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Community 1st Credit Union selects Scienaptic for AI-driven credit decisioning

By Pavithra R

May 24, 2021

  • America
  • Community
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Community 1st Credit Union selects Scienaptic for AI-driven credit decisioningScienaptic AI, an AI-powered credit underwriting company equipping financial institutions with sharper tools to improve credit decisions has announced that Community 1st Credit Union has selected its AI-powered platform to enhance and augment its credit decisioning and underwriting capabilities for new and prospective members.

“We are pleased to help Community 1st Credit Union provide increased credit availability to its members and reach more potential prospects. Scienaptic’s powerful, adaptive AI will bolster Community 1st’s lending decisions, creating more approvals faster, all while strengthening member relationships and delivering an exceptional customer experience,” said Pankaj Jain, President of Scienaptic.

Founded in 1925, Community 1st Credit Union was the first credit union established in the state of Washington and is among the longest-running CUs in the U.S. With a focus and commitment towards community-based engagement and the financial well-being of its members, Community 1st has become a leading national provider of solar and other “green” financing through its ezSolarLoan division.

“We see the potential of Scienaptic’s AI-powered credit decisioning platform. In addition to helping us make quicker and better lending decisions, Scienaptic’s solution will assist us in expanding our solar and green energy lending reach and lowering financing costs in the nationwide residential solar market. We are excited to get started and expect our borrowers will be delighted with their experience at Community 1st and ezSolarLoan.com,” said Bill Paulen, CEO of Community 1st Credit Union.

Founded in 2014, Scienaptic is on a mission to increase credit availability by transforming technology used in credit decisioning. Its platform Ether Underwrite is creating improvements in higher application approvals (15-40%) and lower credit losses (10-25%) with all the regulatory explainability. The firm uses alternate data, raw trade line-level bureau data and AI to help banks find new credit eligible segments of customers.

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