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Project Pangaea is about acquiring the rights to sell a credit card portfolio, improving the average FICO of the portfolio and then earning a commission on a) the sale of leads into the portfolio and b) the sale of the portfolio.

Pangaea means "all lands" in Greek, and figures prominently in the theory of continental drifts. This project is aptly named because of the coagulation of credit into a handful of continents (large issuers like B of A, Citi and Chase).

The PROBLEM is that some portfolios are laden with low FICOs and can't get acquired. These rogue islands/land masses can't mash back up into the Pangaea until the average FICO is boosted.

Solution: raise average FICO by doing lead generation from customer bases of FICO >680. Duck9 hands out slices of pizza through contests and gets students to FICO scores over 750 when they graduate. Minimal investment to buy more slices of pizza. Also, the big guy needs his lap dances, so we'll need to fundraise another $50-125.00. Seriously, we are looking for brains to add to the deal but no money.

Back Story:

UCMS does thin file analysis (TFA) . In the early 80s, the largest challenge was underwriting young adult credit card accounts since their credit files were so thin.

UCMS in 1992 began consulting its credit card issuing clients on how to evaluate college applicants. The following is the derivation of the "Thin File Analysis" model . TFA is now the industry benchmark in underwriting young adults, new immigrants and citizens of third world countries. Larry Chiang presented this at World Bank's "Strengthening Public and Private Credit Registry Ties" in 2004

Larry Chiang, founder of UCMS, utilized a solid-liquid-gas thermodynamics model as the basis for UCMS Scoreard Matrix. Prior to his plagiarization of thermodynamics, credit decisioning was one dimensional 250 to 850 score where all "670s" were treated the same even though some 670s were rising / falling. The largest shortfall of treating all 670s alike was the disparity from 1 year of credit data or 90 years of data.

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