Teachers Federal Credit Union cuts loan processing time 50% with intelligent automation across 16 business functions

Teachers Federal Credit Union, holding $9.7 billion in assets and serving over 460,000 members, faced a structural tension between growth ambitions and operational capacity. Unlike most credit unions bound by membership eligibility, Teachers operates under an open charter — meaning anyone can join — giving it genuine national expansion potential. But that potential was constrained by manual processes spread across 16 business functions including loan services, fraud detection, and credit and risk operations. Staff were consuming thousands of working hours on repetitive, low-value tasks, and member service was limited to contact center and branch hours. Without automation, scaling nationally would have required proportional headcount growth, eroding the cost efficiency that makes credit union membership attractive.

Teachers Federal Credit Union deployed an Intelligent Automation platform from SS&C Blue Prism, with implementation support from Infosys, blending Robotic Process Automation (RPA) with AI capabilities including machine learning and optical character recognition. Rather than a top-down rollout, the credit union selected 'automation champions' from each of its 16 business units to drive cross-functional adoption and tailor automation to real operational needs. Critically, the team embedded measurable KPIs — cost savings, hours saved, volume throughput, and click reduction — directly into each bot during development rather than measuring after the fact. Digital workers were deployed across loan services, fraud detection (using ML for IP tracing and OCR for suspicious activity identification), and credit and risk operations, enabling round-the-clock processing independent of staffing schedules.

The automation program delivered measurable operational improvements across the credit union's core workflows:

• 50% faster loan application and essential transaction processing
• 13,250 employee days recovered from repetitive manual work
• 8 million+ manual clicks eliminated across digital worker deployments
• 24/7 member service enabled without additional staffing

Fraud detection response times improved through ML-assisted IP tracing and OCR-based suspicious activity identification. Employees freed from repetitive tasks were redirected toward member experience initiatives. KPI dashboards created visibility into automation performance across business units, reinforcing adoption and enabling continuous improvement. The credit union is now exploring predictive analytics to extend the program's impact.

• Early adoption of intelligent automation compounds over time — Teachers' CTO identified it as a direct competitive differentiator enabling national expansion ahead of peer institutions.
• Embed performance metrics into each bot at build time rather than retrofitting measurement; this creates accountability and makes the business case visible to every stakeholder.
• Designating business-unit 'automation champions' bridges the gap between IT delivery and operational adoption, reducing resistance and improving fit.
• 'Lights-out' processing capability — transactions completing outside branch hours — is especially valuable for open-charter credit unions competing on member convenience at national scale.
• Fraud detection is a high-value early automation target: ML and OCR can match or exceed manual review speed while reducing staff burden.