System and method for detecting relevant subject entities in various databases

A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising:

• determining a plurality of relevance scores based on transaction data related to a potential participating entity and entity characteristics of a plurality of subject entities indicated in subject entity data, wherein each relevance score represents a relevance of a respective subject entity to the potential participating entity, wherein the plurality of relevance scores is determined using a machine learning model trained based on training subject entity data and training entity characteristics;
• identifying, based on the plurality of relevance scores, a plurality of relevant subject entities for the potential participating entity among the plurality of subject entities;
• resolving the plurality of relevant subject entities between the transaction data and the subject entity data, wherein resolving the plurality of relevant subject entities further comprises applying resolution rules requiring at least matching a plurality of features between respective instances of the subject entity in the transaction data and in the subject entity data, wherein each subject entity is resolved such that respective instances of the subject entity in the transaction data and in the subject entity data are determined as uniquely identifying the same subject entity;
• identifying at least one redundant instance among the plurality of relevant subject entities based on the resolution of the plurality of relevant subject entities between the transaction data and the subject entity data;
• and
• removing the at least one redundant instance from the plurality of relevant subject entities among the transaction data to determine at least one unique relevant subject entity.

A system for detecting a relevant subject entity across different databases, comprising:

• a processing circuitry;
• and
• a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
• determine a plurality of relevance scores based on transaction data related to a potential participating entity and entity characteristics of a plurality of subject entities indicated in subject entity data, wherein each relevance score represents a relevance of a respective subject entity to the potential participating entity, wherein the plurality of relevance scores is determined using a machine learning model trained based on training subject entity data and training entity characteristics;
• identify, based on the plurality of relevance scores, a plurality of relevant subject entities for the potential participating entity among the plurality of subject entities;
• resolve the plurality of relevant subject entities between the transaction data and the subject entity data, wherein resolving the plurality of relevant subject entities further comprises applying resolution rules requiring at least matching a plurality of features between respective instances of the subject entity in the transaction data and in the subject entity data, wherein each subject entity is resolved such that respective instances of the subject entity in the transaction data and in the subject entity data are determined as uniquely identifying the same subject entity;
• identify at least one redundant instance among the plurality of relevant subject entities based on the resolution of the plurality of relevant subject entities between the transaction data and the subject entity data;
• and
• remove the at least one redundant instance from the plurality of relevant subject entities among the transaction data to determine at least one unique relevant subject entity.