Towards practical `neural' computation for combinatorial optimization problems
AIP Conference Proceedings 151 on Neural Networks for Computing
Toward a secure system engineering methodolgy
Proceedings of the 1998 workshop on New security paradigms
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data Mining and Knowledge Discovery
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
Data Mining and Knowledge Discovery
Minority report in fraud detection: classification of skewed data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Minimum Entropy Clustering and Applications to Gene Expression Analysis
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Feature bagging for outlier detection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A systematic classification of cheating in online games
NetGames '05 Proceedings of 4th ACM SIGCOMM workshop on Network and system support for games
A Scalable and Efficient Outlier Detection Strategy for Categorical Data
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Transaction aggregation as a strategy for credit card fraud detection
Data Mining and Knowledge Discovery
A Classifier Ensemble Approach to Intrusion Detection for Network-Initiated Attacks
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
A fast outlier detection strategy for distributed high-dimensional data sets with mixed attributes
Data Mining and Knowledge Discovery
Coordination of Cluster Ensembles via Exact Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved column generation algorithm for minimum sum-of-squares clustering
Mathematical Programming: Series A and B
A survey of multiple classifier systems as hybrid systems
Information Fusion
| Hi-index | 12.05 |
Fraud detection has been an important topic of research in the data mining community for the past two decades. Supervised, semi-supervised, and unsupervised approaches to fraud detection have been proposed for the telecommunications, credit, insurance and health-care industries. We describe a novel hybrid system for detecting fraud in the highly growing lotteries and online games of chance sector. While the objectives of fraudsters in this sector are not unique, money laundering and insider attack scenarios are much more prevalent in lotteries than in the previously studied sectors. The lack of labeled data for supervised classifier design, user anonymity, and the size of the data-sets are the other key factors differentiating the problem from previous studies, and are the key drivers behind the design and implementation decisions for the system described. The system employs online algorithms that optimally aggregate statistical information from raw data and applies a number of pre-specified checks against known fraud scenarios as well as novel clustering-based algorithms for outlier detection which are then fused together to produce alerts with high detection rates at acceptable false alarm levels.