Abstract: There are provided systems and methods for rule agnostic reject inferencing. An example method may receive a request for processing a transaction, and determine, using a machine learning model, a classification for the transaction based on data associated with the transaction. The machine learning model may be trained using first training data having verified labels and second training data having inferred labels, and the inferred labels of the second training data may be generated based on a distribution of classifications associated with the first training data. The example method may further process the request based on the classification.

Abstract: There are provided systems and methods for intelligent forecasting with limited data availability utilizing embeddings from auto-encoders and machine learning models. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users. In order to provide actionable insights into users, accounts, and/or activities associated with the service provider, such as to provide computing or other services to users, the service provider may utilize DNNs and other ML models that are trained for forecasting. The models may be trained by encoding vectors from initial training data using an encoder having an embedding, attention, and LSTM layer, which may retain temporal aspects to data for users or groups that have limited past data availability. Once trained, the models may be used to determine risk and/or engagement scores of users, which may predict or forecast users' future actions to offer services to the users.

Abstract: Methods and systems are presented for providing a semi-supervised machine learning framework for training a machine learning model using partly mislabeled training data sets. Using the semi-supervised machine learning framework, an iterative training process is performed on the machine learning model, wherein the training data is being adjusted continuously in each iteration for training the machine learning model. During each iteration, the machine learning model is evaluated based on its ability to identify training data that has been mislabeled. The labeling of identified mislabeled training data is corrected before feeding back to the machine learning model in the next training iteration.