Revolutionizing Collections: The Impact of Agentic AI on Invoice Management
Navigating the Landscape of Overdue Invoices
In the world of collections, teams often find themselves grappling with pressure as they chase overdue invoices. This effort frequently creates a cycle of friction characterized by numerous phone calls, persistent emails, and extensive research before any effective outreach can occur. The manual grind not only drains productivity but also has the potential to strain valuable customer relationships.
A New Paradigm with Agentic AI
According to Dave Ruda, Vice President of Product at Billtrust, the emergence of agentic artificial intelligence offers a promising solution to this daunting challenge. By introducing structure, prediction, and scalability to the management of aging accounts, agentic AI could transform the way businesses approach collections.
Ruda emphasizes that simply assessing aging buckets of invoices from the highest to lowest value is an inadequate method. This approach provides only a fleeting snapshot of a customer’s situation. Before outreach begins, considerable research is still necessary—a process that should be fully automated.
Understanding Customer Profiles: A Data-Driven Approach
Ruda likens the first step in modernizing collections to assembling a baseball card collection. Each customer possesses a statistical profile that can be developed over time from enterprise resource planning (ERP) data. When this information is effectively structured, AI can then identify clusters of customers exhibiting similar spending patterns or risk profiles.
“The agents come into play once you have structured data,” Ruda notes, “allowing for the identification of patterns and correlations. This knowledge informs not just who to contact, but also how and when to reach out.”
Improving Outreach with Targeted Strategies
With the ability to effectively segment and prioritize accounts, collections teams can move past blanket outreach tactics. Instead, they can focus on targeting the right customers at the optimal time, maximizing their chances of successful engagement.
Scaling Human Interactions through Technology
Agentic AI seeks to enhance the human aspect of collections work. Ruda explains that collectors typically spend most of their time navigating tedious email correspondence and phone calls—tasks that mimic sales strategies but aim to secure payment for invoices. Herein lies technology’s significant opportunity: by augmenting the workforce rather than replacing it, AI can handle repetitive tasks, allowing collectors to engage in more productive conversations.
“This advancement in technology can truly supercharge their reach,” he says.
Measuring Success: The AI-Driven Metrics
Determining success in collections is paramount. Ruda stresses the importance of rigorous testing and validation of AI predictions. Metrics like payment dates, Days Sales Outstanding (DSO), and average delinquency rates provide integral insights into the effectiveness of AI-driven collections and should be utilized: “You implement the capabilities and see if you’re moving the needle,” he explains.
To ensure accuracy, Billtrust’s underlying protocols incorporate comprehensive testing to ground AI predictions in reliable data—considering various potential outcomes while minimizing drift into inaccurate assumptions.
Streamlining Disputes with Foreknowledge
Disputes are another critical area where agentic AI can introduce consistency and speed. Billtrust is actively working to utilize data in predicting and preventing disputes before they even occur. “If you have that data, you can get ahead of the dispute,” Ruda urges. This forward-thinking approach means finance teams can log in knowing which disputes are likely to be easily resolved and which ones may require more effort.
The result? Faster resolutions and sustained customer relationships—outcomes rarely achieved through traditional manual workflows.
Integrating Credit and Collections for Profitability
Agentic AI also facilitates a closer relationship between credit assessment and collections. By analyzing payment consistency, credit limits, and replenishment needs, finance teams, armed with this technology, can transform the collections process into one that drives profit rather than merely chasing overdue invoices.
“If you apply a data-driven approach, you can actually elevate a client’s credit line from $100,000 to $150,000 based on predictive insights. This shifts the finance office’s role into a profit center,” Ruda states.
AI Models and Their Grounding in Reality
Billtrust employs advanced large language models, customizing them with specialized strategies tailored for collections. Techniques such as retrieval-augmented generation (RAG) and contextual augmented generation (CAG) give agentic AI a solid grounding in real customer data, steering it away from generic responses and toward highly relevant suggestions.
When the AI scrapes the database for information, it returns results with 95% accuracy, ensuring suggestions for invoice management and customer outreach remain closely aligned with the finance team’s existing data.
Feedback Loops: Enhancing AI Performance
Human feedback is a crucial component in refining AI performance. If a user modifies an AI-generated collections email, Billtrust captures these changes and adjusts the model accordingly, fostering ongoing learning and improvement. Over time, the AI will adapt to the preferred tones and structures that resonate best with users.
“This ongoing evolution is how we ensure our AI sounds more personalized, with customers indicating, ‘It sounds like me now.’ This nuance is critical; we want to avoid a robotic tone in sensitive interactions,” Ruda emphasizes.
Ensuring Governance and Privacy in AI Operations
Billtrust places a strong emphasis on safeguarding customer data, maintaining compliance with SOC 2 standards and ensuring that every AI action is logged and visible to clients. Future features aim to refine outreach frequency and modality, allowing finance teams to experiment with various communication means—be it email, phone, or other channels—for maximum effectiveness.
“We’re developing tools that will allow teams to test different outreach modalities in collections, and the results will be impactful,” Ruda indicates.
A Vision for the Future: Streamlined Dispute Resolution
Looking ahead, Ruda anticipates a major shift by 2026, when the standardization of dispute resolution could be achieved through the unification of fragmented data sources. For now, agentic AI represents a critical step in modernizing collections, transforming a manual effort into a strategic pillar for customer trust and business growth.
Conclusion: Embracing the AI-Driven Future of Collections
In a landscape where previous methods of managing overdue invoices caused headaches for both collections teams and clients, agentic AI offers a transformative solution. By harnessing cutting-edge technology and data analytics, businesses have the opportunity to streamline their collections, enhance customer relationships, and drive profitability. With ongoing advancements in AI, the future of collections holds immense promise—enabling teams to foster connections rather than merely pursue payments. As organizations continue to embrace these tools, the friction of overdue invoice chasing may soon become a challenge of the past.
