Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Monte Carlo matrix inversion policy evaluation
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Detecting fraud in health insurance data: learning to model incomplete benford's law distributions
ECML'05 Proceedings of the 16th European conference on Machine Learning
Debt Detection in Social Security by Adaptive Sequence Classification
KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
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Adaptive Benford's Law [1] is a digital analysis technique that specifies the probabilistic distribution of digits for many commonly occurring phenomena, even for incomplete data records. We combine this digital analysis technique with a reinforcement learning technique to create a new fraud discovery approach. When applied to records of naturally occurring phenomena, our adaptive fraud detection method uses deviations from the expected Benford's Law distributions as an indicators of anomalous behaviour that are strong indicators of fraud. Through the exploration component of our reinforcement learning method we search for the underlying attributes producing the anomalous behaviour. In a blind test of our approach, using real health and auto insurance data, our Adaptive Fraud Detection method successfully identified actual fraudsters among the test data.