Mentioned Companies:

ZestFinance Machine Learning Underwriting

Traditional credit scoring to support lending businesses depends on mathematics called logistic regression to analyze 20 to 40 variables to support a decision. That math is effective when examining the credit histories of consumers in the prime or super-prime categories. It is less effective when considering consumers with little or no credit histories­—thin file, no file, or subprime prospects. Complex machine learning mathematics combined with what is effectively unlimited computer power is now available to make predictions on the creditworthiness of thin file, no file, or subprime pro...

Want to read more?

Please log into your subscriber account.

Check out our subscription offers.
New subscribers receive over 130 articles in the 22 issues published each year, plus the last five years of issues (that’s over 1,200 articles) as a download link, which are fully searchable in PDF format.

© Copyright 2024 Nilson Report


March 2019
Headlines in this Issue
Charts in this Issue

Top Acquirers in the U.S.

Investments & Acquisitions—February 2019

U.S. Acquirer Totals—2018 Ranked by Purchase Volume and Purchase Transactions

Top Merchant Acquirers in the U.S.—2018 Ranked by Visa/Mastercard Volume

Card Not Present (CNP) Acquiring in the U.S.—2018