AI Agents Chase Invoices Faster Than Finance Teams Can Dial
Collections is still too manual: chasing emails, dialing phones, and digging for context before you even reach out. Agentic AI changes the workflow by bringing structure, prediction, and scale to aging accounts while keeping the human touch where it matters.
- Prioritize with precision: Move past aging buckets to dynamic, data-driven account scoring.
- Scale outreach: Automate the repetitive motions so collectors can focus on conversations that move cash.
- Reduce rework: Predict disputes, stage outreach by frequency and channel, and resolve faster.
From Baseball Cards to Data Clusters
Think of each customer like a baseball card: a statistical profile built over time from ERP activity and payment history. Once structured, that data lets AI identify clusters with similar behavior and risk. You find the "who," then decide "how and when" to reach out.
This shifts teams from blanket outreach to targeted action. Accounts get prioritized by likelihood to pay, dispute risk, and the next best step - not just invoice value and days past due.
Scaling the Human Element
Agentic AI doesn't replace collectors - it extends them. The day-to-day is email and phone work that looks a lot like sales motion, but aimed at cash in. AI handles the rote loops: drafting messages, scheduling follow-ups, summarizing account context, and logging outcomes.
As Dave Ruda, vice president of product at Billtrust, put it: "That's the biggest opportunity to advance with technology and supercharge what their reach can be." Teams spend more time on higher-value conversations and less time prepping for them.
What Finance Should Measure
You don't need new KPIs to prove value - just track movement before and after implementation:
- Days sales outstanding (DSO) and average delinquency
- Dates to payment by segment and outreach sequence
- Effectiveness index by channel and cadence
- Total outstanding and recovery velocity
Billtrust builds testing protocols into its models to validate predictions across positive, negative, and neutral cases. The aim: improve outcomes without drifting into unreliable outputs.
Disputes: Predict, Prevent, Resolve
Disputes slow cash and frustrate customers. With structured data, agentic systems can flag likely disputes in advance, assemble context, and route easy wins for same-day resolution. "We can create agents to get ahead of it so that when finance teams log in that day for work, we've already figured out which disputes can be easily resolved, and which cannot," Ruda said.
The result is faster cycle time and fewer strained relationships.
Credit and Collections on a Shared Backbone
Credit review and collections feed each other. By analyzing payment consistency, credit limits, and replenishment patterns, AI can recommend precise limit adjustments. As Ruda noted, moving a customer from a $100,000 to $150,000 line at the right time can turn finance from cost center to profit driver.
Inside the Models: Grounded, Testable, and Trainable
Billtrust adapts large language models for collections with retrieval-augmented generation (RAG), contextual augmented generation (CAG), and MCP servers to connect directly to structured databases. The instruction is simple: "Skim this database, and here's the coaching language."
Every recommendation ties back to customer-specific data - which invoices to pursue, what to say, and when to escalate. In testing, they report 95% accuracy and continue to improve via feedback loops: if a user edits an AI-drafted email, the system captures those changes, aggregates the patterns, and refines tone, structure, and phrasing over time. "Our customers say, 'It sounds like me now,'" Ruda said.
Governance, Privacy, and Auditability
Data stays contained within Billtrust systems with full audit trails and SOC 2 controls. Every AI action is logged and visible to clients. For finance leaders, that means clear accountability and traceability from data to decision to outcome.
For more on SOC 2 principles, see the AICPA SOC 2 overview.
What's Next: Smarter Outreach Cadence and Modality
Upcoming features will stage outreach by frequency and channel - testing whether a short SMS, a data-backed email, or a well-timed call clears the invoice faster. Expect 2026 to bring more standardization in dispute workflows as fragmented data sources get unified.
How to Pilot Agentic AI in 90 Days
- Centralize your data: Pull invoices, payments, credit terms, and dispute codes into a clean, queryable layer.
- Define outcomes: Pick 3-5 KPIs (DSO, delinquency, right-party contacts, first-touch resolution).
- Segment accounts: Start with a pilot cohort by industry, size, or behavior. Set clear test/control groups.
- Automate outreach: Use AI to draft emails, propose call scripts, and schedule follow-ups. Keep humans in the loop for approvals.
- Instrument feedback: Capture edits to AI content and outcomes to retrain tone and tactics.
- Review weekly: Compare pilot vs. baseline. Keep what moves cash faster; iterate on what doesn't.
Practical Guardrails
- Ground every action in your data: No generic responses. Cite invoices, promises to pay, and payment history.
- Respect customer tone: Match voice to relationship tier and risk profile. Firm, clear, professional.
- Keep humans on exceptions: Complex disputes, key accounts, and legal escalations stay with your team.
- Audit everything: Log prompts, outputs, decisions, and outcomes for compliance and training.
Bottom Line for Finance Leaders
Agentic AI gives collections teams leverage: prioritize what matters, scale outreach without burning your team, and resolve disputes before they stall cash. The tech handles the repetitive loops. Your people handle the conversations that move money and protect relationships.
If you're exploring tools and training for finance-focused AI, browse a curated list of options here: AI tools for finance.
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