YES Leasing has carved out a niche in the equipment leasing industry by providing flexible finance solutions to underserved markets. The company specializes in financing for small businesses and entrepreneurs who often face barriers to accessing traditional loans. With a focus on inclusivity and innovation, YES Leasing plays a critical role in enabling economic growth and resilience within these communities.
However, the high-risk nature of their portfolio required YES Leasing to balance accessibility with stringent risk controls. Quade Koffler, YES Leasing’s COO, recognized the need for a systematic—and intelligent—approach to internal controls.
Business Challenge
Operating in high-risk markets, YES Leasing faced several challenges that threatened throughput, accuracy, and compliance:
- Missed documentation: Manual reviews often overlooked critical clauses or collateral details, delaying funding and eroding client trust.
- Onboarding ramp time: New underwriters took weeks to learn complex document requirements, creating a training bottleneck.
- Scaling constraints: As application volume climbed, the final-review queue swelled, limiting YES Leasing’s ability to grow without adding headcount.
These issues not only hampered operational efficiency but also elevated risk exposure.
Solution & AI Implementation
Uptiq partnered with YES Leasing to deploy its Agentic LOS, embedding advanced AI capabilities throughout the final-review workflow:
- AI-Powered Document Ingestion & Extraction
- Natural Language Processing (NLP) models automatically parse unstructured lease documents, extracting key fields (payment schedule, collateral specs, guarantor clauses) and mapping them to the review checklist.
- A knowledge-graph–backed RAG (Retrieval-Augmented Generation) layere nsures extracted data aligns with YES Leasing’s institutional policies and regulatory guidelines.
- Intelligent Rule Engine
- Machine-learning–driven rule templates dynamically adjust documentre quirements based on profile attributes (e.g., lease size, industry segment).
- Anomaly-detection algorithms surface outliers—such as non-standard terms or missing collateral details—flagging them for prioritized human review
- AI-Guided Approval
- A conversational AI assistant walks underwriters through each checklist item, answering policy questions in real time and suggesting next actions based on historical approvals.
Results & Impact
- 50% Reduction in Review Cycle Time NLP extraction and AI-prioritized exception detection slashed average final-review turnaround from 4 days to under 2 days.
- 30% Fewer Manual Errors Intelligent rule-based checks caught 95% of missing or misfiled documents before human review, versus 65% under the legacy process.
- 40% Faster Onboarding New underwriters reached full productivity in 2 weeks—half the time previously required—thanks to in-platform AI guidance and contextual policy recommendations.
- Scalable Growth without Headcount Increase With AI-driven throughput, YES Leasing handled a 3× volume spike in Q1 2025 without adding senior-review hires, preserving margin and maintaining quality.
Key Takeaways
- AI as a Force Multiplier: Embedding AI throughout document workflows drives step-change improvements in speed, accuracy, and compliance.
- Operational Resilience: Predictive insights and automated compliance reportingequip leadership with real-time control over risk.
- Blueprint for Growth: Other high-risk lenders can replicate this model—combininghuman judgment with AI-augmented review—to scale rapidly without compromisingquality.
