Abstract: The present invention is in the field of Healthcare Claims Fraud Detection. Fraud is perpetrated across multiple healthcare payers. There are few labeled or “tagged” historical fraud examples needed to build “supervised”, traditional fraud models using multiple regression, logistic regression or neural networks. Current technology is to build “Unsupervised Fraud Outlier Detection Models”. Current techniques rely on parametric statistics that are based on assumptions such as outlier free and “normally distributed” data. Even some non-parametric statistics are adversely influenced by non-normality and the presence of outliers. Current technology cannot represent the combined variable values into one meaningful value that reflects the overall risk that this observation is an outlier. The single value, the “score”, must be capable of being measured on the same scale across different segments, such as geographies and specialty groups.