Altana Federal Credit Union streamlines credit underwriting with Scienaptic AI platform

Altana Federal Credit Union, a Billings-based institution with $550 million in assets and 38,000+ members across 12 locations in Montana and Wyoming, faced mounting pressure to modernize a credit underwriting process rooted in legacy manual review. With both direct and indirect lending channels to manage, the credit union's existing decisioning approach limited throughput, introduced inconsistency in risk tiering, and made it difficult to extend credit to underserved members without increasing portfolio risk. Regulatory obligations around Fair Lending and adverse action explainability added further complexity, raising the compliance burden on lending staff and slowing loan cycle times for members who needed timely decisions.

Altana deployed Scienaptic AI's cloud-based, AI-native credit underwriting platform to automate loan decisioning across both direct and indirect lending channels. The platform applies machine learning and predictive analytics to evaluate creditworthiness beyond traditional scoring models, enabling more granular risk tiering and the ability to approve borrowers — including thin-file and underserved applicants — who would otherwise be declined. Scienaptic's platform was built to meet regulatory requirements out of the box, including automated generation of explainable adverse action notices required under Fair Lending standards. The deployment replaced or augmented manual underwriting workflows, integrating into Altana's existing lending operations and positioning the credit union to scale decisioning capacity without adding headcount.

Going live on the Scienaptic platform delivered immediate operational improvements across Altana's lending function. The underwriting process was significantly streamlined, reducing friction for both lending staff and members. Key outcomes include:

• Increased approval rates without a corresponding increase in portfolio risk, enabled by more precise ML-based credit evaluation
• Improved operational efficiency across direct and indirect lending, reducing manual review bottlenecks
• Enhanced member experience through faster, more consistent loan decisions
• Expanded credit accessibility for underserved members in Montana and Wyoming communities

All gains were achieved while maintaining full compliance with Fair Lending requirements and regulatory standards — a non-negotiable for a member-owned cooperative.

• Credit unions serving rural or underbanked populations can use AI underwriting to extend credit to thin-file members without increasing risk exposure — but only if the platform is built with fairness constraints from the ground up.
• Explainable adverse action is not optional for credit unions: any AI underwriting platform must generate compliant notices automatically, or it creates regulatory liability.
• Automating both direct and indirect lending through a single platform reduces operational complexity and creates consistent decisioning standards across channels.
• A cooperative's 75-year legacy of member trust requires that AI adoption visibly serve member outcomes — framing deployment around credit accessibility, not just efficiency, builds internal and member buy-in.