Fraud Detection in Health Insurance Data using Feature Selection and Outlier Approach |
Author(s): |
| S. Dhivya , PONDICHERRY ENGINEERING COLLEGE; Anbarasi M. S, Pondicherry Engineering College |
Keywords: |
| Fraud detection, Feature selection, Profiling method, Outlier Analysis |
Abstract |
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Fraud and abuse on medical claims became a major issue for health insurance recent organisation. Data mining techniques such as detection of outliers are suggested to be an effective predictors for fraud detection. In this paper, the proposed method utilize the profiling techniques for health insurance to know the highest risk i.e, the highest claim ratio for fraud detection. In addition to increase the accuracy of data, the feature selection is applied for better accuracy followed by outlier. Outlier based predictors are not likely to succeed as fraud classification technology, though it explored an important role as decision supportive technology for resource allocation of fraud. |
Other Details |
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Paper ID: IJSRDV5I21484 Published in: Volume : 5, Issue : 2 Publication Date: 01/05/2017 Page(s): 1845-1848 |
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