1 Health Care Systems and Fraud Overview Health care systems Worldwide health care insurance programs Medical overpayments Why health care fraud? Why now? Impact and importance of fraud assessment Types and examples of health care fraud General fraud assessment framework and initiatives Key takeaways Additional resources 2 Describing Health Care Claims Data Overview Health care data Understanding health care claims data Data pre-processing Descriptive statistical analysis Discussion Key takeaways Additional resources 3 Sampling and Overpayment Estimation Overview Sampling and overpayment estimation Sampling procedures A closer look at stratified sampling Overpayment estimation Discussion Key takeaways Additional resources 4 Predicting Health Care Fraud Overview Health care fraud analytics Predictive methods Prediction of overpayment amount and fraud probability Classification of health care claims Accuracy and validation Discussion Key takeaways Additional resources 5 Discovery of New Fraud Patterns Overview Outlier detection: Finding excessive billings Clustering: Grouping health care claims Association: Finding links among claims Effectiveness of the analytical methods Deployment via rules Current efforts Key takeaways Additional resources 6 Challenges, Opportunities and Future Directions Overview Shareholders: putting a face on fraudsters and victims Challenges with payment and fraud control systems Organizational issues: "No news is good news!" Evolution of fraud and adaptive fraudsters Different sides of the coin: Data as a blessing, data as a curse Legal concerns: Embracing uncertainty A take on future Key takeaways Additional resources Bibliography Index amp;lt;/P> 3 Sampling and Overpayment Estimation Overview Sampling and overpayment estimation Sampling procedures A closer look at stratified sampling Overpayment estimation Discussion Key takeaways Additional resources 4 Predicting Health Care Fraud Overview Health care fraud analytics Predictive methods Prediction of overpayment amount and fraud probability Classification of health care claims Accuracy and validation Discussion Key takeaways Additional resources 5 Discovery of New Fraud Patterns Overview Outlier detection: Finding excessive billings Clustering: Grouping health care claims Association: Finding links among claims Effectiveness of the analytical methods Deployment via rules Current efforts Key takeaways Additional resources 6 Challenges, Opportunities and Future Directions Overview Shareholders: putting a face on fraudsters and victims Challenges with payment and fraud control systems Organizational issues: "No news is good news!" Evolution of fraud and adaptive fraudsters Different sides of the coin: Data as a blessing, data as a curse Legal concerns: Embracing uncertainty A take on future Key takeaways Additional resources Bibliography Index ud Patterns Overview Outlier detection: Finding excessive billings Clustering: Grouping health care claims Association: Finding links among claims Effectiveness of the analytical methods Deployment via rules Current efforts Key takeaways Additional resources 6 Challenges, Opportunities and Future Directions Overview Shareholders: putting a face on fraudsters and victims Challenges with payment and fraud control systems Organizational issues: "No news is good news!" Evolution of fraud and adaptive fraudsters Different sides of the coin: Data as a blessing, data as a curse Legal concerns: Embracing uncertainty A take on future Key takeaways Additional resources Bibliography Index y A take on future Key takeaways Additional resources Bibliography Index.