Fraud Risk Evaluation of the Social Security Organizations' Unemployment Insurance Benefits

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GOUDARZI ATOUSA; Tabatabaee Manesh Seyyed Javad

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

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Paper Information

Journal: IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH) Year:2016 | Volume:31 | Issue:3 (123) Page(s): 89-110

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Title

Fraud Risk Evaluation of the Social Security Organizations' Unemployment Insurance Benefits

Author(s)

GOUDARZI ATOUSA | Tabatabaee Manesh Seyyed Javad | Issue Writer Certificate

Keywords

Fraud RiskQ1

Unemployment InsuranceQ1

Data MiningQ2

Neural NetworksQ2

decision treeQ2

social securityQ1

Abstract

Unemployment Insurance (UI) fraud is an important topic in social insurances which is regarded by law as a penal offense. The best way for fraud evaluation is to prevent and control it at early steps, using previous unfolded fraud documents. In this paper, first of all, the fraud control diagram is depicted, then according to the existence of an appropriate database of social security of UI claimants, two modern Data Mining techniques have been used for data analysis. The Neural Networks and decision tree algorithms create models which have been used for evaluating UI benefits fraud. These obtained models will provide an appropriate opportunity for social insurance authorities to have better conceptual understanding and effective combat against all kinds of insurance fraud, clearly just on time. According to several empirical studies, two obtained models have been applied on real data of 15983 records including new and current UI claimants. The accuracy and efficiency of each model have precisely examined. Indicated results show that the Neural Networks model take the first place by 88% of accuracy and the second place is for decision tree model by 84% of accuracy. The most important predictors which build each model for fraud pattern recognition, consist of the insured’ s previous occupation, paid premium record and age in Neural Networks and branch’ s geographic location, gender and the numbers of sponsorships in decision tree.

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APA: Copy

GOUDARZI, ATOUSA, & Tabatabaee Manesh, Seyyed Javad. (2016). Fraud Risk Evaluation of the Social Security Organizations' Unemployment Insurance Benefits. IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH), 31(3 (123) ), 89-110. SID. https://sid.ir/paper/100819/en

Vancouver: Copy

GOUDARZI ATOUSA, Tabatabaee Manesh Seyyed Javad. Fraud Risk Evaluation of the Social Security Organizations' Unemployment Insurance Benefits. IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH)[Internet]. 2016;31(3 (123) ):89-110. Available from: https://sid.ir/paper/100819/en

IEEE: Copy

ATOUSA GOUDARZI, and Seyyed Javad Tabatabaee Manesh, “Fraud Risk Evaluation of the Social Security Organizations' Unemployment Insurance Benefits,” IRANIAN JOURNAL OF INSURANCE RESEARCH (SANAAT-E-BIMEH), vol. 31, no. 3 (123) , pp. 89–110, 2016, [Online]. Available: https://sid.ir/paper/100819/en

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