Abstract: A computerized method of automatic distributed communication includes training a first and second machine learning models with historical feature vector inputs to generate a likelihood output and a mean count output, respectively. For each entity in a set, the method includes processing a likelihood feature vector input with the first machine learning model to generate a likelihood output indicative of a likelihood that the entity will have an avoidable negative health event within a specified first time period, and processing a mean count feature vector input with the second machine learning model to generate a mean count output indicative of an expected number of avoidable negative health events that the entity will have within a specified second time period. The method includes automatically distributing structured campaign data to at least a subset of entities in the set according to the likelihood output or the mean count output.

Abstract: A computer system includes memory hardware configured to store a machine learning model, historical feature vector inputs, and computer-executable instructions, and processor hardware configured to execute the instructions. The instructions include training a first machine learning model with the historical feature vector inputs to generate a title score output, and training a second machine learning model with the historical feature vector inputs to generate a background score output. For each entity in a set, the instructions include processing a title feature vector input with the first machine learning model, and processing a background feature vector with a second machine learning model, to generate a tittle score output and a background score output each indicative of a likelihood that the entity is a decision entity. The instructions include automatically distributing structured campaign data to the entity based on the title score output and the background score output.

Abstract: Systems and methods for constructing sets of synthetic data. A single data record is identified from a first set of data. The first set of data comprises a first plurality of data records, each of the data records including multiple items of data describing an entity. Using pattern recognition, the single data record is processed to identify a group of records from within the first set that have corresponding characteristics equivalent to the single data record. The identified group of records comprises a target set of variables and the group of records from the first set that are not identified comprises a control set of variables. The target set of variables and the control set of variables are processed, using probability estimation and optimization constraints, to determine a score for each of the records in the first set. The score describes how similar each of the records in the first set is to the single data record. The records associated with a percentage of the highest scores are identified.

Abstract: Systems and methods for constructing sets of synthetic data. A single data record is identified from a first set of data. The first set of data comprises a first plurality of data records, each of the data records including multiple items of data describing an entity. Using pattern recognition, the single data record is processed to identify a group of records from within the first set that have corresponding characteristics equivalent to the single data record. The identified group of records comprises a target set of variables and the group of records from the first set that are not identified comprises a control set of variables. The target set of variables and the control set of variables are processed, using probability estimation and optimization constraints, to determine a score for each of the records in the first set. The score describes how similar each of the records in the first set is to the single data record. The records associated with a percentage of the highest scores are identified.

Abstract: Methods and systems for identifying and retaining customers who are likely to terminate an existing loan contract at a date earlier than the loan termination date identified in the loan contract are disclosed. The method includes the steps of using an early termination model to identify customers likely to terminate an existing loan contract at a date earlier than the loan termination date identified in the loan contract, notifying those customers of new loan opportunities at competitive loan interest rates and attempting to cross sell new loans to the customers before the customer early terminates their existing loan.

Abstract: A method is disclosed for generating customer leads for use by dealers attempting to sell a product, for example a loan product. The method is implemented on a system and includes providing a database of customer information, predicting a propensity for one or more customers within the database to respond to an offer, predicting when the customers will respond to the offer, generating a potential customer list, and providing the potential customer list to one or more dealers.

Abstract: Methods and systems for identifying and retaining customers who are likely to terminate an existing loan contract at a date earlier than the loan termination date identified in the loan contract are disclosed. The method includes the steps of using an early termination model to identify customers likely to terminate an existing loan contract at a date earlier than the loan termination date identified in the loan contract, notifying those customers of new loan opportunities at competitive loan interest rates and attempting to cross sell new loans to the customers before the customer early terminates their existing loan.

Abstract: Methods and systems for managing customer relationships are disclosed where the method includes the steps of providing a database of customer information and customer spending data, predicting future customer behavior based on the customer information and customer spending data, and constructing customer campaigns with personalized offers for targeted customers.

Abstract: A method is disclosed for generating customer leads for use by dealers attempting to sell a product, for example a loan product. The method is implemented on a system and includes providing a database of customer information, predicting a propensity for one or more customers within the database to respond to an offer, predicting when the customers will respond to the offer, generating a potential customer list, and providing the potential customer list to one or more dealers.