Abstract: Methods and apparatuses are described for predictive analysis of transaction data using machine learning. A server computing device trains a plurality of machine learning models using historical transaction data for a set of entities as input to predict a likelihood of future transaction activity for each of the entities, each machine learning model trained on a different target transaction variable. The server computing device executes each of the plurality of machine learning models to generate, for each entity, a predicted likelihood value for a future transaction associated with the entity and each of the target transaction variables. The server computing device transmits the predicted likelihood values for each entity to a remote computing device for display.

Abstract: Systems and methods for extracting data from unstructured data sources based on proximity co-reference resolution model. The method includes receiving an electronic document from an unstructured data source and extracting entities from the electronic document. The method also includes receiving fields to be extracted from the electronic document and generating keywords based on the fields. Each of the entities is associated with at least one of the fields. The method further includes identifying keywords in the electronic document based on the generated keywords and calculating, for each of the fields, proximity scores based on a proximity co-reference resolution model. The method also includes, for each of the fields, identifying a field-entity pair based on the calculated proximity scores and generating for display on a user device the field-entity pair.

Abstract: Systems and methods for identifying data strings in electronic documents using pattern recognition. The method includes receiving a first data string corresponding to an electronic reference document from a first database and a second data string corresponding to an electronic legal document from a second database. The method also includes processing the first data string into a first processed data string and processing the second data string into a second processed data string. The method also includes calculating a cosine similarity between the first processed data string and the second processed data string. The method also includes receiving a feedback score from a user which corresponds to an accuracy of the calculated cosine similarity. The method also includes calculating an adjusted cosine similarity between the first processed data string and the second processed data string based on the calculated cosine similarity and the feedback score.

Abstract: Systems and methods for identifying data strings in electronic documents using pattern recognition. The method includes receiving a first data string corresponding to an electronic reference document from a first database and a second data string corresponding to an electronic legal document from a second database. The method also includes processing the first data string into a first processed data string and processing the second data string into a second processed data string. The method also includes calculating a cosine similarity between the first processed data string and the second processed data string. The method also includes receiving a feedback score from a user which corresponds to an accuracy of the calculated cosine similarity. The method also includes calculating an adjusted cosine similarity between the first processed data string and the second processed data string based on the calculated cosine similarity and the feedback score.