Naïve Classification Approach for Insurance Fraud Prediction
Bhavna Batra1, Sheetal Kundra2
1Bhavna Batra, ME. Student, Department of CSE., Chandigarh University, Ajitgarh (Punjab), India.
2Sheetal Kundra, Associate Professor, AIT, Chandigarh University, Ajitgarh (Punjab), India.
Manuscript received on 18 June 2019 | Revised Manuscript received on 25 June 2019 | Manuscript published on 30 June 2019 | PP: 2378-2382 | Volume-8 Issue-5, June 2019 | Retrieval Number: E7551068519/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: An approach which can be used for the prediction of future potentials on the basis of present information is known as prediction analysis. This study is relied on the fraudulent discovery in the insurance business. A number of approaches have been projected up to now for the fraudulent discovery in insurance sector. These approaches mainly rely on machine learning algorithms. The insurance fraud detection is the major issue of prediction analysis. The insurance fraud detection has three phases which are pre-processing, feature extraction and classification. The naïve bayes classification approach is proposed in this work for the insurance fraud prediction. The proposed algorithm is implemented in python and results are analyzed in terms of accuracy, execution time.
Keywords: Insurance Fraud Detection, Voting Method, Naïve Bayes.
Scope of the Article: Classification