public finance support funds, they should strictly conduct an audit by an external auditor independent of the administrative agency and public institution to prevent illicit receipt of public finance support funds, and the results of the audit should be posted on the website of the relevant administrative agency to enhance transparency. In addition, in order to eradicate the negative organizational culture toward whistleblowers and the secret receipt of public finance support funds in Korea, whistleblowers should be allowed to file a U.S.-style qui tarn lawsuit only in cases where they make false claims to administrative agencies or public institutions. However, if the relevant administrative agency or public institution allows intervention in the lawsuit and recovers the illicit profits of public finance funds, they should be compensated 35% of the recovered amount to the whistleblower. Given the nature of fraudulent receipt of public financial support funds, the regional National Human Rights Commission's anonymous proxy reporting advisory group should be expanded in a balanced manner to ensure that the anonymous proxy reporting system is activated, and the anonymity of internal reporters should be guaranteed.

Comparative Study of Machine Learning Techniques for Insurance Fraud Detection

Insurance fraud has been a constant presence in the realm of insurance. However, as strategies and methods for committing insurance fraud have evolved, the frequency and volume of such fraudulent activities have also increased. An example of this is vehicle insurance fraud, which involves collaborating to fabricate false or exaggerated claims related to property damage or personal injuries resulting from an accident. Machine learning techniques seems to be more beneficial and great way to address the fraud in the insurance industry. This paper comprehensively examines existing research through a systematic literature review. This review aims to identify previously attempted approaches and evaluate which machine learning algorithm is best suited for this specific problem. This paper proposes a methodology for identifying fraudulent insurance claims. This approach can significantly improve efficiency and cost savings for insurance companies in handling such cases. The most popular traditional machine learning algorithms used to identify insurance fraud in the auto industry were found to be support vector machine, logistic regression, and random forest.

AUTOMOBILE INSURANCE FRAUD DETECTION USING ENSEMBLE LEARNING MODELS

Automobile insurance fraud is a universal problem that has negative effects on both insurance companies and policyholders. This research proposes a novel ensemble learning model to accurately detect potential fraudulent vehicle insurance claims. By leveraging advanced machine learning techniques and addressing the challenge of imbalanced data, our model aims to enhance fraud detection efficiency and reduce financial losses. Our approach combines a stacking ensemble learner with carefully selected base classifiers, meta-classifier and data pre-processing techniques. We evaluate the model's performance on a real-world dataset of insurance claims, demonstrating superior results compared to existing methods. Notably, our model achieves high accuracy, recall, precision and area under curves, ensuring comprehensive detection and minimizing false positives.

A spatiotemporal chaos based deep learning model watermarking scheme

With deep learning techniques achieving great results in modern industry, the intellectual property (IP) protection for deep learning models has attracted the attention of academics and engineers. However, training a commercially viable deep learning model usually needs professional resources and time. Once a malicious user clones, illegally distributes and uses the model, it can infringe on the model owner's IP and even steal its market share. Among the existing IP protection methods, scholars prefer the black-box watermarking approaches, of which the content of the trigger set and the label are the key part of the watermarking technique. However, most schemes do not consider the security and invisibility of the trigger set, which allows attackers to easily trigger the model by creating a fake trigger set, thereby committing a fraudulent ownership claim attack and claiming the ownership belongs to themselves. To overcome these drawbacks, we proposed a spatiotemporal chaotic data annotation method. Firstly, the unpredictability and acyclicity of chaos make the model resistant to fraudulent ownership claim attacks, statistical inference and other common machine learning attacks; Secondly, the trigger set and parameters are independent of each other, guaranteeing the security of the key; Thirdly, the spatiotemporal chaotic system provides a large key space, which meets the commercialization needs of deep learning models. Theoretical analysis and experimental results show that our scheme has security, practicality and robustness. To further validate the superiority of the proposed method, we also compare it with the Logistic chaotic annotation watermarking-based method, and the results show that our method performs better in terms of robustness, effectiveness, completeness, fidelity, security and practicality.

From Confidence to Convenience: Changes in Voting Systems, Donald Trump, and Voter Confidence

Abstract A growing body of research indicates that voters’ perceptions of the electoral system change when the means of administering elections change. As some electoral jurisdictions have moved to systems designed to increase voter convenience, little is known about whether convenience is achieved at the expense of confidence. Utah’s rollout of Vote by Mail (VBM) occurred county by county across five federal elections, and the three most populous counties adopted all VBM elections successively in 2014, 2016, and 2018. This meaningful variance in voter experience with VBM allows us to model the relationship between experience with VBM and voter confidence. We find that the switch to a more convenient system of voting came at the expense of diminished confidence in the voting process. However, experience with VBM is positively related to confidence such that the loss in confidence can be recouped over time. To capture this dynamic relative to other factors, we also estimate the effects of partisanship and the messages of political elites. In the aftermath of the 2020 election, the effect of partisanship on voter confidence changed compared to just two years earlier. Elite messages appear to be especially negative for Trump voters after 2020. More experience with VBM acts as a bulwark against those negative messages, almost completely attenuating the negative effects of Trump’s fraud claims among his voters with the highest levels of VBM experience.

Slant, Extremity, and Diversity: How the Shape of News Use Explains Electoral Judgments and Confidence

Abstract The 2020 election and its aftermath present an opportunity to understand how audiences’ changing news consumption patterns within an expanded, digitized, and polarized media environment shape electoral judgments. This paper introduces measures that capture individuals’ partisan slant, partisan extremity, and overall diversity of news media use to understand how people interact with the contemporary news ecology. The measures combine self-reported media use with the partisan slant of news outlets along the ideological spectrum. Using these measures, we analyze a two-wave panel survey conducted before and after the 2020 US election to investigate how slant, diversity, and extremity are related to post-election beliefs, including public confidence in the election and the acceptance of fraud claims. Our findings show that Republicans have more insulated news use behaviors in terms of slant and diversity. The analysis also reveals that the slant of people’s news use is associated with post-election fraud beliefs, with right-wing news consumers more likely to believe such claims. However, a diverse news consumption style can moderate misinformation beliefs. Panel analysis points to the role of extreme-right news use in decreasing confidence in the legitimacy of the election.

Does Exposure to Election Fraud Research Undermine Confidence in Elections?

Abstract Can academic research on election fraud undermine public confidence in elections? As election results become instantaneously accessible all over the world, academic research can increasingly influence perceptions of election integrity. To examine how electoral fraud information from academic research can affect voters’ beliefs about the legitimacy of election results, we conducted a survey experiment following the 2020 general election in South Korea. Using a nationally representative sample of Korean voters and exploiting a unique real-world situation featuring foreign academics considered relatively impartial, we found that exposure to hypothetical election fraud claims undermined confidence in electoral outcomes, measured via both self-reports and behavioral measures. Moreover, the claims’ impact was conditional on voters’ partisan attachments—strongest among supporters of the losing party—which is in line with the logic of motivated reasoning. Our work contributes to the literature on election integrity, using a relatively new yet stable democracy as its test case.

HPR62 Analysis of FDA-Issued Warning Letters for Fraudulent Claims Within the Economic, Clinical, and Humanistic Outcomes (ECHO) Framework

HPR62 Analysis of FDA-Issued Warning Letters for Fraudulent Claims Within the Economic, Clinical, and Humanistic Outcomes (ECHO) Framework

PATIENT CARE AND FINANCIAL INTEGRITY IN HEALTHCARE BILLING THROUGH ADVANCED FRAUD DETECTION SYSTEMS

Healthcare fraud poses a significant challenge, leading to substantial financial losses and compromising the quality of patient care. This study assesses the efficacy of advanced fraud detection systems, including data analytics, machine learning, predictive modeling, and natural language processing (NLP), in enhancing the detection and prevention of fraudulent activities in healthcare. By leveraging these technologies, healthcare organizations can process large volumes of complex data, adapt to evolving fraud patterns, and provide real-time monitoring. The findings indicate that data analytics effectively uncovers hidden patterns and anomalies, while machine learning and AI improve predictive accuracy by continuously learning from historical data. Predictive modeling enables proactive fraud prevention by forecasting potential fraud scenarios, and NLP extends detection capabilities to unstructured data such as clinical notes. The integration of these advanced technologies has resulted in significant financial savings and improved patient care, as demonstrated by case studies highlighting substantial reductions in fraudulent claims. The study concludes that adopting advanced fraud detection systems is essential for maintaining financial integrity and ensuring high-quality patient care in the evolving healthcare landscape.

The economic impacts of social activism and corporate social responsibility on food fraud.

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.

Secure hardware IP of GLRT cascade using color interval graph based embedded fingerprint for ECG detector

This paper presents a security aware design methodology to design secure generalized likelihood ratio test (GLRT) hardware intellectual property (IP) core for electrocardiogram (ECG) detector against IP piracy and fraudulent claim of IP ownership threats. Integrating authentic (secure version) GLRT hardware IP core in the system-on-chip (SoC) of ECG detectors is paramount for reliable operation and estimation of ECG parametric data, such as Q wave, R wave and S wave (QRS) complex detection. A pirated GLRT hardware IP integrated into an ECG detector may result in an unreliable/erratic estimation of ECG parametric data that can be hazardous and fatal for the end patient. The proposed methodology presents an integrated design flow to secure micro GLRT and GLRT cascade hardware IP cores for the ECG detector, using the colored interval graph (CIG) framework based fingerprint biometric, during high level synthesis (HLS). The proposed approach integrates a fingerprint biometric based security constraint generation process for securing the GLRT hardware IP core. This paper also presents a secure register transfer level (RTL) datapath design corresponding to micro GLRT and GLRT cascade hardware IP cores with embedded IP vendor's fingerprint. The proposed secure GLRT hardware IP core embedded with fingerprint biometric achieves superior results in terms of probability of coincidence and tamper tolerance than other security approaches. More explicitly, the proposed approach reports a significantly lower value of probability of coincidence and stronger value for tamper tolerance. Further, the proposed approach incurs zero design cost overhead.

Feasibility and Program Design of Maintenance Insurance for Common Parts in Residential Communities

Residential special maintenance funds for residential common parts, common facilities and equipment warranty expires after the maintenance and renewal, transformation. However, in the process of repairing the common parts of the district, the problems of misrepresentation, fraudulent claim and excessive repair of maintenance funds are prominent. In this paper, based on the maintenance data of Xuanwu District and Gulou District in Nanjing City from 2020 to 2022, we carry out the feasibility and program design of maintenance insurance for the common parts of residential neighborhoods. The results show that the existing maintenance funds can cover the expected premiums, verifying the feasibility of common parts maintenance insurance and designing an insurance program to help solve many problems in the process of community common parts maintenance.

Research on the risk governance of fraudulent reimbursement of patient consultation fees.

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. 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). 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. 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".