Abstract: Systems and methods that facilitate determining and predicting fraudulent or non-compliant behavior using poly-structured data analytics are discussed. An unstructured data steam comprising a set of emails can be received, and processed to reduce the noise, or non-relevant portions of the dataset. Structured data that includes contextual relevant information about the users can also be received, and the poly-structured data modeling system can identify unstructured variable and structured variables that are relevant for identifying and predicting non-compliant behavior. These unstructured and structured variables can then be modeled in order to identify communications that may contain non-compliant and/or fraudulent behavior.