document: Data mining to predict and prevent errors in health insurance claims processing 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 .
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