Tool for configuring computational models

A method comprising:

• providing a client device a tool for configuring computational models;
• receiving, from the client device and through the tool, a selection of a set of different external data sources;
• receiving, from the client device and through the tool, a plurality of weight values for a plurality of different categories;
• receiving, from the client device and through the tool, a plurality of threshold values for the plurality of different categories;
• generating a plurality of different types of computational models based on the set of different external data sources, the plurality of weight values for the plurality of different categories, and the plurality of threshold values for the plurality of different categories, wherein each computational model in the plurality of different types of computational models is configured to generate a score associated with a category in the plurality of different categories;
• receiving, from the client device, a request for an overall score for an entity;
• using the plurality of different types of computational models to generate a plurality of scores for the entity;
• and
• determining the overall score for the entity based on the plurality of scores.

A system comprising:

• a set of processing units;
• and
• a non-transitory machine-readable medium storing instructions that when executed by at least one processing unit in the set of processing units cause the at least one processing unit to:
• provide a client device a tool for configuring computational models;
• receive, from the client device and through the tool, a selection of a set of different external data sources;
• receive, from the client device and through the tool, a plurality of weight values for a plurality of different categories;
• receive, from the client device and through the tool, a plurality of threshold values for the plurality of different categories;
• generate a plurality of different types of computational models based on the set of different external data sources, the plurality of weight values for the plurality of different categories, and the plurality of threshold values for the plurality of different categories, wherein each computational model in the plurality of different types of computational models is configured to generate a score associated with a category in the plurality of different categories;
• receive, from the client device, a request for an overall score for an entity;
• use the plurality of different types of computational models to generate a plurality of scores for the entity;
• and
• determine the overall score for the entity based on the plurality of scores.