Earticle

초록

영어

In fraud detection paradigms, the role of predictor variables cannot be overemphasized particularly when analytical tools – such as statistical, machine learning and artificial intelligent tools are employed. These variables or attributes are used to organize records of data in database tables. The combination of the values of these attributes usually affects which class of a target variable a record or an observation would belong. In this paper, we propose an algorithm and employ spreadsheet ‘count’ ‘count if’ and ‘filtering’ functionalities (techniques) to take toll on how the individual attribute may affect the prediction of the class of an observation in an insurance dataset of 5000 observations. The analysis showed that indeed, the individual predictor attribute affects the outcome of the target variable (legal or fraudulent) differently.

목차

Abstract
 1. Introduction
 2. Insurance Fraud Review
 3. Materials and Methods
  3.1. Data Set
  3.2. The Spreadsheet Function
  3.3. The Proposed Method
 4. Result and Analysis
  4.1. Ticket Predictor Attribute
  4.2. Sex Predictor Attribute
  4.3. Sex Previous Claims Attribute
  4.4. Attorney Predictor Attribute
 5. Conclusion and Recommendation
 References

키워드

저자

• Saliu Adam Muhammad [ Department of Computer Science and Technology, School of Information Science and Electronic Engineering, Hunan University, Changsha, Hunan Province, 410082, P.R. China ]
• Xiangtao Chen [ Department of Computer Science and Technology, School of Information Science and Electronic Engineering, Hunan University, Changsha, Hunan Province, 410082, P.R. China ]
• Liao Bo [ Department of Computer Science and Technology, School of Information Science and Electronic Engineering, Hunan University, Changsha, Hunan Province, 410082, P.R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605