UC15: Collections Prioritisation & Escalation

The Problem

Large enterprises with 10,000 to 50,000 active customers must prioritize collections efforts. Traditional aging bucket analysis (30/60/90 days) is insufficient; collectors waste time calling customers with strong historical payment records who will eventually pay, while chronically late payers are under-prioritized. Without intelligent prioritization, DSO remains 5 to 10 days longer than optimal, and 5 to 10% of invoices go to write-off despite being collectible.

What the Agent Does

Scores all past-due accounts using multi-factor model:
Historical payment probability (% paid within 30/60/90 days): weighted 40%
Customer lifetime value and profitability: weighted 20%
Days past due and aging bucket: weighted 15%
Dispute history and dispute reasons: weighted 10%
Industry/sector credit quality: weighted 10%, weighted 5%
Ranks all overdue accounts by probability-to-pay × outstanding amount (expected value of collection effort)
Stratifies into tiers:
Tier 1 (High Priority): High-probability/high-value (>$50K and >70% pay probability within 30d)
Tier 2 (Medium Priority): Medium-value or medium-probability
Tier 3 (Low Priority): Low-probability or very low-value (<$5K) or strong historical payer (likely to pay without intervention)
Recommends collection action by tier:
Tier 1: Immediate phone call from senior collector
Tier 2: Email + phone call within 3 days
Tier 3: Automated dunning email, no manual contact
For Tier 1 accounts >90 days overdue: Recommends credit hold on new orders
For Tier 1 accounts >180 days overdue: Flags for write-off review or escalation to legal/collections agency
Maintains weekly collections dashboard showing pipeline by tier, payment conversions, and DSO trend

Data Requirements

Data Sources:

ERP AR module , Open invoices, customer master, payment terms, aging
Payment history , 3 to 5 years of payment clearance records
CRM , Customer communication, dispute notes, relationship status
Customer master , Industry classification, customer tier, profitability data
GL , Write-off history, collections reserves

Data Classification:

Public: None
Internal Structured: AR aging, customer master, payment history, profitability data
Internal Unstructured: CRM notes, email communication, dispute documentation
External Third-Party: None
Sensitive/Regulated: Customer financial data (AR amounts), GDPR contact information

Data Quality Requirements:

Timeliness: Real-time AR aging, daily payment updates
Completeness: 98%+ of customers must have 24+ months of payment history; 95%+ must have profitability data
Accuracy: Payment history must match cleared transactions; profitability data must reconcile to GL

Integration Complexity: Medium , Requires AR/CRM data integration, customer master enrichment with profitability data, scoring model development, and collection dashboard

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	4	0.60
Error Reduction	10%	3	0.30
Cost Avoidance	10%	4	0.40
Strategic Leverage	5%	4	0.20
Data Availability	15%	4	0.60
Process Clarity	15%	4	0.60
Ease of Implementation	10%	3	0.30
Fallback Available	10%	4	0.40
Audience (Internal)	10%	4	0.40
Composite	100%		3.80

Why It Scores Well

Time optimization: Directing collection effort to highest-probability/highest-value accounts increases conversion and DSO reduction. DSO improvement: 3 to 5% DSO reduction from smarter prioritization × $500M AR = $4M to $7M cash flow improvement. Time savings: 30 to 40% reduction in low-value collection calls = 20 to 30 FTE hours/week freed for proactive outreach or other work.

Regulatory Alignment

None specific, but improves working capital and cash position

Sprint Factory Fit

Sprint 0 (2 weeks) + 1 build sprint (2 weeks)

Sprint 0 + 1 build sprint. Discovery focuses on scoring model factors, collection policies, and AR data quality. Sprint 0 covers AR/CRM data integration and basic scoring model. Build sprint focuses on model optimization based on pilot results and dashboard refinement.

Comparable Implementations

HighRadius Collections Management , Intelligent prioritization and collections orchestration
Billtrust Collections Analytics , Collections effectiveness and DSO optimization

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

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