Abstract: Computer-implemented systems and methods for determining a subset of unknown targets to investigate. For example, a method can be configured to receive a target data set, wherein the target data set includes known targets and unknown targets. A supervised model such as a neural network model is generated using the known targets. The unknown targets are used with the neural network model to generate values for the unknown targets. Analysis with an unsupervised model is performed using the target data set in order to determine which of the unknown targets are outliers. A comparison of list of outlier unknown targets is performed with the values for the unknown targets that were generated by the neural network model. The subset of unknown targets to investigate is determined based upon the comparison.

Abstract: Systems and methods are provided for analyzing disparate treatment in financial transactions. Data processing software instructions may be used to process lending-related data to identify a plurality of primary factors and one or more secondary factors for use making a lending-related decision. Model facilitation software instructions may be used to receive one or more relationships between the primary factors and the one or more secondary factors, wherein the relationships define criteria in which one or more positive secondary factors will compensate for a negative primary factor in making the lending-related decision. Model generation software instructions may be used to analyze lending-related data based on the primary factors, secondary factors and the one or more relationships.