Risk-Based Pricing Gets a Boost with Fastlane (Fastlane Series, Part 3)

Parts 1 and 2 showed how Fastlane makes underwriting relationship-aware and delivers true straight-through processing. But there’s another challenge: adverse selection—and it’s costing credit unions loans and revenue every day.

The Problem: Credit Scores Don’t Tell the Whole Story

Adverse selection happens when one side of a transaction has more information than the other. In lending, that often means:

Credit unions rely heavily on credit scores.
Members know they’re lower risk than their score suggests.
They shop elsewhere for a fairer rate.

Example: A member with a 635 score is priced in a non-prime tier. But they’ve been with your CU for 10+ years, have stable income, strong deposits, and a sizable down payment. They know they’re less risky than their score reflects—and will find a lender who recognizes it.

The Solution: From Credit Score to Member Score

Fastlane enables credit unions to build a Member Score that factors in:

Tenure and relationship depth
Employment and income stability
Deposit and savings behavior
Loan-to-value ratios and down payments
Repayment history with the credit union

This holistic view balances traditional scoring with relationship insights—pricing loans more fairly and competitively.

Why It Matters

For Members: Recognition beyond a credit score builds trust and loyalty.
For Credit Unions: Win loans you’d otherwise lose, grow interest income, and reduce adverse selection.
For Both: Lending decisions become fair, transparent, and rooted in real data.

Member Score in Action

Fastlane customers apply Member Scores to enhance pricing and policy decisions, such as:

Loan Sizing: Allow higher DTI for unsecured products if the member’s score indicates lower risk.
LTV Flexibility: Offer higher loan-to-value maximums for vehicle loans when members show strong relationships.

This allows credit unions to serve loyal members profitably—without raising portfolio risk.

Fastlane In a Sentence

Fastlane ends adverse selection by making credit unions the best-informed party in lending—using relationship data to price loans fairly, retain members, and grow revenue.