Outlier analysis for plastic card fraud detection a hybridized and multi-objective approach

Authors:
Arturo Elías;Alberto Ochoa-Zezzatti;Alejandro Padilla;Julio Ponce
Affiliations:
Universidad Autónoma de Aguascalientes;Universidad Autónoma de Ciudad Juárez;Universidad Autónoma de Aguascalientes;Universidad Autónoma de Aguascalientes
Venue:
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Year:
2011

Citing 12
Cited 1

Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
A probabilistic framework for semi-supervised clustering

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
MOPSO: a proposal for multiple objective particle swarm optimization

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Credit Card Fraud Detection Using Hidden Markov Model

IEEE Transactions on Dependable and Secure Computing
Off-the-peg and bespoke classifiers for fraud detection

Computational Statistics & Data Analysis
Association rules applied to credit card fraud detection

Expert Systems with Applications: An International Journal
Transaction aggregation as a strategy for credit card fraud detection

Data Mining and Knowledge Discovery
Anomaly detection: A survey

ACM Computing Surveys (CSUR)
A Framework for Multi-Objective Clustering and Its Application to Co-Location Mining

ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: Overview and progress report

Computers and Operations Research
A unified view on hybrid metaheuristics

HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
Survey: Multi-criterion Pareto based particle swarm optimized polynomial neural network for classification: A review and state-of-the-art

Computer Science Review

Tackling outliers in granular box regression

Information Sciences: an International Journal

Quantified Score

Hi-index	0.04

Visualization

Abstract

Nowadays, plastic card fraud detection is of great importance to financial institutions. This paper presents a proposal for an automated credit card fraud detection system based on the outlier analysis technology. Previous research has established that the use of outlier analysis is one of the best techniques for the detection of fraud in general. However, to establish patterns to identify anomalies, these patterns are learned by the fraudsters and then they change the way to make de fraud. The approach applies a multi-objective model hybridized with particle swarm optimization of typical cardholder's behavior and to analyze the deviation of transactions, thus finding suspicious transactions in a non supervised scheme.