Data-Driven Implementation To Filter Fraudulent Medicaid Applications

There has been much work to improve IT systems for managing and maintaining health records. The U.S government is trying to integrate different types of health care data for providers and patients. Health care fraud detection research has focused on claims by providers, physicians, hospitals, and other medical service providers to detect fraudulent billing, abuse, and waste. Data-mining techniques have been used to detect patterns in health care fraud and reduce the amount of waste and abuse in the health care system. However, less attention has been paid to implementing a system to detect fraudulent applications, specifically for Medicaid. In this study, a data-driven system using layered architecture to filter fraudulent applications for Medicaid was proposed. The Medicaid Eligibility Application System utilizes a set of public and private databases that contain individual asset records. These asset records are used to determine the Medicaid eligibility of applicants using a scoring model integrated with a threshold algorithm. The findings indicated that by using the proposed data-driven approach, the state Medicaid agency could filter fraudulent Medicaid applications and save over $4 million in Medicaid expenditures

Data-Driven Implementation To Filter Fraudulent Medicaid Applications

There has been much work to improve IT systems for managing and maintaining health records. The U.S government is trying to integrate different types of health care data for providers and patients. Health care fraud detection research has focused on claims by providers, physicians, hospitals, and other medical service providers to detect fraudulent billing, abuse, and waste. Data-mining techniques have been used to detect patterns in health care fraud and reduce the amount of waste and abuse in the health care system. However, less attention has been paid to implementing a system to detect fraudulent applications, specifically for Medicaid. In this study, a data-driven system using layered architecture to filter fraudulent applications for Medicaid was proposed. The Medicaid Eligibility Application System utilizes a set of public and private databases that contain individual asset records. These asset records are used to determine the Medicaid eligibility of applicants using a scoring model integrated with a threshold algorithm. The findings indicated that by using the proposed data-driven approach, the state Medicaid agency could filter fraudulent Medicaid applications and save over $4 million in Medicaid expenditures

Redescribing Health Privacy: The Importance of Health Policy

Current conversations about health information policy often tend to be based on three broad assumptions. First, many perceive a tension between regulation and innovation. We often hear that privacy regulations are keeping researchers, companies, and providers from aggregating the data they need to promote innovation. Second, aggregation of fragmented data is seen as a threat to its proper regulation, creating the risk of breaches and other misuse. Third, a prime directive for technicians and policymakers is to give patients ever more granular methods of control over data. This article questions and complicates those assumptions, which I deem (respectively) the Privacy Threat to Research, the Aggregation Threat to Privacy, and the Control Solution. This article is also intended to enrich our concepts of “fragmentation” and “integration” in health care. There is a good deal of sloganeering around “firewalls” and “vertical integration” as idealized implementations of “fragmentation” and “integration” (respective). The problem, though, is that terms like these (as well as “disruption”) are insufficiently normative to guide large-scale health system change. They describe, but they do not adequately prescribe. By examining those instances where: a) regulation promotes innovation, and b) increasing (some kinds of) availability of data actually enhances security, confidentiality, and privacy protections, this article attempts to give a richer account of the ethics of fragmentation and integration in the U.S. health care system. But, it also has a darker side, highlighting the inevitable conflicts of values created in a “reputation society” driven by stigmatizing social sorting systems. Personal data control may exacerbate social inequalities. Data aggregation may increase both our powers of research and our vulnerability to breach. The health data policymaking landscape of the next decade will feature a series of intractable conflicts between these important social values

Losing the War Against Dirty Money: Rethinking Global Standards on Preventing Money Laundering and Terrorism Financing

Following a brief overview in Part I.A of the overall system to prevent money laundering, Part I.B describes the role of the private sector, which is to identify customers, create a profile of their legitimate activities, keep detailed records of clients and their transactions, monitor their transactions to see if they conform to their profile, examine further any unusual transactions, and report to the government any suspicious transactions. Part I.C continues the description of the preventive measures system by describing the government\u27s role, which is to assist the private sector in identifying suspicious transactions, ensure compliance with the preventive measures requirements, and analyze suspicious transaction reports to determine those that should be investigated. Parts I.D and I.E examine the effectiveness of this system. Part I.D discusses successes and failures in the private sector\u27s role. Borrowing from theory concerning the effectiveness of private sector unfunded mandates, this Part reviews why many aspects of the system are failing, focusing on the subjectivity of the mandate, the disincentives to comply, and the lack of comprehensive data on client identification and transactions. It notes that the system includes an inherent contradiction: the public sector is tasked with informing the private sector how best to detect launderers and terrorists, but to do so could act as a road map on how to avoid detection should such information fall into the wrong hands. Part I.D discusses how financial institutions do not and cannot use scientifically tested statistical means to determine if a particular client or set of transactions is more likely than others to indicate criminal activity. Part I.D then turns to a discussion of a few issues regarding the impact the system has but that are not related to effectiveness, followed by a summary and analysis of how flaws might be addressed. Part I.E continues by discussing the successes and failures in the public sector\u27s role. It reviews why the system is failing, focusing on the lack of assistance to the private sector in and the lack of necessary data on client identification and transactions. It also discusses how financial intelligence units, like financial institutions, do not and cannot use scientifically tested statistical means to determine probabilities of criminal activity. Part I concludes with a summary and analysis tying both private and public roles together. Part II then turns to a review of certain current techniques for selecting income tax returns for audit. After an overview of the system, Part II first discusses the limited role of the private sector in providing tax administrators with information, comparing this to the far greater role the private sector plays in implementing preventive measures. Next, this Part turns to consider how tax administrators, particularly the U.S. Internal Revenue Service, select taxpayers for audit, comparing this to the role of both the private and public sectors in implementing preventive measures. It focuses on how some tax administrations use scientifically tested statistical means to determine probabilities of tax evasion. Part II then suggests how flaws in both private and public roles of implementing money laundering and terrorism financing preventive measures might be theoretically addressed by borrowing from the experience of tax administration. Part II concludes with a short summary and analysis that relates these conclusions to the preventive measures system. Referring to the analyses in Parts I and II, Part III suggests changes to the current preventive measures standard. It suggests that financial intelligence units should be uniquely tasked with analyzing and selecting clients and transactions for further investigation for money laundering and terrorism financing. The private sector\u27s role should be restricted to identifying customers, creating an initial profile of their legitimate activities, and reporting such information and all client transactions to financial intelligence units

Discovery of Transport Operations from Geolocation Data

Os dados de geolocalização identificam a localização geográfica de pessoas ou objetos e são fundamentais para empresas que dependem de veículos, como empresas logísticas e de transportes. Com o avanço da tecnologia, a recolha de dados de geolocalização tornou-se cada vez mais acessível e económica, gerando novas oportunidades de inteligência empresarial. Este tipo de dados tem sido utilizado principalmente para caracterizar o veículo em termos de posicionamento e navegação, mas também pode ter um papel preponderante na avaliação de desempenho em relação às atividades e operações executadas. A abordagem proposta consiste numa metodologia com várias etapas que recebe dados de geolocalização como entrada e permite a análise do processo de negócio no final. Em primeiro lugar, a preparação dos dados é aplicada para lidar com uma série de questões relacionadas com ruído e erros nos dados. Depois, a identificação dos eventos estacionários é realizada com base nos estados estacionários dos veículos. Em seguida, é realizada a inferência de operações com base numa análise espacial, que permite descobrir os locais onde os eventos estacionários ocorrem com frequência. Finalmente, as operações identificadas são classificadas com base nas suas características, e a sequência de eventos pode ser estruturada. A aplicação de técnicas de process mining é então possível e a consequente extração de conhecimento do processo. As etapas da metodologia também podem ser utilizadas separadamente para enfrentar desafios específicos, dando mais flexibilidade à sua aplicação. Três estudos de caso distintos são apresentados para demonstrar a eficácia e transversalidade da solução. Fluxos de dados de geolocalização em tempo real de autocarros de duas redes distintas de transporte público são usados para demonstrar a detecção de operações relacionadas com os veículos e comparar as distintas abordagens propostas por este trabalho. As operações dos autocarros produzem uma sequência estruturada de eventos que descreve o comportamento dos mesmos. Esse comportamento é mapeado por meio da aplicação de técnicas de process mining, para descobrir oportunidades de análise e gargalos no processo. Complementarmente, os dados de geolocalização de uma empresa de logística internacional são explorados para a monitorização de processos logísticos, nomeadamente para detecção de operações de logística em tempo real, demonstrando a eficácia da solução proposta para resolver problemas específicos da indústria. Os resultados deste trabalho revelam novas possibilidades no uso de dados de geolocalização e o seu potencial para gerar conhecimento acerca do processo. A exploração de dados de geolocalização nos contextos logísticos e de transportes públicos apresenta-se como uma oportunidade para melhorar a monitorização e gestão das operações baseadas em veículos. Isso pode originar melhorias na eficiência do processo e, consequentemente, maior lucro e melhor qualidade do serviço.Geolocation data identifies the geographic location of people or objects, and is fundamental for businesses relying on vehicles such as logistics and transportation. With the advance of technology, collecting geolocation data has become increasingly accessible and affordable, raising new opportunities for business intelligence. This type of data has been used mainly for characterizing the vehicle in terms of positioning and navigation, but it can also showcase its performance regarding the executed activities and operations. The proposed approach consists on a multi-step methodology that receives geolocation data as an input and allows the analysis of the business process in the end. Firstly, the preparation of the data is applied to handle a number of issues related to outliers, data noise, and missing or erroneous information. Then, the identification of stationary events is performed based on the motionless states of the vehicles. Next, the inference of operations based on a spatial analysis is performed, which allows the discovery of the locations where stationary events occur frequently. Finally, the identified operations are classified based on their characteristics, and the sequence of events can be structured into an event log. The application of process mining techniques is then possible and the consequently extraction of process knowledge. The steps of the methodology can also be used separately to tackle specific challenges, giving more flexibility to its application. Three distinct case studies are presented to demonstrate the effectiveness and transversality of the solution. Real-time geolocation data streams of buses from two distinct public transport networks are used to demonstrate the detection of vehicle-based operations and compare the distinct approaches proposed by this work. The buses operations produce a structured sequence of events that describes the behaviour of the buses. This behaviour is mapped through the application of process mining techniques uncovering analysis opportunities and discovering bottlenecks in the process. Geolocation data from an international logistics company is exploited for monitoring logistics processes, namely for detecting vehicle-based operations in real time, showing the effectiveness of the proposed solution to solve specific industry problems. The results of this work reveal new possibilities for geolocation data and its potential to generate process knowledge. The exploitation of geolocation data in the public transport and logistics contexts poses as an opportunity for improving the monitoring and management of vehicle-based operations. This can lead to into improvements in the process efficiency and consequently higher profit and better service quality

Predictive Modeling of Fuel Efficiency of Trucks

This research studied the behavior of several controllable variables that affect the fuel efficiency of trucks. Re-routing is the process of modifying the parameters of the routes for a set of trips to optimize fuel consumption and also to increase customer satisfaction through efficient deliveries. This is an important process undertaken by a food distribution company to modify the trips to adapt to the immediate necessities. A predictive model was developed to calculate the change in Miles per Gallon (MPG) whenever a re-route is performed on a region of a particular distribution area. The data that was used, was from the Dallas center which is one of the distribution centers owned by the company. A consistent model that could provide relatively accurate predictions across five distribution centers had to be developed. It was found that the model built using the data from the Corporate center was the most consistent one. The timeline of the data used to build the model was from May 2013 through December 2013. The predictive model provided predictions of which about 88% of the data that was used, was within the 0-10% error group. This was an improvement on the lesser 43% obtained for the linear regression and K-means clustering models. The model was also validated on the data for January 2014 through the first two weeks of March 2014 and it provided predictions of which about 81% of the data was within the 0-10 % error group. The average overall error was around 10%, which was the least for the approaches explored in this research. Weight, stop count and stop time were identified as the most significant factors which influence the fuel efficiency of the trucks. Further, neural network architecture was built to improve the predictions of the MPG. The model can be used to predict the average change in MPG for a set of trips whenever a re-route is performed. Since the aim of re-routing is to reduce the miles and trips; extra load will be added to the remaining trips. Although, the MPG would decrease because of this extra load, it would be offset by the savings due to the drop in miles and trips. The net savings in the fuel can now be translated into the amount of money saved

The value of modus operandi in fraud investigation : a short-term insurance industry perspective

This study sought to examine the value of modus operandi (MO) information in the investigation of short-term insurance fraud. A comprehensive literature study was conducted concerning the dynamics of MO information in forensic investigation and short-term insurance fraud in South Africa and internationally, and individual semi-structured interviews were conducted with forensic investigators at Santam and MiWay to promote knowledge and understanding of the importance of MO information in short-term insurance fraud investigations. Results of this research indicate that participants did grasp the significance of MO information in the investigation of short-term insurance fraud. It is, however, apparent that they did not optimally exploit MO information regarding insurance fraud as a result of limited experience, ineffective databases and the inaccessibility of available data – all of which prevent the improvement of utilising MO data pertaining to short-term insurance fraud. Forensic investigators in the short-term insurance industry isolate themselves from each other and fail to share the available MO information amongst each other, resulting in a non-systematic fragmented approach to short-term insurance fraud investigation. The study identifies the challenges and shortcomings experienced by forensic investigators at Santam and MiWay that prevent the optimal utilisation of MO information in the investigation of short-term insurance fraud. The study then suggests a set of recommendations that could assist forensic investigators and other role-players in enhancing the utilisation of such information.Criminology and Security ScienceM. Tech. (Forensic Investigation

Web-based Geographical Visualization of Container Itineraries

Around 90% of the world cargo is transported in maritime containers, but only around 2% are physically inspected. This opens the possibility for illicit activities. A viable solution is to control containerized cargo through information-based risk analysis. Container route-based analysis has been considered a key factor in identifying potentially suspicious consignments. Essential part of itinerary analysis is the geographical visualization of the itinerary. In the present paper, we present initial work of a web-based system’s realization for interactive geographical visualization of container itinerary.JRC.G.4-Maritime affair

The Corporate Purpose of Social License

This Article deploys the sociological theory of social license, or the acceptance of a business or organization by the relevant communities and stakeholders, in the context of the board of directors and corporate governance. Corporations are generally treated as “private” actors and thus are regulated by “private” corporate law. This construct allows for considerable latitude. Corporate actors are not, however, solely “private.” They are the beneficiaries of economic and political power, and the decisions they make have impacts that extend well beyond the boundaries of the entities they represent. Using Wells Fargo and Uber as case studies, this Article explores how the failure to account for the public nature of corporate actions, regardless of whether a “legal” license exists, can result in the loss of “social” license. This loss occurs through publicness, which is the interplay between inside corporate governance players and outside actors who report on, recapitulate, reframe and, in some cases, control the company’s information and public perception. The theory of social license is that businesses and other entities exist with permission from the communities in which they are located, as well as permission from the greater community and outside stakeholders. In this sense, businesses are social, not just economic, institutions and, thus, they are subject to public accountability and, at times, public control. Social license derives not from legally granted permission, but instead from the development of legitimacy, credibility, and trust within the relevant communities and stakeholders. It can prevent demonstrations, boycotts, shutdowns, negative publicity, and the increases in regulation that are a hallmark of publicness — but social license must be earned with consistent trustworthy behavior. Thus, social license is bilateral, not unilateral, and should be part of corporate strategy and a tool for risk management and managing publicness more generally. By focusing on and deploying social license and publicness in the context of board decision-making, this Article adds to the discussions in the literature from other disciplines, such as the economic theory on reputational capital, and provides boards with a set of standards with which to engage and address the publicness of the companies they represent. Discussing, weighing, and developing social license is not just in the zone of what boards can do, but is something they should do, making it a part of strategic, proactive cost-benefit decision-making. Indeed, the failure to do so can have dramatic business consequences