Abstract: Methods, systems, and computer-readable media are disclosed herein for improved state identification and prediction in computerized queries. In an aspects, a neural network model is trained via word embedding, using a plurality of workflows having a plurality of steps as input training data. The model may be searched using a string to locate and identify semantic matches as potential results, where the potential results correspond to a specific step and/or a particular workflow. Markov chaining may also be performed, using the potential results, in order to predict one or more additional results, where the additional results correspond to a specific succeeding step within a particular workflow, in some aspects. The results and predicted steps may be displayed.

Abstract: Systems, methods, and computer program products for detecting merchant data shifts may identify a shift in transaction volume of a merchant system across Merchant Category Codes (MCCs) using a combination of time series analysis and machine learning.

Abstract: Methods and systems are described. A method includes accessing training data samples that includes a plurality of transaction laundering associated features and generating random samples, training a first plurality of different models to identify transaction laundering merchants based on random samples generated from each of the training data samples, training a second plurality of different models to identify the transaction laundering merchants based on the training data, generating a transaction laundering classification for a merchant from each of the first plurality of models and each of the second plurality of models, generating a first model group classification based on a first majority vote on transaction laundering classifications from the first plurality of models, and generating a second model group classification based on a second majority vote on transaction laundering classifications from the second plurality of models. A likelihood that the merchant is a transaction launderer is determined.

Abstract: Methods and systems are described. A method includes accessing training data samples that includes a plurality of transaction laundering associated features and generating random samples, training a first plurality of different models to identify transaction laundering merchants based on random samples generated from each of the training data samples, training a second plurality of different models to identify the transaction laundering merchants based on the training data, generating a transaction laundering classification for a merchant from each of the first plurality of models and each of the second plurality of models, generating a first model group classification based on a first majority vote on transaction laundering classifications from the first plurality of models, and generating a second model group classification based on a second majority vote on transaction laundering classifications from the second plurality of models. A likelihood that the merchant is a transaction launderer is determined.