Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
A framework for multiple-instance learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Feature selection using linear classifier weights: interaction with classification models
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting changes in large data sets of payment card data: a case study
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Interactive learning for efficiently detecting errors in insurance claims
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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Health insurance costs across the world have increased alarmingly in recent years. A major cause of this increase are payment errors made by the insurance companies while processing claims. These errors often result in extra administrative effort to re-process (or rework) the claim which accounts for up to 30% of the administrative staff in a typical health insurer. We describe a system that helps reduce these errors using machine learning techniques by predicting claims that will need to be reworked, generating explanations to help the auditors correct these claims, and experiment with feature selection, concept drift, and active learning to collect feedback from the auditors to improve over time. We describe our framework, problem formulation, evaluation metrics, and experimental results on claims data from a large US health insurer. We show that our system results in an order of magnitude better precision (hit rate) over existing approaches which is accurate enough to potentially result in over $15-25 million in savings for a typical insurer. We also describe interesting research problems in this domain as well as design choices made to make the system easily deployable across health insurance companies.