ISS 2014 Abstracts

Full Papers
Paper Nr: 6

Web-based Demonstration of Semantic Similarity Detection Using Citation Pattern Visualization for a Cross Language Plagiarism Case


Bela Gipp, Norman Meuschke, Corinna Breitinger, Jim Pitman and Andreas Nürnberger

Abstract: In a previous paper, we showed that analyzing citation patterns in the well-known plagiarized thesis by K. T. zu Guttenberg clearly outperformed current detection methods in identifying cross-language plagiarism. However, the experiment was a proof of concept and we did not provide a prototype. This paper presents a fully functional, web-based visualization of citation patterns for this verified cross-language plagiarism case, allowing the user to interactively experience the benefits of citation pattern analysis for plagiarism detection. Using examples from the Guttenberg plagiarism case, we demonstrate that the citation pattern visualization reduces the required examiner effort to verify the extent of plagiarism.

Paper Nr: 7

Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data


Dallas Thornton, Guido van Capelleveen, Mannes Poel, Jos van Hillegersberg and Roland M. Mueller

Abstract: Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier detection are suggested as effective predictors for fraud. Based on a multi-dimensional data model developed for Medicaid claim data, specific metrics for dental providers were developed and evaluated in analytical experiments using outlier detection applied to claim, provider, and patient data in a state Medicaid program. The proposed methodology enabled successful identification of fraudulent activity, with 12 of the top 17 suspicious providers (71%) referred to officials for investigation with clearly anomalous and inappropriate activity. Future research is underway to extend the method to other specialties and enable its use by fraud analysts.

Short Papers
Paper Nr: 5

Finding You on the Internet - An Approach for Finding On-line Presences of People for Fraud Risk Analysis


Henry Been and Maurice van Keulen

Abstract: Fraud risk analysis on data from formal information sources, being a ‘paper reality’, suffers from blindness to false information. Moreover, the very act of providing false information is a strong indicator for fraud. The technology presented in this paper provides one step towards the vision of harnessing real-world data from social media and internet for fraud risk analysis. We introduce a novel iterative search, monitor, and match approach for finding on-line presences of people. A real-world experiment showed that Twitter accounts can be effectively found given only limited name and address data. We also present an analysis of the ethical considerations surrounding the application of such technology for fraud risk analysis.

Paper Nr: 8

Auditing Data Reliability in International Logistics - An Application of Bayesian Networks


Lingzhe Liu, Hennie Daniels and Ron Triepels

Abstract: Data reliability closely relates to the risk management in international logistics. Unreliable data negatively affect the business in various ways. Due to the competence specialization and cooperation among the business partners in a logistics chain, the business in a focal company is inevitably dependent on external data sources from its partner, which is impractical to control. In this paper, we present a research-in-progress on an analysis method with Bayesian networks. The goal is to support auditor’s assessment on the reliability of the external data. A case study is provided to illustrate the merits of Bayesian networks when dealing with the data reliability problem.