Abstract: Systems and methods for validation of transaction amounts with a predictive model are disclosed. An example method may be performed by one or more processors of a system and include retrieving data indicating attributes for each of a plurality of transactions, assigning a label to each of the transactions based on whether an original amount entered changed, defining predictive features suggesting an extent to which final amounts stored for a particular set of similar transactions tend to vary, defining one or more interaction features suggesting a probability of a particular predictive feature value being generated for a transaction having particular attributes, generating, using a machine learning process, an anomaly scoring algorithm based on the predictive features and the one or more interaction features, and training, using the labeled transactions, a predictive model to predict, using the anomaly scoring algorithm, whether an amount entered for a given transaction will be changed.

Abstract: Systems and methods for predicting a future status of an entity using a recurrent neural network are described. A system is configured to obtain a first time series data associated with a first entity. The system is also configured to predict, by a recurrent neural network, a future equity status of the first entity based on the time series data. The system is also configured to provide an indication of the future equity status of the first entity, with the future equity status of the first entity to be indicated to a user. An equity status may be a cash status of an entity, and predicting the cash status of the first entity can be based on time series of a plurality of features associated with the first entity provided to the recurrent neural network. The recurrent neural network may be a long short-term memory network.