In the fast-evolving space of finance technology, the implementation of agentic artificial intelligence (AI) is revolutionizing the way businesses handle overdue invoices. Traditionally, collections teams have faced the arduous task of chasing overdue payments through a series of time-consuming phone calls, emails, and extensive research. This manual grind not only hampers productivity but can also strain vital customer relationships.
### The Promise of Agentic AI
Dave Ruda, Vice President of Product at Billtrust, highlights the transformative potential of agentic AI in modernizing collections processes. The conventional approach of merely working through aging buckets of invoices offers limited insight into a customer’s payment behavior. Ruda argues that by automating research and outreach, businesses can fundamentally shift how they manage and resolve overdue invoices.
The initial step in this transformation is akin to organizing a baseball card collection, where each customer is viewed through multiple layers of data. By creating structured statistical profiles from Enterprise Resource Planning (ERP) data, AI systems can identify clusters of customers with similar spending or risk profiles. This capability enables finance teams to target outreach efforts more effectively, moving away from blanket communication toward strategic engagement based on data-driven insights.
### Scaling the Human Element in Collections
Ruda emphasizes that agentic AI is designed to enhance the human elements of collections rather than replace them. Collections staff often find themselves engaged in rote activities that mimic sales strategies but are focused on collecting payments. By automating these repetitive tasks, AI allows collectors to focus on more valuable, high-impact conversations with clients.
Measuring the success of these AI-driven enhancements involves tracking traditional business metrics like dates to payment, Days Sales Outstanding (DSO), and the effectiveness index. Billtrust implements rigorous testing protocols for its models to ensure the credibility of AI predictions, carefully considering both successful and unsuccessful outcomes to drive reliable results.
### Improving Dispute Management
Disputes over invoices pose significant challenges in the collections process. Billtrust is actively developing AI capabilities aimed at predicting and preventing disputes, allowing businesses to proactively address issues. Ruda notes that by analyzing patterns and historical data, AI can help anticipate conflicts before they arise and suggest solutions, resulting in faster resolutions and strengthened customer relationships.
Moreover, the integration of credit review and collections functions is made easier through AI analytics, which evaluates payment behavior and recommends adjustments in credit limits. This data-driven methodology can position finance departments as profit centers, thereby changing the narrative from one focused on collections to one of strategic financial growth.
### Technological Foundations of Billtrust’s AI
Billtrust’s AI is built on foundational large language models but customized with specialized strategies tailored to meet the unique challenges of collections. Innovations such as retrieval-augmented generation (RAG) and contextual augmented generation (CAG) are employed to enable AI systems to connect directly with structured databases. This grounding in specific customer data avoids generic responses and increases the reliability of AI outputs.
To ensure the AI remains aligned with actual customer interactions, human feedback is crucial. For instance, if a collections email is adjusted by a staff member, Billtrust captures this change to refine AI recommendations over time. This iterative learning process aids the AI in evolving its language to better reflect the brand’s voice, making interactions feel more human and less robotic.
### Governance and Future Directions
In addition to improving communication and resolution processes, Billtrust places a high emphasis on data security and compliance. The company ensures that customer data is contained and managed within its systems, adhering to SOC 2 compliance and maintaining full audit capabilities.
Looking ahead, Billtrust plans to introduce features that will allow finance teams to optimize outreach in terms of frequency and communication modality. By testing different channels for payment reminders—be it email, phone, or other avenues—finance teams will gain insights into what approaches yield the best results.
As the landscape of finance technology continues to evolve, Ruda projects that 2026 may see significant advancements in standardizing dispute resolution processes. Bridging fragmented data sources through AI could empower finance teams to shift collections from a labor-intensive chore into a strategic function that fosters customer trust and drives growth.
### Conclusion
The integration of agentic AI into collections processes offers a significant opportunity for businesses to enhance efficiency and improve customer relationships. By automating routine tasks, providing predictive insights, and fostering a more data-driven approach, finance teams can transform their operations into proactive, strategic functions. In doing so, they can not only chase invoices faster than ever before but also harness the potential for financial growth and enhanced customer trust.
With continued advancements in AI technology and the commitment to human-centered interactions, the future of collections is not just about recovering payments but building long-term partnerships and driving business success.
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