Revolutionizing Collections with Agentic AI

For collections teams, the task of pursuing overdue invoices is often fraught with tension. This process usually involves extensive phone calls, emails, and research, which can drain productivity and strain customer relationships.

According to Dave Ruda, Vice President of Products at Billtrust, the emergence of agentic AI is on the verge of changing the traditional dynamics of managing aging accounts. Ruda emphasizes that this technology aims to provide businesses with structure, prediction, and scalability in their collections efforts.

The Shift from Manual Labor to Automation

Ruda highlights that the common practice of analyzing overdue invoices based on their age, from highest to lowest value, merely offers a fleeting snapshot of a customer’s current position. Instead of manual research before outreach, Ruda argues that this entire process should be automated, allowing collections teams to focus on higher-value tasks.

He likens modernizing collections to building a baseball card collection. Each customer can be assigned a statistical profile based on ERP data, allowing AI to identify groups with similar spending behaviors or risks. “Once you have structured data,” Ruda says, “agents can find patterns and correlations, helping teams determine how and when to reach out to specific customers effectively.”

Scaling Human Interaction with AI

Agentic AI aims to enhance the human aspect of collections, rather than replace it. Ruda points out that collectors typically spend much of their time on repetitive email and phone tasks mimicking sales strategies, with the ultimate goal of ensuring invoices are paid. By implementing agent systems, the current limitations can be extended significantly.

This technology allows collectors to focus on meaningful conversations instead of mundane tasks. Ruda further explains that measuring success becomes more systematic through established business outcomes and rigorous testing of AI predictions, leading to improved metrics such as payment dates, DSO questions, and overall debt management.

Enhancing Dispute Resolution

Another domain where agentic AI can have a substantial impact is in dispute resolution. Ruda mentions that aside from enhancing collections, Billtrust is focused on utilizing data to predict and prevent disputes proactively. “With this data, we can anticipate potential conflicts, enabling finance teams to resolve issues efficiently,” he explains.

The ultimate goal is to create agents that can preemptively resolve disputes as the finance team logs in for their day’s work, resulting in faster resolutions and improved customer relationships—outcomes that manual processes often fail to deliver.

Bridging Credit and Collections

Agentic AI also serves to align credit review and collections more closely. By analyzing payment histories and credit limits, the technology not only enhances collections but also enables finance teams to transform this function into a potential profit-generating department. Ruda describes this as taking a more formal approach to forecast customer credit adjustments to turn the finance office into a revenue center.

Inside Billtrust’s Innovative AI Models

Billtrust employs advanced foundational language models adapted with specialized data strategies tailored for the collections sector. Ruda mentions technological advancements such as retrieval augmented generation (RAG) and context-aware augmented generation (CAG) models. This allows AI systems to fetch structured data directly from databases, resulting in more accurate and reliable insights.

Through meticulous testing, Billtrust achieves a 95% accuracy rate in their AI models. They utilize human feedback loops, capturing user edits to improve the AI’s performance over time. This continuous refinement process ensures that the balance between technical efficiency and human touch remains intact, which is essential for sensitive customer interactions.

Commitment to Governance and Privacy

Billtrust prioritizes customer privacy and data governance, maintaining SOC 2 compliance and full auditability of AI actions. Future enhancements are expected to focus on optimizing the frequency and methods of outreach to better test which communication channels yield the highest response rates. Ruda expresses excitement about these upcoming features, hinting at the potential innovations on the horizon.

Looking ahead, Ruda foresees 2026 as a pivotal year for standardizing dispute resolution as fragmented data sources are unified. Agentic AI offers a promising pathway for finance teams, equipping them with modern tools that can transform traditional collections into a strategic driver for customer trust and business growth.