UC9: Predictive Collections & Dunning Agent

The Problem

Traditional collections strategies (FIFO, largest-balance-first) treat all late payers equally. In reality, some customers have consistent payment delays but always eventually pay, while others are chronic disputes or credit risks. Manual collection prioritization is biased and inefficient. Companies miss DSO reduction opportunities and waste collection effort on low-probability accounts.

What the Agent Does

Analyzes 3 to 5 years of customer payment history: on-time percentage, average days late, payment volatility
Builds predictive model to score customers by probability-to-pay within 30/60/90 days
Segments customers into risk tiers (high-probability, medium-risk, high-risk/dispute)
Generates dunning strategy for each tier:
High-probability: 1 email reminder at +15 days, polite tone
Medium-risk: 2 emails (+10 days, +20 days) + 1 phone call request
High-risk: Immediate escalation to AR manager or credit hold on new orders
Drafts personalized dunning emails using NLP, referencing customer's specific payment patterns
Prioritizes collections queue by probability-to-pay × outstanding amount (expected value)
Tracks dunning effectiveness: conversion rates by segment, DSO improvement by customer
Generates weekly collections dashboard showing pipeline by risk tier and estimated cash flow

Data Requirements

Data Sources:

ERP AR module , Open invoices, aging, customer credit limits, payment terms
Payment history , 3 to 5 years of clearance data, on-time/late breakdown
CRM , Customer relationship data, contact information, escalation history
ERP GL , Collection write-offs and disputed amounts

Data Classification:

Public: None
Internal Structured: AR aging, customer master, payment history, collection terms
Internal Unstructured: CRM notes, email templates, dunning message templates
External Third-Party: None
Sensitive/Regulated: Customer financial data (DSO, credit scores), GDPR contact information

Data Quality Requirements:

Timeliness: Real-time AR aging, weekly payment history updates
Completeness: 98%+ of customers must have 24+ months of payment history
Accuracy: Payment clearance data must match bank clearing records

Integration Complexity: High , Requires ML model development, historical payment data integration, CRM integration for customer context, and email generation capability

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	4	0.60
Error Reduction	10%	4	0.40
Cost Avoidance	10%	4	0.40
Strategic Leverage	5%	4	0.20
Data Availability	15%	4	0.60
Process Clarity	15%	3	0.45
Ease of Implementation	10%	3	0.30
Fallback Available	10%	4	0.40
Audience (Internal)	10%	4	0.40
Composite	100%		4.15

Why It Scores Well

Financial impact is quantifiable: 20% DSO reduction across $500M AR = $27M cash flow improvement. Time savings: 50+ FTE hours weekly on collections work. Collectability improves because high-risk accounts are identified early for credit hold or write-off decisions.

Regulatory Alignment

None specific, but improves working capital and cash position

Sprint Factory Fit

Sprint 1 (4 weeks) + 1 to 2 build sprints (4 weeks)

Sprint 1 + 1 to 2 build sprints due to ML model development complexity. Sprint 1 focuses on data extraction, feature engineering, and historical model training. First build sprint focuses on model validation and pilot deployment. Second build sprint focuses on dunning template optimization and feedback loop integration.

Comparable Implementations

HighRadius Autonomous Finance , 20% DSO reduction through AI-driven collections prioritization
Billtrust AI Collections , Predictive collections and automated dunning

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From zero to a governed, production agent in 6 weeks.

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