Scoring trustworthiness, competence, and/or compatibility of any entity for activities including recruiting or hiring decisions, composing a team, insurance underwriting, credit decisions, or shortening or improving sales cycles

Abstract

Systems and methods for recruiting, counter-terrorism / security, insurance underwriting, sales and marketing improvement, decisioning financial transactions and collections, and social scoring are provided. Machine learning can assign connectivity values to other community members, including individuals, companies, products, brands, cities or neighborhoods, etc. Connectivity values may be automatically harvested from or assigned by third parties or based on the frequency and / or type of interactions between community members. Connectivity values may represent such factors as alignment, reputation within the community, degree of trust, competence at one or more skills, or compatibility with others. The degree and type of connectivity between two entities may be assessed by computing a connectivity value based upon connections between entities and relative or absolute trust, competence and / or compatibility features of the connections. Connectivity values identify best prospects (customers, hires, dates), find off-grid people, underwrite insurance, ‘decision’ loans & collections, shorten sales cycles, etc.

Description

BACKGROUND OF THE INVENTION[0001]This invention relates generally to networks of individuals and / or entities and network communities and, more particularly, to systems and methods for determining trust scores or connectivity within or between individuals and / or entities or networks of individuals and / or entities and using these scores to facilitate financial transactions.[0002]The connectivity, or relationships, of an individual or entity within a network community may be used to infer attributes of that individual or entity. For example, an individual or entity's connectivity within a network community may be used to determine the identity of the individual or entity (e.g., used to make decisions about identity claims and authentication), the trustworthiness or reputation of the individual, or the membership, status, and / or influence of that individual in a particular community or subset of a particular community.[0003]An individual or entity's connectivity within a network communi...

AI Technical Summary

Benefits of technology

The patent describes a decision-making algorithm that uses connectivity values to make decisions in advance, such as before a user requests credit or insurance. This algorithm can make these decisions based on user requests or as part of an automated process. This allows for quick and flexible decision-making in response to changes or new information.

Problems solved by technology

An individual or entity's connectivity within a network community, however, is difficult to quantify.

In addition, connectivity information from community members may come in various forms and on various scales, making it difficult to meaningfully compare one member's “trustworthiness” or “competence” and connectivity information with another member's “trustworthiness” or “competence” and connectivity information.

Also, many individuals may belong to multiple communities, further complicating the determination of a quantifiable representation of trust and connectivity within a network community.

Similarly, a particular individual may be associated with duplicate entries in one or more communities, due to, for example, errors in personal information such as name / information misspellings and / or outdated personal information.

Even if a quantifiable representation of an individual's connectivity is determined, it is often difficult to use this representation in a meaningful way to make real-world decisions about the individual (e.g., whether or not to trust the individual).

Such prospective analysis may be difficult as an individual or entity's connectivity within a network community may change rapidly as the connections between the individual or entity and others in the network community may change quantitatively or qualitatively.

This analysis becomes increasingly complex as if applied across multiple communities.

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