Fraud detection in health insurance using data mining techniques

Rawte, Vipula; Anuradha, G.

doi:10.1109/iccict.2015.7045689

Cited by 56 publications

(30 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, because each of the learning techniques above has its advantages and disadvantages, a new hybrid approach to identify fraudulent claims in the health insurance industry had presented via combining the benefits of both methods. (18) Considering the advantages and disadvantages of the classification and clustering techniques, ECM was chosen by the authors as the clustering method because the data flows in continuously, and the ability to cluster dynamic data and the SVM as classification method since it provides the scalability and usability.…”

Section: Public Healthmentioning

confidence: 99%

An Application-Based Review of Recent Advances of Data Mining in Healthcare

Shirazi¹,

Baziyad²,

Karimi³

2020

jbe

View full text Add to dashboard Cite

Background: Data mining as an integral part of the knowledge discovery in database (KDD) has gained significant attention over the past few years. By and large, data mining is the process of finding interesting structures in a considerably voluminous amount of data. Owing to its methods and algorithms supporting variable types of data, the data mining approach has been applied in many scientific areas, including the healthcare industry. Regarding this matter, in this paper, we elaborate on the latest papers, including data miningtechniques and algorithms in the healthcare field of research. Results: We present a data mining review based on the newest researches. Afterward, we categorize data mining papers in healthcare based on supervised and unsupervised learning paradigms as well as classifying them in terms of their applications in the healthcare domain. Conclusions: In every healthcare application, we propose some summary points of the papers. At last, we delve into the absence and hence, the necessity of existing some novel methods in healthcare domains in this researches.

show abstract

Section: Public Healthmentioning

confidence: 99%

An Application-Based Review of Recent Advances of Data Mining in Healthcare

Shirazi¹,

Baziyad²,

Karimi³

2020

jbe

View full text Add to dashboard Cite

show abstract

“…However, fraud and abuse refer to a situation where healthcare service is paid for but not provided or reimbursement of funds is made to third-party insurance companies. Fraud and abuse are further explained as healthcare providers receiving kickbacks, patients seeking treatments that are potentially harmful to them (such as seeking drugs to satisfy addictions), and the prescription of services known to be unnecessary [12,[17][18][19]. Health insurance fraud is an intentional act of deceiving, concealing, or misrepresenting information that results in healthcare benefits being paid to an individual or group.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Several types of fraud schemes form the basis of this problem in health insurance programs worldwide. ese are (1) billing for services not rendered (identity theft and phantom billing), (2) upcoding of services and items (upcoding), (3) duplicate billing, (4) unbundling of claims (unbundling/creative billing), (5) medically unnecessary services (bill padding), (6) excessive services (bill padding), (7) kickbacks, (8) impersonation, (9) ganging, (10) illegal cash exchange for prescription, (11) frivolous use of service, (12) insurance carriers' fraud, (13) falsifying reimbursement, (14) upcoding of service, and (14) insurance subscribers' fraud, among others [9,[13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs)

Sowah

Kuuboore

Ofoli

et al. 2019

Journal of Engineering

View full text Add to dashboard Cite

Fraud in health insurance claims has become a significant problem whose rampant growth has deeply affected the global delivery of health services. In addition to financial losses incurred, patients who genuinely need medical care suffer because service providers are not paid on time as a result of delays in the manual vetting of their claims and are therefore unwilling to continue offering their services. Health insurance claims fraud is committed through service providers, insurance subscribers, and insurance companies. The need for the development of a decision support system (DSS) for accurate, automated claim processing to offset the attendant challenges faced by the National Health Insurance Scheme cannot be overstated. This paper utilized the National Health Insurance Scheme claims dataset obtained from hospitals in Ghana for detecting health insurance fraud and other anomalies. Genetic support vector machines (GSVMs), a novel hybridized data mining and statistical machine learning tool, which provide a set of sophisticated algorithms for the automatic detection of fraudulent claims in these health insurance databases are used. The experimental results have proven that the GSVM possessed better detection and classification performance when applied using SVM kernel classifiers. Three GSVM classifiers were evaluated and their results compared. Experimental results show a significant reduction in computational time on claims processing while increasing classification accuracy via the various SVM classifiers (linear (80.67%), polynomial (81.22%), and radial basis function (RBF) kernel (87.91%).

show abstract

“…The system is trained to determine a decision boundary between classes of "legitimate" and "fraudulent" claims. Then each claim is compared with that decision boundary and is placed into either legitimate or fraudulent class [17].…”

Section: B Classification Using Support Vector Machine (Svm)mentioning

confidence: 99%

Classification of Firm External Audit Using Ensemble Support Vector Machine Method

Dewiani¹,

Lawi²,

Sarro³

et al. 2019

Proceedings of the 1st International Conference on Science and Technology, ICOST 2019, 2-3 May, Makassar, Indonesia

View full text Add to dashboard Cite

Financial fraud is an important problem because it can detrimental firm in the modern business world. An audit is carried out to prevent and be responsible for detecting fraud. External audit is one of the audit practices conducted outside of the firm internal audit by visiting firms in carrying out the work of financial report audit data. The application of machine learning can be used as a solution in the use of data analysis methods needed to solve these problems. This study proposes a Support Vector Machine (SVM) method by combining the Ensemble Bagging model to improve single classification performance. Data comes from 14 different corporate sectors with 777 records. The results showed that the Ensemble Bagging model could improve the accuracy of classification performance from the Support Vector Machine (SVM) method and achieved the highest accuracy of 89.95%. Based on the results of the accuracy obtained, the Support Vector Machine (SVM) method with the Ensemble Bagging model can be used to detect fraud in the firm.

show abstract

Fraud detection in health insurance using data mining techniques

Cited by 56 publications

References 2 publications

An Application-Based Review of Recent Advances of Data Mining in Healthcare

An Application-Based Review of Recent Advances of Data Mining in Healthcare

Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs)

Classification of Firm External Audit Using Ensemble Support Vector Machine Method

Contact Info

Product

Resources

About