Machine Learning and AI in Lending

Our digital platform enables loan automation with machine learning and AI. Lenders are able to get better insights, improve business performance and access reliable credit scores of thin-file and no-file applicants. We look at the size and reach of social networks, the sentiment of messages and data, the volume of information available, the presence of red flag indicators, and much more. When this data is coupled with specific decision profiles, a reliable profile can be generated. This is Credit Bureau 2.0TM.

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Dynamic Credit Scoring

Powered by our patented machine learning and artificial intelligence technology, the Trust Science credit scoring algorithms combine traditional and alternative data with consent-based mobile and social data to deliver in-depth and dynamic credit scoring machine learning models and scores. Trust Science:

  • Expands scorable universe and identifies new targets who may not have a complete profile based on traditional methods (e.g. thin-file, no-file, credit invisibles)
  • Better matches products and services to targets based on robust credit scoring machine learning models

Trust Science® uses patented algorithms to analyze publicly available digital information. We collect loan data from social media, news sources, court data, web search, transactions and more. Using sophisticated machine learning models and algorithms, we process this data to then determine the creditworthiness of individuals, business and organizations.


30+ Patents In Twelve Countries

We have developed a portfolio of over 30+ patents related to artificial intelligence and machine learning as applied to leverage publicly available, mobile and social data in scoring trust. Our patents have been granted in twelve countries.

Trust Science Patent Map

To date, our patent portfolio covers:

  • Mapping the intensity of connectedness between two people (the “six degrees of separation” idea) and among many people/social fabric. "Social Graph"
  • The use of an A.I.-infused SIX°SCORE™ in settings from lending to dating to recruiting to law enforcement/counter-terrorism to monitoring one's employees to online commerce or the use of sharing economy sites
  • Machine, A.I. and statistical analysis of trustworthiness and connectedness
  • The application of trustworthiness in the context of digital currencies (virtual/blockchain)
  • The fact that trustworthiness may be context-dependent
  • Not only is trustworthiness of a person important, but so is the Risk Tolerance of our users
  • Geo-location factors (of both the user and the target) regarding trust assessments
  • Identity-matching and Entity-resolution is important for a system like this, which gathers data "in the wild"
  • Visually depicting complex, Big Data results
  • Determining social connectivity for use in determining trustworthiness
  • Permitting entry into, or filtering the ability to view, financial applications based upon connectedness
  • Assessing trustworthiness of people, businesses, products, brands, and places
  • Improving trustworthiness based on crowd-sourced data
  • Trend analysis of direct and indirect data to extrapolate and predict trustworthiness trajectories

We continue to expand our capabilities in the applications of AI, machine learning, big data, and cognitive computing as they relate to context-aware social index scoring and trustworthiness, with additional patents pending.

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Credit scoring you can count on that integrates into your decisioning process.