Insurance Approval Analysis using Machine Learning Algorithms

Authors

  • CH. Lakshman Vinay Dept. of Computer Science and Engineering, Gayatri Vidya Parishad College for Degree and PG Courses, Visakhapatnam, India
  • G. Vijay Sagar Dept. of Computer Science and Engineering, Gayatri Vidya Parishad College for Degree and PG Courses, Visakhapatnam, India
  • M. Ajay Dept. of Computer Science and Engineering, Gayatri Vidya Parishad College for Degree and PG Courses, Visakhapatnam, India
  • SK. Hussain Dept. of Computer Science and Engineering, Gayatri Vidya Parishad College for Degree and PG Courses, Visakhapatnam, India
  • Bh Padma Dept. of Computer Science and Engineering, Gayatri Vidya Parishad College for Degree and PG Courses, Visakhapatnam, India

Keywords:

Insurance, Machine Learning, Decision Tree Induction

Abstract

Risk Management is important for insurance industry to ensure the eligibility of a new customer for approval. Insurance companies need to analyze the existing customer’s information such as income, assets, occupation, premium payment records to decide whether a new customer is qualified for an insurance policy. This paper focuses on forecasting the eligibility of the new customers for insurance approval by performing classification on a real time insurance company dataset using three Machine Learning algorithms such as Decision Tree Induction, Naive Bayes Classification and K- Nearest Neighbor algorithms. These algorithms are examined against their classifier accuracy after implementation and the algorithm that demonstrates the best accuracy is chosen for predicting the new customer.

References

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Published

2020-12-31

How to Cite

[1]
C. L. Vinay, G. V. Sagar, M. Ajay, S. Hussain, and B. Padma, “Insurance Approval Analysis using Machine Learning Algorithms”, Int. J. Comp. Sci. Eng., vol. 8, no. 12, pp. 46–50, Dec. 2020.

Issue

Vol. 8 No. 12 (2020): IJCSE December Edition

Section

Survey Article

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