Machine learning techniques for evaluating entities

An apparatus, comprising:

• at least one processor;
• and
• at least one memory comprising computer program code,
• the at least one memory and computer program code configured, with the at least one processor, to cause the apparatus at least to
• receive identifying information for an entity;
• collect, using the identifying information, data relating to the entity from at least one of public data sources or private data sources;
• determine, by a relevance model, a relevancy of the collected data to the entity;
• filter the collected data based on the determined relevancy of the collected data to produce relevant data;
• classify, by a classification model, the relevant data into different areas of risk associated with the entity;
• store the relevant data and links between the relevant data in a knowledge graph;
• determine, from the relevant data, information regarding risk attributes of the entity;
• analyze the relevant data, the different areas of risk associated with the entity, and the information regarding the risk attributes of the entity to determine and assign, through an entity risk model, a risk score for the entity;
• and
• output the risk score for the entity to a device of an end user.

A computer program, embodied on a non-transitory computer readable medium, the computer program configured to control a processor to perform a process, comprising:

• receiving identifying information for an entity;
• collecting, using the identifying information, data relating to the entity from at least one of public data sources or private data sources;
• determining, by a relevance model, a relevancy of the collected data to the entity;
• filtering the collected data based on the determined relevancy of the collected data to produce relevant data;
• classifying, by a classification model, the relevant data into different areas of risk associated with the entity;
• storing the relevant data and links between the relevant data in a knowledge graph;
• determining, from the relevant data, information regarding risk attributes of the entity;
• analyzing the relevant data, the different areas of risk associated with the entity, and the information regarding the risk attributes of the entity to determine and assign, through an entity risk model, a risk score for the entity;
• and
• outputting the risk score for the entity to a device of an end user.