Insurance Fraud Detection Using Novel Machine Learning Technique

Authors

  • M. Sathya Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India
  • B. Balakumar Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India

Keywords:

Insurance Fraud, Block chain technique, RF classifier, SVM classifier, Hybrid ERFSVM classifier

Abstract

The ultimate focus of the insurance agency is the management of financial risks, since it includes a sizable amount of daily transactions. It is mandatory for the insurance industry to develop the capability of identifying the fraudulent activities with increased accuracy in order to minimize the excessive unwarranted loss due to claim leakage. Initially, many fraud detecting techniques has been introduced and it relies on the heuristics around fraud indicators, also on the checklist prepared about frauds. However, the above mentioned traditional techniques highly relies on the manual intervention. Therefore, for eliminating the above mentioned issues, the insurance companies are looking towards machine learning based fraud detection techniques. In this paper, initially a block chain technique is presented as a secure network by the insurance agencies to detect, store and share different customer informations. Then, a hybrid classifier called eRFSVM, which entails Random Forest (RF) and Support Vector Machine (SVM) algorithm is used for classifying the insurance frauds. Additionally, to analyse the working of the proposed work, it is implemented in Python software.

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Benefits of blockchain in insurance sector

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Published

01.10.2022

How to Cite

Sathya, M. ., & Balakumar , B. . (2022). Insurance Fraud Detection Using Novel Machine Learning Technique. International Journal of Intelligent Systems and Applications in Engineering, 10(3), 374–381. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2178

Issue

Vol. 10 No. 3 (2022)

Section

Research Article

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