Abstract: As described herein, a system, method, and computer program are provided for a multi-dimensional credit worthiness evaluation. Financial transaction data is accessed for an individual. A plurality of dimensions of credit worthiness is computed for the individual, using the financial transaction data. At least one machine learning model is trained, using the financial transaction data and the plurality of dimensions of credit worthiness as training data, to make at least one financial-related prediction for additional individuals. The at least one machine learning model is output for use in making the at least one financial-related prediction for the one or more of the additional individuals.

Abstract: As described herein, a system, method, and computer program are provided for quantifying the primacy of a financial relation for financial institution account management. An account of a customer with a financial institution is identified. Transactional-related information for the account is accessed. A holistic primacy score is computed for the account, using the transactional-related information, where the holistic primacy score holistically quantifies a primacy relation of the customer with the financial institution. At least one action is caused to be performed in association with the account, based on the holistic primacy score.

Abstract: As described herein, a system, method, and computer program are provided for a financial-based recommendation engine. In use, financial-related data is gathered for a plurality of users from a plurality of sources. Additionally, the plurality of users are clustered into a plurality of cohorts, based on the financial-related data. Further, a plurality of user profiles are created for the plurality of cohorts, including for each cohort of the plurality of cohorts, creating a corresponding user profile based on the financial-related data for the users in the cohort. Still yet, each user profile of the plurality of user profiles is matched to one another and to one or more financial products. Moreover, the one or more financial products matched to each user profile of the plurality of user profiles are recommended to the users in the cohort that corresponds to the user profile.

Abstract: Methods, systems and computer program products implementing data enrichment using global structure learning are disclosed. An information enrichment system predicts a likely canonical name from a transaction record in which names may be shortened, or extra token(s) inserted. In a training phase, the information enrichment system determines tag patterns based on labeled and unlabeled training transaction records. The tag patterns include co-occurrence probability and sequential order of co-occurrence of tags. In a testing phase, the information enrichment system receives a test transaction record. The information enrichment system predicts a likely tag sequence from the test transaction record based on the tag patterns. The information enrichment system predicts a canonical name based on likely tag values and token composition. The information enrichment system can then enrich the test transaction record with the predicted canonical name.