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

Teachers Federal Credit Union holds $9.7 billion in assets and serves over 460,000 members. Unlike most credit unions bound by membership eligibility, Teachers operates under an open charter — meaning anyone can join — giving it genuine national expansion potential. Processes across 16 business functions, including loan services, fraud detection, and credit and risk operations, involved repetitive, low-value tasks, and member service was limited to contact center and branch hours.

Teachers Federal Credit Union deployed an Intelligent Automation platform with support from SS&C Blue Prism and Infosys, blending Robotic Process Automation (RPA) with AI capabilities including machine learning and optical character recognition. The credit union selected 'automation champions' from each business unit to drive cross-functional adoption. The team embedded measurable KPIs — cost savings, hours saved, volume throughput, and click reduction — directly into each bot during development. Digital workers were deployed across loan services, fraud detection (IP tracing, with ML and OCR used to identify suspicious activity), 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

Fraud detection response times improved through IP tracing and the use of ML and OCR to identify suspicious activity. Employees freed from repetitive tasks were redirected toward member experience initiatives. A KPI dashboard created visibility into automation performance, reinforcing adoption. The credit union is now exploring predictive analytics to extend the program's impact.

• Early adoption of intelligent automation can be a competitive advantage — Teachers' CTO said adopting it ahead of peer institutions gave the credit union that advantage.
• 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 automation target: ML and OCR can speed up identification of suspicious activity while reducing staff burden.