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Northteq Upgrades Aurora Platform with AI, Fast Funding, Section 1071 Readiness Features

byRita Garwood
September 12, 2023
in EF News
Reading Time: 1 min read
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Northteq, a provider of Salesforce loan origination solutions, unveiled several upgrades to its Aurora platform, including the embracing of artificial intelligence technology and the introduction of intelligent application entry, Section 1071 borrower authentication and enhanced credit summaries.

Northteq’s AI model transforms handwritten applications into digital credit applications while its pre-configured tools expedite the entire lending process to just three minutes. The company’s borrower authentication upgrades ensure a frictionless, compliant process and allow lenders to be ready to implement Section 1071 requirements. The company’s enhanced credit summaries offer a tailored borrower snapshot for precise decisioning.

Key feature updates include:

  • Intelligent Application Entry: Lenders can instantly populate credit applications directly into Aurora using Northteq’s trained AI model. Lenders, brokers and vendor partners can take a picture of a handwritten application and securely upload it from any device or drag and drop a PDF application directly into a lender’s loan origination system in seconds.
  • End-to-End in 3 Minutes: Out of the box, pre-configured components, like an approval matrix, scorecards, pricing tables and more, come ready to use. Lenders can take a deal from application to documentation in a fully automated process that takes approximately three minutes on installation.
  • Section 1071 Borrower Authentication: Users can meet credit authorization needs, modify applications and securely upload necessary documents from any device. Aurora is primed for the impending Section 1071 requirements to assist lenders when the regulations take effect.
  • Enhanced Credit Summaries: Users can get a picture of a borrower in a single screen. Now lenders have the added advantage of customizing the credit summary screen with the data that matters most for credit decisioning.

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