A random sampling of a subset of a data population is taken and the sampled data is used to build a predictive model using a cubic or multiquadric radial basis function, and then "scores" (i.e., predictions) are generated for each data point in the entire data population. This process is repeated on additional random sample subsets of the same data population. After a predetermined number of random sample subsets have been modeled and scores for all data points in the population are generated for each of the models, the average score and variation for each predicted data point is calculated. The data points are subjected to rank ordering by their variance, thereby allowing those data points having a high variance to be identified as outliers.