Techniques for improving the accuracy of automated predictions

Abstract

Techniques are provided for forming clusters of individual prediction targets (IPTs). An initial prediction target is a target for which an automated prediction has been generated. IPTs may be, for example, borrowers to which a lending entity has extended loans based on predictions generated by a credit policy. Each cluster includes (a) a 'core' of underperforming entities (UEs), and (b) a set of boundary performant entities (PEs). The UEs that belong to the UE core of a cluster are “similarly situated” relative to the values of their features. For example, in the context where the IPTs are borrowers, the UEs at the core of a cluster may correspond to defaulting borrowers that had similar bureau data, lending entity data, and borrower data. The boundary performant entities of the cluster may be borrowers that have not defaulted, but had similar credit qualifications as the UEs of the cluster. Having formed these clusters, the clusters may be used in a variety of ways, including but not limited to improving the accuracy of the credit model, identifying potentially problematic future borrowers, generating visualizations that illustrate the relative importance of clusters of defaulting borrowers, etc.