Abstract: A computer system is configured to receive a dataset that includes a plurality of transaction request records and is divisible into a plurality of segments. Each transaction request record includes an individual score calculated by a machine learning algorithm. The computer system also receives a plurality of constraints. The computer system is configured to calculate, using a linear programming algorithm, a decision threshold score for a particular segment of the plurality of segments using the transaction request records. The computer system is configured to provides access to the calculated decision threshold score to a production computer system. The production computer system is configured to use the decision threshold score to evaluate a subsequent transaction request corresponding to the particular segment.

Abstract: Techniques are disclosed relating to improving machine learning classification using both labeled and unlabeled data, including electronic transactions. A computing system may train a machine learning module using a first set of transactions (of any classifiable data) with label information that indicates designated classifications for those transactions and a second set of transactions without label information. This can allow for improved classification error rates, particularly when additional labeled data may not be present (e.g., if a transaction was disallowed, it may not be later labeled as fraudulent or not). The training process may include generating first error data based on classification results for the first set of transactions, generating second error data based on reconstruction results for both the first and second sets of transactions, and updating the machine learning module based on the first and second error data.

Abstract: There are provided systems and methods for an attention mechanism and dataset bagging for time series forecasting using deep neural network models. A service provider, such as an electronic transaction processor for digital transactions, may provide computing services to users. In order to provide time series forecasting for users, accounts, and/or activities associated with the service provider, the service provider may provide time series forecasting where future predictive forecasts of a variable are performed at future timesteps. The time series forecasting may be optimized for deep neural networks using data bagging, where multiple subsets of training data are used to train multiple models for ensemble learning. Further, an attention mechanism may be used to focus on specific past timesteps of relevance, such as those timesteps that correspond to the forecasted timestep. External features may be used to provide forecasting based on external data relevant to the forecasted timestep.

Abstract: There are provided systems and methods for clustering data vectors based on deep neural network embeddings. 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, the service provider may provide clustering of deep embeddings from an embedding layer of a deep neural network model. The clustering may be improved to handle and utilize temporal data, such as time sensitive and/or changing data, using a long short-term memory model with sequential data. The embedding layer may be trained and used for embedding generation using a distribution-wise objective function and a silhouette score to determine cluster membership, cluster loss, and the number of clusters. Once trained, data records may be clustered and relationships between different data records may be identified for taking next-best-actions.

Abstract: Users interact with a computer system, which collects data about individual interactions the users have had with the computer system. The users are sorted into one of a first group or a second group. The computer system generates respective user sequence models for the users using information representing the individual interactions. The computer system analyzes the respective user sequence models using a recurrent neural network with an attention mechanism, which produces respective vectors corresponding to the user sequence models. Individual values in the vectors represent respective individual interactions by a given user and correspond to an amount of correlation between the respective individual interactions and the sorting of the given user into the first group or the second group. The computer system identifies a particular type of interaction that is correlated to users being sorted into the first group by analyzing the respective vectors.

Abstract: Users interact with a computer system, which collects data about individual interactions the users have had with the computer system. The users are sorted into one of a first group or a second group. The computer system generates respective user sequence models for the users using information representing the individual interactions. The computer system analyzes the respective user sequence models using a recurrent neural network with an attention mechanism, which produces respective vectors corresponding to the user sequence models. Individual values in the vectors represent respective individual interactions by a given user and correspond to an amount of correlation between the respective individual interactions and the sorting of the given user into the first group or the second group. The computer system identifies a particular type of interaction that is correlated to users being sorted into the first group by analyzing the respective vectors.

Abstract: Techniques are disclosed relating to improving machine learning classification using both labeled and unlabeled data, including electronic transactions. A computing system may train a machine learning module using a first set of transactions (of any classifiable data) with label information that indicates designated classifications for those transactions and a second set of transactions without label information. This can allow for improved classification error rates, particularly when additional labeled data may not be present (e.g., if a transaction was disallowed, it may not be later labeled as fraudulent or not). The training process may include generating first error data based on classification results for the first set of transactions, generating second error data based on reconstruction results for both the first and second sets of transactions, and updating the machine learning module based on the first and second error data.

Abstract: A system performs operations that include receiving a request to process a current payment transaction between a payment provider and a user having a user account with the payment provider. The operations further include determining a state of a recurrent neural network (RNN) fraud model for the user account by accessing a cache storing a set of encoded states for a plurality of nodes included in the RNN fraud model. The RNN fraud model is executed based on data associated with the current payment transaction and the set of encoded states stored in the cache. A new set of encoded states for the plurality of nodes are encoded and stored in place of the set of encoded state previous stored in the cache.

Abstract: A computer system is configured to receive a dataset that includes a plurality of transaction request records and is divisible into a plurality of segments. Each transaction request record includes an individual score calculated by a machine learning algorithm. The computer system also receives a plurality of constraints. The computer system is configured to calculate, using a linear programming algorithm, a decision threshold score for a particular segment of the plurality of segments using the transaction request records. The computer system is configured to provides access to the calculated decision threshold score to a production computer system. The production computer system is configured to use the decision threshold score to evaluate a subsequent transaction request corresponding to the particular segment.