Health insurance fraud detection based on multi-channel heterogeneous graph structure learning.

Health insurance fraud is becoming more common and impacting the fairness and sustainability of the health insurance system. Traditional health insurance fraud detection primarily relies on recognizing established data patterns. However, with the ever-expanding and complex nature of health insurance data, it is difficult for these traditional methods to effectively capture evolving fraudulent activity and tactics and keep pace with the constant improvements and innovations of fraudsters. As a result, there is an urgent need for more accurate and flexible analytics to detect potential fraud. To address this, the Multi-channel Heterogeneous Graph Structured Learning-based health insurance fraud detection method (MHGSL) was proposed. MHGSL constructs a graph of health insurance data from various entities, such as patients, departments, and medicines, and employs graph structure learning to extract topological structure, features, and semantic information to construct multiple graphs that reflect the diversity and complexity of the data. We utilize deep learning methods such as heterogeneous graph neural networks and graph convolutional neural networks to combine multi-channel information transfer and feature fusion to detect anomalies in health insurance data. The results of extensive experiments on real health insurance data demonstrate that MHGSL achieves a high level of accuracy in detecting potential fraud, which is better than existing methods, and is able to quickly and accurately identify patients with fraudulent behaviors to avoid loss of health insurance funds. Experiments have shown that multi-channel heterogeneous graph structure learning in MHGSL can be very helpful for health insurance fraud detection. It provides a promising solution for detecting health insurance fraud and improving the fairness and sustainability of the health insurance system. Subsequent research on fraud detection methods can consider semantic information between patients and different types of entities.

A novel approach for credit card fraud transaction detection using deep reinforcement learning scheme.

Online transactions are still the backbone of the financial industry worldwide today. Millions of consumers use credit cards for their daily transactions, which has led to an exponential rise in credit card fraud. Over time, many variations and schemes of fraudulent transactions have been reported. Nevertheless, it remains a difficult task to detect credit card fraud in real-time. It can be assumed that each person has a unique transaction pattern that may change over time. The work in this article aims to (1) understand how deep reinforcement learning can play an important role in detecting credit card fraud with changing human patterns, and (2) develop a solution architecture for real-time fraud detection. Our proposed model utilizes the Deep Q network for real-time detection. The Kaggle dataset available online was used to train and test the model. As a result, a validation performance of 97.10% was achieved with the proposed deep learning component. In addition, the reinforcement learning component has a learning rate of 80%. The proposed model was able to learn patterns autonomously based on previous events. It adapts to the pattern changes over time and can take them into account without further manual training.

William Lawrence Tower's Beetles: Experimental Evolution and the Manipulation of Inheritance.

William Lawrence Tower's work on the evolution of the Colorado Potato Beetle (Leptinotarsa decemlineata), documenting the environmental induction of mutation and speciation, made him a leading figure in experimental genetics during the first decade of the 20th century. His research program served as a model for other experimental evolution studies seeking to demonstrate the environmental modification of inheritance. Tower enjoyed the support of influential figures in the field, despite well-known problems that plagued Tower's earlier academic career. The validity of his genetic work, and other findings reported by Tower, were later challenged. The Tower affair illustrates how questionable and possibly fraudulent scientific practices can be tolerated to explore certain experimental directions and theoretical frameworks, particularly at the frontier of expanding disciplines. When needed, those explorations can be forestalled or extinguished by exploiting conspicuous vulnerabilities of rogue practitioners. In Tower's case, both unrefuted allegations of scientific misconduct and personal problems dissolved his institutional support, leading to a swift ouster from academic science. Tower's downfall discredited soft inheritance and neo-Lamarckian conceptions in the field of experimental genetics, facilitating the discipline's embrace of a hard inheritance model that featured a hereditary material resistant to environmental modification.

Fingerprinting of Octane Rating Gasoline and its Contaminants Using Synchronous Fluorescence Spectroscopy.

Fuel fraud has proliferated due to underlying economic advantage in nearly every nation. For the purpose of detecting adulteration and providing real-time quality assurance, non-destructive oil analysis is crucial. This paper reports the simple approach for fingerprinting undiluted petroleum products including gasoline from various brands, diesel, and kerosene oil in comparison with organic solvents using synchronous fluorescence spectroscopy and hierarchical cluster analysis. Fluorescence-based successful detection of adulterated samples is demonstrated in imported RON 92 gasoline, synthetically adulterated with kerosene oil (KO) in proportions up to 70%. Compared to gasoline, kerosene oil has a lower relative poly aromatic hydrocarbons, as the amount of kerosene oil (KO) increases, the KO peak at 352 nm rises, but the gasoline's peak intensity decreases in the range of 371-500 nm. It is noteworthy that imported fuel grades RON 92 and RON 95 are comparable to each other and surprisingly clustered with RON 91 from the Attock refinery presenting concerns about quality. Similarly, the Shell website mentions that Shell V-Power is RON 99 but interestingly it clusters with retail fuel samples acquired from PSO filling stations and PSO RON 95 showing disagreement with the claim that the fuel is high octane. Another use for this technique in oil exploration was the detection of adulterants and successfully spotted methanol, ethanol, and kerosene oil in the tainted samples. These findings suggest SFS as an accurate, and low-cost testing tool for gasoline fingerprinting and contamination screening.

Prevention of food fraud and fraud emulation among companies in the supply chain based on a social Co-governance framework.

This study develops a three-party evolutionary game model among upstream raw material producers, midstream food producers, and downstream distributors in the food supply chain, and investigates food fraud and fraud emulation among companies in the same group based on a food safety social co-governance framework. Moreover, the equilibrium points are divided into four scenarios according to the number of groups of companies committing fraud in the supply chain and whether companies in the same group emulate each other's fraudulent behavior. The stability conditions of these scenarios are also discussed and verified by numerical simulation in MATLAB. The results show that the behavioral strategy choices of different groups of food companies in the supply chain are closely related to the level of social co-governance involving the government, market, and consumers. Government regulation, supervision between companies, and consumer reporting can all change companies' behavioral strategies. Although the level of fraud emulation among companies in the same group does not change their behavioral strategy choice, it affects the time it takes for their behavioral strategy to evolve to a stable state. Moreover, the level of social co-governance directly affects companies' behavioral strategy choices at different emulation levels.

Radiology quick cash? Kickbacks, compliance, and consequences.

The Anti-Kickback Statute was passed by Congress in the 1970s to reduce the overuse of government-reimbursed medical services. It attempts to eliminate fraud, abuse, and waste of medical services by outlawing the incentive of personal gain when referring patients for government-funded services. Although safe harbors were written into the law to maintain transactions beneficial to society, they require strict adherence. Anti-Kickback Statute violations are subject to the whistleblower provision of the False Claims Act, and violations can yield significant civil and criminal penalties.

Parvalbumin Gene: A Valuable Marker for Pike Authentication and Allergen Risk Assessment.

Fish from the pike (Esox) genus are valued in gastronomy for their superior meat quality. However, they can cause allergic reactions in sensitive consumers. This work aimed to fill the gap in the detection of pike allergens using molecular-biological techniques. New, fast, and accurate loop-mediated isothermal amplification (LAMP) and real-time PCR (qPCR) assays were designed to detect pike DNA using the parvalbumin gene as a marker. LAMP was assessed by electrophoresis, SYBR green optical detection, and real-time fluorescence detection. The latter was the most sensitive, detecting as little as 0.78 ng of pike DNA; the qPCR detection limit was 0.1 ng. The LAMP analysis took 20-70 min, which is significantly faster than qPCR. The study provides reliable detection and quantification of the parvalbumin gene in both fresh and processed samples and further highlights the versatility of the use of the parvalbumin gene for the authentication of food products and consumer protection via refined allergen risk assessment that is independent of the type of tissue or food processing method used.

Food Fraud Conceptualization: An Exploratory Study with Portuguese Consumers.

Food fraud refers to deceptive practices conducted for economic gain, and incidents of such fraud are often reported in the media and scientific literature. However, little is known about how European consumers perceive food fraud. To address this gap, a study explored Portuguese consumers' knowledge and perceptions of food fraud using qualitative methods such as free word association and semi-structured interviews. For this research, 340 participants were recruited, providing 911 valid words, classified into categories, major categories, and dimensions. Differences between consumers' previous exposure to food fraud and sociodemographic characteristics were explored. Additionally, other thirty-six participants were selected and interviewed, following a semi-structured guide. Interviews were transcribed, coded, and analyzed using a thematic analysis procedure. The results suggest that Portuguese consumers view food fraud as a morally reprehensible deception and are aware of its causes and impacts. However, not all consumers know the different forms of food fraud or the types of products vulnerable to fraud. Among the most repeated words were "deception", "expiration date", and "falsification". Despite this food fraud awareness, most consumers believed they were not exposed to food fraud and stated that they do not conduct daily practices to reduce exposure to it. Following the chi-square and Mann-Whitney tests, significant differences (p ≤ 0.05) were identified between participants exposed and not exposed to food fraud. The study also found that consumers with higher education and self-reported exposure to food fraud had a better understanding of the issue. This study provides insights for quantitative research on consumer perceptions and beliefs about food fraud to explore further vulnerable food categories and types of food fraud in real-world scenarios.

Rapid and sensitive approaches for detecting food fraud: A review on prospects and challenges.

Precise and reliable analytical techniques are required to guarantee food quality in light of the expanding concerns regarding food safety and quality. Because traditional procedures are expensive and time-consuming, quick food control techniques are required to ensure product quality. Various analytical techniques are used to identify and detect food fraud, including spectroscopy, chromatography, DNA barcoding, and inotrope ratio mass spectrometry (IRMS). Due to its quick findings, simplicity of use, high throughput, affordability, and non-destructive evaluations of numerous food matrices, NI spectroscopy and hyperspectral imaging are financially preferred in the food business. The applicability of this technology has increased with the development of chemometric techniques and near-infrared spectroscopy-based instruments. The current research also discusses the use of several multivariate analytical techniques in identifying food fraud, such as principal component analysis, partial least squares, cluster analysis, multivariate curve resolutions, and artificial intelligence.

Death anxiety and older adults' vulnerability to fraud: the mediating role of materialism.

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.

Food authentication, current issues, analytical techniques, and future challenges: A comprehensive review.

Food authentication and contamination are significant concerns, especially for consumers with unique nutritional, cultural, lifestyle, and religious needs. Food authenticity involves identifying food contamination for many purposes, such as adherence to religious beliefs, safeguarding health, and consuming sanitary and organic food products. This review article examines the issues related to food authentication and food fraud in recent periods. Furthermore, the development and innovations in analytical techniques employed to authenticate various food products are comprehensively focused. Food products derived from animals are susceptible to deceptive practices, which can undermine customer confidence and pose potential health hazards due to the transmission of diseases from animals to humans. Therefore, it is necessary to employ suitable and robust analytical techniques for complex and high-risk animal-derived goods, in which molecular biomarker-based (genomics, proteomics, and metabolomics) techniques are covered. Various analytical methods have been employed to ascertain the geographical provenance of food items that exhibit rapid response times, low cost, nondestructiveness, and condensability.

Current trends in retraction of plastic surgery and reconstruction research.

BACKGROUND: Various studies regarding retractions of publications have determined the rate of retraction has increased in recent years. Although this trend may apply to any field, there is a paucity of literature exploring the publication of erroneous studies within plastic and reconstructive surgery. The present study aims to identify trends in frequency and reasons for retraction of plastic and reconstructive surgery studies, with analysis of subspecialty and journals. METHODS: A database search was conducted for retracted papers within plastic and reconstructive surgery. The initial search yielded 2347 results, which were analyzed by two independent reviewers. 77 studies were jointly identified for data collection. RESULTS: The most common reasons for retractions were duplication (n = 20, 25.9 %), request of author (n = 15, 19.5 %), plagiarism (n = 9, 11.6 %), error (n = 9, 11.6 %), fraud (n = 2, 2.6 %), and conflict of interest (n = 1, 1.3 %). 15 were basic science studies (19.4 %), 58 were clinical science studies (75.3 %), and 4 were not categorized (5.2 %). Subspecialties of retracted papers were maxillofacial (n = 29, 37.7 %), reconstructive (n = 17, 22.0 %), wound healing (n = 8, 10.4 %), burn (n = 6, 7.8 %), esthetics (n = 5, 6.5 %), breast (n = 3, 3.9 %), and trauma (n = 1, 1.3 %). Mean impact factor was 2.9 and average time from publication to retraction was 32 months. CONCLUSION: Analysis of retracted plastic surgery studies revealed a recent rise in frequency of retractions, spanning a wide spectrum of journals and subspecialties.

cor1 Gene: A Suitable Marker for Identification of Opium Poppy (Papaver somniferum L.).

This paper discusses the development of rapid, reliable, and accurate polymerase chain reaction (PCR) assays for detecting opium poppy (Papaver somniferum L.) in food. Endpoint, quantitative, and digital PCRs were compared based on the amplification of a newly developed DNA marker targeting the NADPH-dependent codeinone reductase (COR) gene. Designed assays were shown to be highly specific and sensitive in discriminating opium poppy from other plant species, even in heat-treated and food samples. Digital PCR was the most sensitive, with a detection limit of up to 5 copies, i.e., approximately 14 pg of target DNA per reaction. Quantitative and digital PCR further allowed the quantification of opium poppy in up to 1.5 ng and 42 pg (15 copies) of target DNA in a sample, respectively. In addition, two duplex PCRs have been developed for the simultaneous detection of opium poppy DNA and representatives of (i) the Papaveraceae family or (ii) the Plantae kingdom. Finally, all designed assays were successfully applied for analysis of 15 commercial foodstuffs; two were suspected of being adulterated. The study results have an important impact on addressing food fraud and ensuring the safety and authenticity of food products. Beyond food adulteration, the study may also have significant implications for forensics and law enforcement.

Data mining algorithm in the identification of accounting fraud by smart city information technology.

The identification of accounting fraud is an important measure to safeguard the interests of stakeholders and ensure the long-term development of the company. The current traditional methods for identifying accounting fraud rely on manual review and judgment, lacking objectivity and accuracy. In order to improve the accuracy of accounting fraud identification, improve identification efficiency and objectivity, this article combines smart city information technology to conduct in-depth research on data mining algorithms for accounting fraud identification. This article first provides a brief overview of smart cities and information technology, then introduces the basic theory of accounting fraud identification, and finally implements accounting fraud identification through k-means clustering mining algorithm. The data is divided into k clusters, and abnormal clusters are identified by checking the characteristics and attributes of each cluster. Compared with traditional rule-based and pattern based methods, this approach can more flexibly adapt to different types and forms of fraud, and can discover unknown patterns of fraud. In the experiment, this article used electronic data collection, analysis, and retrieval systems on the websites of the Shanghai Stock Exchange and Shenzhen Stock Exchange to collect 641 annual reports and financial characteristics from 62 listed companies that engaged in financial statement fraud and 84 companies that were not reported to have financial statement fraud from 2012 to 2021 as test samples. The results were tested and analyzed from several aspects, including the number of misjudgments, misjudgment rate, and ROC curve. The final test results show that compared to traditional accounting fraud identification methods, the comprehensive misjudgment rate of data mining algorithms based on smart cities has decreased by 3 %. The conclusion indicates that data mining algorithms used in smart city information technology to identify accounting fraud can help improve the accuracy of accounting fraud, improve audit objectivity and effectiveness.

Insider Threats to the Military Health System: A Systematic Background Check of TRICARE West Providers.

Background: To address the pandemic, the Defense Health Agency (DHA) expanded its TRICARE civilian provider network by 30.1%. In 2022, the DHA Annual Report stated that TRICARE's provider directories were only 80% accurate. Unlike Medicare, the DHA does not publicly reveal National Provider Identification (NPI) numbers. As a result, TRICARE's 9.6 million beneficiaries lack the means to verify their doctor's credentials. Since 2013, the Department of Health and Human Services' (HHS) Office of Inspector General (OIG) has excluded 17,706 physicians and other providers from federal health programs due to billing fraud, neglect, drug-related convictions, and other offenses. These providers and their NPIs are included on the OIG's List of Excluded Individuals and Entities (LEIE). Patients who receive care from excluded providers face higher risks of hospitalization and mortality. Objective: We sought to assess the extent to which TRICARE screens health care provider names on their referral website against criminal databases. Methods: Between January 1-31, 2023, we used TRICARE West's provider directory to search for all providers within a 5-mile radius of 798 zip codes (38 per state, ≥10,000 residents each, randomly entered). We then copied and pasted all directory results' first and last names, business names, addresses, phone numbers, fax numbers, degree types, practice specialties, and active or closed statuses into a CSV file. We cross-referenced the search results against US and state databases for medical and criminal misconduct, including the OIG-LEIE and General Services Administration's (GSA) SAM.gov exclusion lists, the HHS Office of Civil Rights Health Insurance Portability and Accountability Act (HIPAA) breach reports, 15 available state Medicaid exclusion lists (state), the International Trade Administration's Consolidated Screening List (CSL), 3 Food and Drug Administration (FDA) debarment lists, the Federal Bureau of Investigation's (FBI) list of January 6 federal defendants, and the OIG-HHS list of fugitives (FUG). Results: Our provider search yielded 111,619 raw results; 54 zip codes contained no data. After removing 72,156 (64.65%) duplicate entries, closed offices, and non-TRICARE West locations, we identified 39,463 active provider names. Within this baseline sample group, there were 2398 (6.08%) total matches against all exclusion and sanction databases, including 2197 on the OIG-LEIE, 2311 on the GSA-SAM.gov list, 2 on the HIPAA list, 54 on the state Medicaid exclusion lists, 69 on the CSL, 3 on the FDA lists, 53 on the FBI list, and 10 on the FUG. Conclusions: TRICARE's civilian provider roster merits further scrutiny by law enforcement. Following the National Institute of Standards and Technology 800, the DHA can mitigate privacy, safety, and security clearance threats by implementing an insider threat management model, robust enforcement of the False Claims Act, and mandatory security risk assessments. These are the views of the author, not the Department of Defense or the US government.

Development of a LC-QTOF-MS based dilute-and-shoot approach for the botanical discrimination of honeys.

Honeys of particular botanical origins can be associated with premium market prices, a trait which also makes them susceptible to fraud. Currently available authenticity testing methods for botanical classification of honeys are either time-consuming or only target a few "known" types of markers. Simple and effective methods are therefore needed to monitor and guarantee the authenticity of honey. In this study, a 'dilute-and-shoot' approach using liquid chromatography (LC) coupled to quadrupole time-of-flight-mass spectrometry (QTOF-MS) was applied to the non-targeted fingerprinting of honeys of different floral origin (buckwheat, clover and blueberry). This work investigated for the first time the impact of different instrumental conditions such as the column type, the mobile phase composition, the chromatographic gradient, and the MS fragmentor voltage (in-source collision-induced dissociation) on the botanical classification of honeys as well as the data quality. Results indicated that the data sets obtained for the various LC-QTOF-MS conditions tested were all suitable to discriminate the three honeys of different floral origin regardless of the mathematical model applied (random forest, partial least squares-discriminant analysis, soft independent modelling by class analogy and linear discriminant analysis). The present study investigated different LC-QTOF-MS conditions in a "dilute and shoot" method for honey analysis, in order to establish a relatively fast, simple and reliable analytical method to record the chemical fingerprints of honey. This approach is suitable for marker discovery and will be used for the future development of advanced predictive models for honey botanical origin.

Validation of the brief version of the Susceptibility to Persuasion-II scale and evaluation of measurement invariance across age and gender in the Canadian general population.

Measurement tools that can assess personality traits rendering people more susceptible to engagement with and compliance in scams can help identify at-risk populations. The brief, 30-item version of the Susceptibility to Persuasion-II (StP-II-B) scale is a recently developed instrument for assessing 10 personality traits that play a role in scam compliance; however, psychometric evidence supporting the use of this scale is limited. This study aimed to validate the StP-II-B by examining its internal consistency reliability, factor structure, as well as age- and gender-related measurement invariance with a sample of 1287 Canadians aged 16 years and older. Confirmatory factor analysis supported a 10-factor structure identified in previous research. Good internal consistency reliability was obtained for each of the 10 subscales. This 10-factor structure was found to be invariant across age and gender at configural, metric, and scalar levels, suggesting that the StP-II-B was conceptualized in the same way across age and gender and that meaningful comparisons of factor scores could be made. Age and gender differences were found in most factors, with younger individuals and men scoring higher than older individuals and women. This study supports the use of the StP-II-B as a valid and reliable scale for measuring personality traits associated with scam compliance in the Canadian general population and offers insights into age and gender cohorts that may be at higher risk of scam victimization.

Enhancing fraud detection in banking by integration of graph databases with machine learning.

The banking sector's shift from traditional physical locations to digital channels has offered customers unprecedented convenience and increased the risk of fraud for customers and institutions alike. In this study, we discuss the pressing need for robust fraud detection & prevention systems in the context of evolving technological environments. We introduce a graph-based machine learning model that is specifically designed to detect fraudulent activity in various types of banking operations, such as credit card transactions, debit card transactions, and online banking transactions. This model uses advanced methods for anomalies, behaviors, and patterns to analyze past transactions and user behavior almost immediately. We provide an in-depth methodology for evaluating fraud detection systems based on parameters such as Accuracy Recall rate and False positive rate ROC curves. The findings can be used by financial institutions to develop and enhance fraud detection strategies as they demonstrate the effectiveness and reliability of the proposed approach. This study emphasizes the critical role that innovative technologies play in safeguarding the financial sector from the ever-changing strategies of fraudsters while also enhancing banking security.•This paper aims to implement the detection of fraudulent transactions using a state-of-the-art Graph Database approach.•The relational graph of features in the dataset used is modelled using Neo4J as a graph database.•Applying JSON features from the exported graph to various Machine Learning models, giving effective outcomes.

Perceptions of Consumer Fraud and Aging Among Older People and the Judiciary in China.

Systematic research has been conducted on the relationship between aging and consumer fraud victimization. But few empirical studies examine the reality of judicial dispute resolution in consumer fraud against older people from the perspective of older adults and judges in China. Based on 161 court rulings, this qualitative study explores the perceptions of older adults in litigation about their experiences of being defrauded in China, alongside judges' responses. Results reveal that common fraud patterns used by business perpetrators render older individuals more susceptible to fraud. Older plaintiffs strategically frame "old age" in litigation, potentially as a tactical maneuver, while also readily emphasizing the importance of procedural justice. Results further show that judges expressed either negative ageism or age-neutral discourse in response to fraud targeting older individuals. Findings highlight the need to enhance consumer education to prevent fraud and address ageist stereotypes among older people. Findings also highlight the need to encourage Chinese courts to consider individual case specifics, leading to fair judgments and the protection of older individuals from fraud while upholding their rights.

Advancing data honesty in experimental biology.

The ease with which scientific data, particularly certain types of raw data in experimental biology, can be fabricated without trace begs urgent attention. This is thought to be a widespread problem across the academic world, where published results are the major currency, incentivizing publication of (usually positive) results at the cost of lax scientific rigor and even fraudulent data. Although solutions to improve data sharing and methodological transparency are increasingly being implemented, the inability to detect dishonesty within raw data remains an inherent flaw in the way in which we judge research. We therefore propose that one solution would be the development of a non-modifiable raw data format that could be published alongside scientific results; a format that would enable data authentication from the earliest stages of experimental data collection. A further extension of this tool could allow changes to the initial original version to be tracked, so every reviewer and reader could follow the logical footsteps of the author and detect unintentional errors or intentional manipulations of the data. Were such a tool to be developed, we would not advocate its use as a prerequisite for journal submission; rather, we envisage that authors would be given the option to provide such authentication. Only authors who did not manipulate or fabricate their data can provide the original data without risking discovery, so the mere choice to do so already increases their credibility (much like 'honest signaling' in animals). We strongly believe that such a tool would enhance data honesty and encourage more reliable science.