Examining the Transitional Impact of ICD-10 on Healthcare Fraud Detection

On October 1st, 2015, the tenth revision of the International Classification of Diseases (ICD-10) will be mandatorily implemented in the United States. Although this medical classification system will allow healthcare professionals to code with greater accuracy, specificity, and detail, these codes will have a significant impact on the flavor of healthcare insurance claims. While the overall benefit of ICD-10 throughout the healthcare industry is unquestionable, some experts believe healthcare fraud detection and prevention could experience an initial drop in performance due to the implementation of ICD-10. We aim to quantitatively test the validity of this concern regarding an adverse transitional impact. This project explores how predictive fraud detection systems developed using ICD-9 claims data will initially react to the introduction of ICD-10. We have developed a basic fraud detection system incorporating both unsupervised and supervised learning methods in order to examine the potential fraudulence of both ICD-9 and ICD-10 claims in a predictive environment. Using this system, we are able to analyze the ability and performance of statistical methods trained using ICD-9 data to properly identify fraudulent ICD-10 claims. This research makes contributions to the domains of medical coding, healthcare informatics, and fraud detection

Trust and Experience in Online Auctions

This paper aims to shed light on the complexities and difficulties in predicting the effects of trust and the experience of online auction participants on bid levels in online auctions. To provide some insights into learning by bidders, a field study was conducted first to examine auction and bidder characteristics from eBay auctions of rare coins. We proposed that such learning is partly because of institutional-based trust. Data were then gathered from 453 participants in an online experiment and survey, and a structural equation model was used to analyze the results. This paper reveals that experience has a nonmonotonic effect on the levels of online auction bids. Contrary to previous research on traditional auctions, as online auction bidders gain more experience, their level of institutional-based trust increases and leads to higher bid levels. Data also show that both a bidder’s selling and bidding experiences increase bid levels, with the selling experience having a somewhat stronger effect. This paper offers an in-depth study that examines the effects of experience and learning and bid levels in online auctions. We postulate this learning is because of institutional-based trust. Although personal trust in sellers has received a significant amount of research attention, this paper addresses an important gap in the literature by focusing on institutional-based trust

Fake-Website Detection Tools: Identifying Elements that Promote Individuals’ Use and Enhance Their Performance

By successfully exploiting human vulnerabilities, fake websites have emerged as a major source of online fraud. Fake websites continue to inflict exorbitant monetary losses and also have significant ramifications for online security. We explore the process by which salient performance-related elements could increase the reliance on protective tools and, thus, reduce the success rate of fake websites. We develop the theory of detection tool impact (DTI) for this investigation by borrowing and contextualizing the protection motivation theory. Based on the DTI theory, we conceptualize a model to investigate how salient performance and cost-related elements of detection tools could influence users’ perceptions of the tools and threats, efficacy in dealing with threats, and reliance on such tools. The research method was a controlled lab experiment with a novel and extensive experimental design and protocol in two distinct domains: online pharmacies and banks. We found that the detector accuracy and speed, reflecting in response efficacy as perceived by users, form the pivotal coping mechanism in dealing with security threats and are major conduits for transforming salient performance-related elements into increased reliance on the detector. Furthermore, reported reliance on the detector showed a significant impact on the users’ performance in terms of self-protection. Therefore, users’ perceived response efficacy should be used as a critical metric to evaluate the design, assess the performance, and promote the use of fake-website detectors. We also found that cost of detector error had profound impacts on threat perceptions. We discuss the significant theoretical and empirical implications of the findings

Unsupervised Intrusion Detection with Cross-Domain Artificial Intelligence Methods

Cybercrime is a major concern for corporations, business owners, governments and citizens, and it continues to grow in spite of increasing investments in security and fraud prevention. The main challenges in this research field are: being able to detect unknown attacks, and reducing the false positive ratio. The aim of this research work was to target both problems by leveraging four artificial intelligence techniques. The first technique is a novel unsupervised learning method based on skip-gram modeling. It was designed, developed and tested against a public dataset with popular intrusion patterns. A high accuracy and a low false positive rate were achieved without prior knowledge of attack patterns. The second technique is a novel unsupervised learning method based on topic modeling. It was applied to three related domains (network attacks, payments fraud, IoT malware traffic). A high accuracy was achieved in the three scenarios, even though the malicious activity significantly differs from one domain to the other. The third technique is a novel unsupervised learning method based on deep autoencoders, with feature selection performed by a supervised method, random forest. Obtained results showed that this technique can outperform other similar techniques. The fourth technique is based on an MLP neural network, and is applied to alert reduction in fraud prevention. This method automates manual reviews previously done by human experts, without significantly impacting accuracy

LAW ENFORCEMENT RISK MODEL TO COMBAT OPIOID RECIDIVISM

Bergen County, New Jersey, has seen opioid-related overdoses and deaths spike in the last few years. One of the challenges in addressing this epidemic is that “at-risk” individuals may encounter multiple segmented domains such as law enforcement, recovery services, and healthcare institutions, but no one agency has oversight of all the contacts. Each encounter with at-risk populations, including those who suffer from opioid addiction or who may recidivate, becomes a data record in a system. This thesis asks how can law enforcement leverage such data sets to address the opioid epidemic and battle recidivism? This research examined law enforcement arrest data and overdose reporting in Bergen County, analyzing which risk factors in recidivism could be discerned using statistical information, cross-tabulations, Pearson’s chi-squared tests, and data modeling from the Cox proportional hazards model. The results showed that no demographic profile was more likely to have another overdose or death, and theft arrests coincided with a decreased chance of overdose, despite law enforcement’s presumption of the contrary. The strongest predictor of an overdose was a prior overdose, with the risk increasing for each additional overdose. Additionally, having any contact with law enforcement was an indicator of a significantly higher chance of overdose or death. Thus, each interaction between law enforcement and an observed opioid abuser is a critical point for intervention.Civilian, Bergen County Prosecutor's Office New JerseyApproved for public release. Distribution is unlimited

Advanced analytical methods for fraud detection: a systematic literature review

The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic

TOWARDS A HOLISTIC RISK MODEL FOR SAFEGUARDING THE PHARMACEUTICAL SUPPLY CHAIN: CAPTURING THE HUMAN-INDUCED RISK TO DRUG QUALITY

Counterfeit, adulterated, and misbranded medicines in the pharmaceutical supply chain (PSC) are a critical problem. Regulators charged with safeguarding the supply chain are facing shrinking resources for inspections while concurrently facing increasing demands posed by new drug products being manufactured at more sites in the US and abroad. To mitigate risk, the University of Kentucky (UK) Central Pharmacy Drug Quality Study (DQS) tests injectable drugs dispensed within the UK hospital. Using FT-NIR spectrometry coupled with machine learning techniques the team identifies and flags potentially contaminated drugs for further testing and possible removal from the pharmacy. Teams like the DQS are always working with limited equipment, time, and staffing resources. Scanning every vial immediately before use is infeasible and drugs must be prioritized for analysis. A risk scoring system coupled with batch sampling techniques is currently used in the DQS. However, a risk scoring system only allows the team to know about the risks to the PSC today. It doesn’t let us predict what the risks will be in the future. To begin bridging this gap in predictive modeling capabilities the authors assert that models must incorporate the human element. A sister project to the DQS, the Drug Quality Game (DGC), enables humans and all of their unpredictability to be inserted into a virtual PSC. The DQG approach was adopted as a means of capturing human creativity, imagination, and problem-solving skills. Current methods of prioritizing drug scans rely heavily on drug cost, sole-source status, warning letters, equipment and material specifications. However, humans, not machines, commit fraud. Given that even one defective drug product could have catastrophic consequences this project will improve risk-based modeling by equipping future models to identify and incorporate human-induced risks, expanding the overall landscape of risk-based modeling. This exploratory study tested the following hypotheses (1) a useful game system able to simulate real-life humans and their actions in a pharmaceutical manufacturing process can be designed and deployed, (2) there are variables in the game that are predictive of human-induced risks to the PSC, and (3) the game can identify ways in which bad actors can “game the system” (GTS) to produce counterfeit, adulterated, and misbranded drugs. A commercial-off-the-shelf (COTS) game, BigPharma, was used as the basis of a game system able to simulate the human subjects and their actions in a pharmaceutical manufacturing process. BigPharma was selected as it provides a low-cost, time-efficient virtual environment that captures the major elements of a pharmaceutical business- research, marketing, and manufacturing/processing. Running Big Pharma with a Python shell enables researchers to implement specific GxP-related tasks (Good x Practice, where x=Manufacturing, Clinical, Research, etc.) not provided in the COTS BigPharma game. Results from players\u27 interaction with the Python shell/Big Pharma environment suggest that the game can identify both variables predictive of human-induced risks to the PSC and ways in which bad actors may GTS. For example, company profitability emerged as one variable predictive of successful GTS. Player\u27s unethical in-game techniques matched well with observations seen within the DQS