Fraud Analytics Using Data Mining

Mukherjee, Sreeparna; Mukherjee, Triparna; Nath, Asoke; Bolton, R; Hand, D; Barson, P; Field, S; Davey, N; Mcaskie, G; Frank, R; Fanning, K; Cogger, K; Green, B; Choi, J; Lin, J; Hwang, M; Becker, J; Estevez, P; Held, C; Perez, C; Cooper, C; Hall, C; He, H; Wang, J; Graco, W; Hawkins, S; Ortega, P; Figueroa, C; Ruz, G; Shapiro, A; Bonchi, F; Giannotti, F; Mainetto, G; Pedreschi, D; Williams, G; Huang, Z; Viveros, M; Nearhos, J; Rothman, M; Ormerod, T; Morley, N; Ball, L; Langley, C; Spenser, C; He, H; Hawkins, S; Graco, W; Yao, X; Williams, G; Viaene, S

doi:10.20431/2349-4859.0304001

Public

Fraud Analytics Using Data Mining

Published in International journal of research studies in computer science and engineering • Jan 1, 2016

NobleIDNI3P45W23R78S88

Authors:

Sreeparna Mukherjee

,

Triparna Mukherjee

,

Asoke Nath

Abstract

This paper deals with the data mining techniques used to combat fraud in different industries. The main challenges in fraud analytics have been addressed and the type of fraudsters involved have been studied in the light of different situations. The two main types of data mining methods i.e. supervi...

Subject

Analytics

Computer science

Data science

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