Skip to main content
This site uses cookies to provide you with a greater user experience. By using Exceed LMS, you accept our use of cookies.
Billtrust

All Topics

Introduction to Billtrust Cash Application

  • By Billtrust
  • Published: Sep 4, 2026
  • Duration 5m

By the end of this course, you will be able to:

  • Explain how Billtrust Cash Application uses confidence-based matching, machine learning, and OCR to automate cash application.

  • Identify key operational benefits of Billtrust Cash Application, including higher match rates, faster exception handling, and reduced manual research.

  • Adjust confidence thresholds and use Match Review and smart queues to resolve payment exceptions efficiently.

Outline

Overview of Billtrust Cash Application

Billtrust Cash Application automates and streamlines the cash application process, focusing user effort on exceptions rather than routine matching. The system learns from user actions to inform future matching decisions, improving over time. Confidence-based matching enables straight-through processing by matching transactions even when remittance data is imperfect or incomplete, reducing manual research and rework. The platform handles asynchronous remits and payments, allowing decoupled remittances to be matched efficiently for faster processing. The product targets improvements in match rates, exception handling speed, and overall processing time.

That means more accuracy and fewer anomalies with automated data extraction and capture, eliminating errors and exceptions while advanced algorithms auto-match with precision. Teams can customize matching criteria even further with user-controlled adjustments. Actionable insights pinpoint customers with low match rates. And since the system is confidence-based, not rule-based, teams can adjust the confidence scoring threshold as they see fit to increase or decrease the number of exceptions flagged for manual follow-up. With machine learning and optical character recognition (OCR) models the system matches evolving invoice formats and payment structures using adaptive intelligence, handling unstructured data with ease.

Accurate matching with AI in Cash Application

Features such as Match Review help your business resolve payment exceptions faster with full context and control.

Conclusion

  1. Key Features of the System: Billtrust Cash Application uses confidence-based matching and machine learning to automate transaction matching, even when remittance data is incomplete.
  2. Benefits of Implementation: The platform improves match rates and speeds up exception handling, cutting down on manual research while giving teams control over confidence thresholds.
  3. Steps for Effective Use: Use Match Review to resolve payment exceptions with full context, and let the system's smart queues and adaptive intelligence handle routine matching automatically.

Ready to keep learning?

Use the Topics menu to explore training grouped by product or use the search bar to find courses about a specific feature.