AI Agent for
Accounts Receivable Collections
What is a voice AI agent for AR collections?
A voice AI agent makes outbound collection calls to customers with past-due invoices, conducting natural conversations to request payment, understand objections, and document outcomes. You maintain control over who gets called. The agent logs complete call details with disposition codes in your ERP.
The Cost of Manual Collections
$75,000
Annual Labor Cost
For one full-time collector (150-200 calls/week)
60-80
Calls Required Weekly
Just to touch every past-due account once
The Human Factor Challenges
Caller Fatigue:
Making collection calls is repetitive. Staff performance degrades. The 50th call gets less attention than the 10th.
Emotional Variability:
Frustration from a difficult earlier call affects the next conversation. Bad days produce inconsistent results.
Unconscious Bias:
Collectors develop assumptions about which customers will pay. These assumptions affect call priority and tone.
Inconsistent Approach:
Different collectors use diff. tones. The same customer might get different treatment depending on who calls.
Documentation Gaps:
After 30-40 calls, notes get shorter. Key details like sentiment and specific objections get missed.
How the Voice Agent Works
Step-by-step process flow:
1
You Select Who to Call
It starts with your control. The agent presents a dashboard of past-due accounts with aging, amount, and customer info.
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Review and select customers for today's queue
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Exclude sensitive or strategic accounts
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Define priority for queue processing
2
Agent Places the Call
The agent dials directly from your ERP context.
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Handles initial greeting naturally
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Introduces purpose clearly and professionally
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Adapts immediately to person vs. voicemail
3
Conducts the Conversation
Speaks naturally using prompts you have defined.
"Hello, this is calling regarding invoice #4092 for $1,250 due last Friday..."
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States specific invoice details
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Requests payment or commitment
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Actively listens to response
4
Handles Customer Responses
Intelligent handling of common scenarios and objections.
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Answers questions (payment terms, how to pay)
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Addresses standard objections ("didn't receive invoice")
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Recognizes when human intervention is actually needed
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Maintains neutral, professional tone always
5
Real-Time Sentiment Analysis
Listens to tone and word choice to adjust approach.
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Identifies: Cooperative, Frustrated, Evasive, Confused
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Adjusts: More patient with confused, persistent with evasive
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Logs sentiment for your review
6
Documents the Outcome
Records a specific Disposition Code for every call.
✅ Payment Promised
⚠️ Dispute Raised
📞 Callback Requested
📧 Voicemail Left
Logs complete details and timestamps directly in your ERP.
7
Escalates When Needed
Recognizes trigger phrases and transfers immediately.
"Speak to manager" "Disputed" "Lawyer"
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Transfers call to human staff immediately
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Provides context (who, what discussed, sentiment)
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Holds call until human takes over
8
Provides Dashboard Visibility
Full oversight of the automated workforce.
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Shows all call activity (duration, outcome)
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Displays sentiment analysis across accounts
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Highlights accounts needing human attention
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Tracks payment promises and follow-ups
What the Voice Agent Does
Natural Conversation
The agent speaks fluently and naturally using conversational language you define. You control exactly what it says in different scenarios: opening greeting and introduction, how to state invoice details and payment requests, responses to common questions and objections, how to handle difficult customers (patient but persistent), when to offer payment arrangements vs. request full payment, and escalation language. This flexibility means the agent speaks with your company's voice and approach while maintaining consistent, professional delivery.
Sentiment Analysis:
Analyzes customer voice tone, pace, word choice, and emotional cues. Identifies whether customer is cooperative, frustrated, evasive, or agreeable. This context helps prioritize follow-up and escalation decisions, not score customers. Sentiment data logs for pattern analysis across your customer base.
Disposition code Docs:
Automatically assigns disposition codes based on call outcome. Standard codes include: Payment Promised, Dispute Raised, Callback Requested, Voicemail Left, Wrong Number, Needs Escalation. You define additional codes specific to your business. All codes logged to ERP for reporting and analysis.
Dashboard and reporting
Real-time visibility into calling activity. See who was called, when, call duration, outcome, sentiment detected. Review calls requiring human follow-up. Track payment promise fulfillment. Analyze patterns in disposition codes by customer segment or aging category.
Human Control & Override:
You select which accounts get called each session. You can pause calling at any time. You can exclude specific customers from calling (strategic accounts, sensitive situations). You review agent prompts and adjust language. The agent assists your process, doesn't replace your judgment.
Consistent Performance:
Real-time visibility into calling activity. See who was called, when, call duration, outcome, sentiment detected. Review calls requiring human follow-up. Track payment promise fulfillment. Analyze patterns in disposition codes by customer segment or aging category.
Real Results
Representative outcomes from voice agent implementations:
Call Volume Increase:
Companies typically handle 3-5x more collection calls with voice agents than with human callers. A team making 50-60 calls weekly can now make 200-250 calls weekly. This means every past-due account gets contacted systematically, not just large balances.
Staff Time Reallocation:
Collection staff time spent on routine calls reduced 60-70%. A team spending 20 hours weekly on calls now spends 6-8 hours on complex accounts, disputes, and payment arrangements. Agent handles routine payment requests and promise tracking.
Consistency Improvement:
Every customer receives the same professional, courteous approach. No variable tone based on caller mood. No unconscious bias. No fatigue effect. Customers report more consistent and professional collection experience.
Sentiment Data Insights:
Voice agent tracks sentiment patterns across customer base. You can identify which customers consistently respond cooperatively vs. defensively. This informs credit decisions and account management approach. Patterns invisible in manual calling become visible and actionable.
Documentation Quality:
Complete records of every call with exact disposition codes and sentiment analysis. No missing notes. No forgotten details. Full audit trail for compliance and performance analysis.
DSO Improvement:
Systematic calling on all past-due accounts (not just large ones) typically produces 15-25% DSO reduction. Smaller accounts that previously waited for attention now get timely calls. Early contact prevents accounts from aging deeper.
Working Capital Impact:
For a $200M company with 50-day DSO, reducing to 40 days frees approximately $5.5M in working capital. For a $100M company, 10-day improvement frees $2.7M.
Staff Satisfaction:
AR teams report higher job satisfaction when voice agent handles routine calls. Staff focus on relationship management, problem-solving, and complex accounts. Less time spent on repetitive, emotionally draining calls.
Aspect | Voice AI Agent | Human Callers |
|---|---|---|
Coverage | All past-due accounts systematically | Prioritizes large balances |
Staff time requirement | 6-8 hours weekly (oversight) | 15-20 hours weekly (calling) |
Disposition coding | Automatic, standardized | Manual, inconsistent |
Escalation handling | Immediate transfer with context | Natural handoff |
Best for | Routine payment requests, high volume | Complex negotiations, relationships |
Call volume capacity | 200-250 calls weekly | 50-60 calls weekly |
Tone consistency | Consistent every call | Varies by mood, fatigue |
Emotional neutrality | No emotions, no bias | Unconscious bias, emotional reactions |
Documentation quality | Complete, automatic, coded | Variable, depends on diligence |
Sentiment capture | Analyzed and logged every call | Rarely documented |
What Implementation Looks Like
Timeline: 6-8 weeks from kickoff to production
Week 1-2: Discovery
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Document current collections process
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Define customer segmentation rules (tiers, payment terms)
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Establish escalation thresholds (aging, amount, attempts)
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Configure ERP data access
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Set up communication channels
Week 3-5: Development
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Build agent decision logic
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Integrate with ERP aging data
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Configure communication templates
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Set up escalation routing
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Create dashboards and reports
Week 6: Testing
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Deploy to test environment
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Process sample aging report
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Review agent decisions and communications
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Refine rules based on feedback
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Test escalation workflows
Week 7-8: Pilot deployment
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Deploy to production with limited scope (typically one customer segment or aging category)
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Monitor all agent decisions initially
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Refine rules based on real results
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Gradually expand scope
Your involvement:
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2-3 stakeholder meetings (kickoff, design review, go-live planning)
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ERP access configuration (read access to AR data, write access for communication logging)
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Review and approval of communication templates
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Testing and feedback during pilot phase
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Weekly check-ins during first month of production
Ongoing Maintenance:
2-4 Hours Monthly Requirement
Agent monitors its own performance and suggests rule adjustments. You review and approve changes quarterly or as needed.
Common Questions
Getting Started
We recommend a 90-day pilot starting with one customer segment or aging category. Common approaches:
Option 1
Customer Tier Pilot
Start with tier 2 or tier 3 customers (mid-volume, standard terms). Excludes strategic accounts that receive high-touch service.
Option 2
Invoice Size Pilot
Start with invoices under $5,000 or $10,000. High volume, lower individual risk, clear measurement opportunity.
Option 3
Aging Category Pilot
Start with accounts 15-45 days past due. Excludes very fresh aging (might pay without contact) and severe aging (already in escalation process).