1) The document discusses using various machine learning and predictive analytics techniques like random forests, support vector machines, neural networks, and network analysis to analyze healthcare claims data and detect anomalies and fraud. 2) Over 87 million claim lines from 17,000 providers and 1.6 million members are analyzed monthly using 70 measures of claiming behavior and 6 algorithms. Small clusters of providers with high-risk claiming patterns are identified. 3) Random forest classification and support vector machines are used to predict which investigations will yield findings, and random forests provide a measure of variable importance. Network analysis can reveal problematic relationships between providers and members.