iWebCare: An ontological approach for fraud detection in the healthcare domain (original) (raw)
Related papers
2009 9th International Conference on Information Technology and Applications in Biomedicine, 2009
Public healthcare is a basic service provided by governments to citizens which is increasingly coming under pressure as the European population ages and the ratio of working to elderly persons falls. A way to make public spending on healthcare more efficient is to ensure that the money is spent on legitimate causes. This paper presents the work of the iWebCare project where a flexible, on-line, fraud detection, web services platform was designed and developed. It aims to help those in the Healthcare business, minimize the loss of funds to fraud. The Platform is able to detect erroneous or suspicious records in submitted health care data sets, ensuring homogeneity and consistency and promoting awareness and harmonization of fraud detection practices across health care systems in the EU. Critical objectives included, the development of an ontology of health care data associated with semantic rules, implementation and initial population of an ontology and rules repository, development of a fraud detection engine and implementation of a data mining module. The potential impact of this work can be substantial. More money on healthcare mean better healthcare. Living conditions and the trust of citizens in public healthcare will be improved.
Towards a generic fraud ontology in e-government
2008
Abstract: Fraud detection and prevention systems are based on various technological paradigms but the two prevailing approaches are rule-based reasoning and data mining. In this paper we claim that ontologies, an increasingly popular and widely accepted knowledge representation paradigm, can help both of these approaches be more efficient as far as fraud detection is concerned and we introduce a methodology for building domain specific fraud ontologies in the e-government domain.
Towards Ontology-based E-mail Fraud Detection
… , 2005. epia 2005. …, 2005
The European FF POIROT project (IST-2001-38248) aims at developing applications for tackling financial fraud, using formal ontological repositories as well as multilingual terminological resources. In this article, we want to focus on the development cycle towards an application recognizing several types of e-mail fraud, such as phishing, Nigerian advance fee fraud and lottery scam. The development cycle covers four tracks of development -language engineering, terminology engineering, knowledge engineering and system engineering. These development tracks are preceded by a problem determination phase and followed by a deployment phase. Each development track is supported by a methodology. All methodologies and phases in the development cycle will be discussed in detail.
Elaboration of Financial Fraud Ontology
Communication Papers of the 17th Conference on Computer Science and Intelligence Systems
Financial Frauds have dynamically changed, the fraudsters are becoming more sophisticated. There has been an estimated global loss of 5.127 trillion dollars each year due to various forms of financial frauds. Industries like banking, insurance, e-commerce and telecommunication are the main targets for financial frauds. Several techniques have been proposed and applied to understand and detect financial frauds. In this paper we propose an ontology to describe financial frauds and related knowledge. The aim of this ontology is to provide a semantic framework for the detection of financial frauds. Theoretical ontology has been elaborated exploring various sources of information. After describing the research objectives, related works and research methodology, this paper presents details of theoretical ontology. It is followed by its validation using real datasets. Discussion of the obtained results gives some perspectives for the future work.
A Model System to Identify Health Care Frauds
As the human life march towards the modern amenities all the sectors of life become more and more advanced, Health care is not spared from this. The revolutionary health care policy concept eventually facilitates all the patients irrespective of any cast and creed to avail the best services of the doctors for their diseases. Many of the health care insurance companies are existed to provide this facility for the peoples, but all of them are suffer from the headache of fraud insurance claims from the doctors. Many systems are existed to deal with these kinds of fraud health insurance claims from the doctors, but most of them are not up to the mark to identify the proper fraud detection operandi. So as a tiny step towards this the proposed system develops a web application panel for both the doctors and insurance companies to identify the fraud claims of the doctors at insurance company’s end using Hidden markov model which is powered with fuzzy classification.
Towards a Financial Fraud Ontology: A Legal Modelling Approach
Artificial Intelligence and Law, 2004
This document discusses the status of research on detection and prevention of financial fraud undertaken as part of the European Commission funded FF POIROT ( financial fraud prevention oriented information resources using ontology technology) project. A first task has been the specification of the user requirements that define the functionality of the financial fraud ontology to be designed by the FF POIROT partners. It is claimed here that modeling fraudulent activity involves a mixture of law and facts as well as inferences about facts present, facts presumed or facts missing. The purpose of this paper is to explain this abstract model and to specify the set of user requirements.
Applied Sciences, 2020
This paper presents a novel framework for fraud detection in healthcare systems which self-learns from the historical medical data. Historical medical records are required for training and testing of machine learning models. The main problem being faced by both private and government health supported schemes is a rapid rise in the amount of claims by beneficiaries mostly based on fraudulent billing. Detection of fraudulent transactions in healthcare systems is a strenuous task due to intricate relationships among dynamic elements including doctors, patients, service. In light of aforementioned challenges in health support programs, there is a need to develop intelligent fraud detection models for tracing the loopholes in procedures which may lead to successful reimbursement of fraudulent medical bills. In order to address the issue of fraud in healthcare programs our solution proposes a framework based on three entities (patient, doctor, service). Firstly, the framework computes ass...
International Journal of Organizational and Collective Intelligence, 2017
Using an interpretive case study approach, this paper describes the data quality problems in a regional health insurance (RHI) company. Within this company, two interpretive cases examine different processes of the healthcare supply chain and their integration with a business intelligence system. Specifically, the first case examines RHI's provider enrollment and credentialing process, and the second case examines the processes within the special investigations unit (SIU) for investigating and detecting fraud. The second case examines DIQ issues and how social media can be used to acquire evidence to support a fraud case. In addition, the second case utilized lean six sigma to streamline internal processes. A data and information quality (DIQ) assessment of these processes demonstrates how a framework, referred to as PGOT, can identify improvement opportunities within any information intensive environment. This paper provides recommendations for DIQ and social media best practic...
Fraud detection in healthcare billing and claims
International Journal of Science and Research Archive, 2024
Healthcare fraud in billing and claims is a pervasive issue, costing the United States healthcare system billions of dollars annually. This paper provides a comprehensive exploration of how advanced technologies such as data analytics, machine learning, and anomaly detection can effectively combat fraudulent practices, including upcoding, phantom billing, and duplicate claims. By leveraging healthcare claims data, predictive models are developed to detect suspicious patterns in real time, assign risk scores to providers, and flag high-risk claims for further investigation. This approach enhances fraud detection accuracy, minimizes false positives, and enables efficient resource allocation for fraud mitigation. The proposed solutions include AI-powered fraud detection tools, automated alert systems, and continuous model training to adapt to evolving fraud tactics. These tools, when integrated into the claims processing workflow, facilitate proactive fraud prevention by identifying anomalies and streamlining investigation processes. Operational measures such as provider risk scoring and robust data-sharing frameworks complement these technical innovations, creating a multi-layered defense strategy. Additionally, the paper emphasizes the importance of policy initiatives, including enhanced staff training and inter-organizational collaboration, to foster a culture of vigilance and compliance. By combining cutting-edge technology with sound operational practices, this research aims to significantly reduce healthcare fraud, enhance system efficiency, and rebuild trust within the healthcare industry. The insights and recommendations presented in this study have broad implications for policymakers, healthcare providers, and insurers seeking to implement cost-effective and scalable fraud prevention strategies.
Applying Semantic Technologies to Fight Online Banking Fraud
2015
Cybercrime tackling is a major challenge for Law Enforcement Agencies (LEAs). Traditional digital forensics and investigation procedures are not coping with the sheer amount of data to analyse, which is stored in multiple devices seized from distinct, possibly-related cases. Moreover, inefficient information representation and exchange hampers evidence recovery and relationship discovery. Aiming at a better balance between human reasoning skills and computer processing capabilities, this paper discusses how semantic technologies could make cybercrime investigation more efficient. It takes the example of online banking fraud to propose an ontology aimed at mapping criminal organisations and identifying malware developers. Although still on early stage of development, it reviews concepts to extend from well-established ontologies and proposes novel abstractions that could enhance relationship discovery. Finally, it suggests inference rules based on empirical knowledge which could better address the needs of the human analyst.