1. A system for rental fraud detection, comprising: an input interface configured to: receive a set of training data, wherein the set of training data comprises a set of rental fraud indications; and receive a set of prediction data; a feature vector extractor configured to: extract a first training set of feature vectors from the set of training data, wherein the set of training data comprises real estate listings determined to be not fraudulent and real estate listings determined to be fraudulent; and extract a first test feature vector from the set of prediction data, wherein the set of prediction data comprises one or more real estate listings of undetermined fraudulence; a model builder configured to: build a first model using the first training set of feature vectors and the rental fraud indications; and a predictor configured to: predict whether the first test feature vector corresponds to fraudulent data using the first model.
1. A system for rental fraud detection, comprising: an input interface configured to: receive a set of training data, wherein the set of training data comprises a set of rental fraud indications; and receive a set of prediction data; a feature vector extractor configured to: extract a first training set of feature vectors from the set of training data, wherein the set of training data comprises real estate listings determined to be not fraudulent and real estate listings determined to be fraudulent; and extract a first test feature vector from the set of prediction data, wherein the set of prediction data comprises one or more real estate listings of undetermined fraudulence; a model builder configured to: build a first model using the first training set of feature vectors and the rental fraud indications; and a predictor configured to: predict whether the first test feature vector corresponds to fraudulent data using the first model. 2. The system of claim 1 , wherein the feature vector extractor is further configured to: extract a second training set of feature vectors from the set of training data; and extract a second test feature vector from the set of prediction data.