Abstract: An enhanced pipeline for the generation, validation, and deployment of machine-based predictive models (PMs) is provided. The pipeline analyzes records to generate a graph that indicates various relationships between the records. A user provides a selection of a data element of interest (DEOI). The generated PM predicts values for the DEOI based on input records that do not include values for the DEOI. The user provides selections for values of the DEOI that represent positive outcomes associated with the DEOI. The user provides selections for values of the DEOI that represent negative outcomes associated with the DEOI. A subgraph of the graph is determined based on the DEOI. A relevant set of records is determined based on the subgraph. The PM is automatically trained, validated, and deployed based on the relevant set of records, the DEOI, and the representative values for the DEOI.

Abstract: A number of memory spaces are defined for a memory structure. Transaction data is received, and financial transactions are scored based on the transaction data to generate a batch of scores for the financial transactions. A score range is divided into k bins, each of the k bins representing one memory space of the memory spaces of the memory structure. The batch of scores are aggregated by storing a count of each score in a memory space, and a percentile is computed for each score in the batch of scores based on a set of values associated with each score. Each new financial transaction is scored to generate a new score, and a new percentile is assigned to the new score according to the set of values. The new percentile is then translated to a calibration score with fixed percentile characteristics according to a fixed reference curve.

Abstract: A number of memory spaces are defined for a memory structure. Transaction data is received, and financial transactions are scored based on the transaction data to generate a batch of scores for the financial transactions. A score range is divided into k bins, each of the k bins representing one memory space of the memory spaces of the memory structure. The batch of scores are aggregated by storing a count of each score in a memory space, and a percentile is computed for each score in the batch of scores based on a set of values associated with each score. Each new financial transaction is scored to generate a new score, and a new percentile is assigned to the new score according to the set of values. The new percentile is then translated to a calibration score with fixed percentile characteristics according to a fixed reference curve.

Abstract: A system and method for maintaining a pre-defined score distribution for financial transactions are disclosed. A number of memory spaces are defined for a memory structure. Transaction data for the financial transactions is received by the system. Each of the financial transactions is scored based on the transaction data to generate a batch of scores for the financial transactions. A score range is divided into k bins, where each of the k bins representing one memory space of the memory spaces of the memory structure. The batch of scores are aggregated by storing a count of each score of the batch of scores in an associated memory space of the plurality of memory spaces, and a percentile is computed for each score in the batch of scores based on a set of values associated with the count of each score. Each new financial transaction is scored to generate a new score, and a new percentile is assigned to the new score according to the set of values.

Abstract: A system and method for maintaining a pre-defined score distribution for financial transactions are disclosed. A number of memory spaces are defined for a memory structure. Transaction data for the financial transactions is received by the system. Each of the financial transactions is scored based on the transaction data to generate a batch of scores for the financial transactions. A score range is divided into k bins, where each of the k bins representing one memory space of the memory spaces of the memory structure. The batch of scores are aggregated by storing a count of each score of the batch of scores in an associated memory space of the plurality of memory spaces, and a percentile is computed for each score in the batch of scores based on a set of values associated with the count of each score. Each new financial transaction is scored to generate a new score, and a new percentile is assigned to the new score according to the set of values.