Fast discovery of association rules
Advances in knowledge discovery and data mining
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Detection of Mobile Phone Fraud Using Supervised Neural Networks: A First Prototype
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A Neural Classifier with Fraud Density Map for Effective Credit Card Fraud Detection
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
A Frequent Patterns Tree Approach for Rule Generation with Categorical Septic Shock Patient Data
ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
Choosing k for two-class nearest neighbour classifiers with unbalanced classes
Pattern Recognition Letters
An auctioning reputation system based on anomaly
Proceedings of the 12th ACM conference on Computer and communications security
Back propagation networks for credit card fraud prediction using stratified personalized data
ISP'06 Proceedings of the 5th WSEAS International Conference on Information Security and Privacy
Off-the-peg and bespoke classifiers for fraud detection
Computational Statistics & Data Analysis
Location of trusted email for prevention of credit card fraud in soft-products e-commerce
AIC'04 Proceedings of the 4th WSEAS International Conference on Applied Informatics and Communications
Transaction aggregation as a strategy for credit card fraud detection
Data Mining and Knowledge Discovery
ACM Computing Surveys (CSUR)
Using Bayesian Belief Networks for credit card fraud detection
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
A hybrid model for plastic card fraud detection systems
Expert Systems with Applications: An International Journal
Comprehensive study on methods of fraud prevention in credit card e-payment system
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
An analytic approach to select data mining for business decision
Expert Systems with Applications: An International Journal
Using regression analysis to identify patterns of non-technical losses on power utilities
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Journal of Electronic Testing: Theory and Applications
Data mining for credit card fraud: A comparative study
Decision Support Systems
Anomaly detection in monitoring sensor data for preventive maintenance
Expert Systems with Applications: An International Journal
Novel questionnaire-responded transaction approach with SVM for credit card fraud detection
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Modeling service representatives in enterprise systems using generic agents
Service Oriented Computing and Applications
Expert Systems with Applications: An International Journal
A prescription fraud detection model
Computer Methods and Programs in Biomedicine
Two-Stage credit card fraud detection using sequence alignment
ICISS'06 Proceedings of the Second international conference on Information Systems Security
Expert Systems with Applications: An International Journal
A cost-sensitive decision tree approach for fraud detection
Expert Systems with Applications: An International Journal
Solving credit card fraud detection problem by the new metaheuristics migrating birds optimization
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: Since only one financial transaction of a thousand is invalid no prediction success less than 99.9% is acceptable. Due to these credit card transaction proportions complete new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate.