FINANCE

B2B Payment & Billing Revenue Leakage Analytics

Find the billing patterns quietly draining collectible revenue.

Enterprise finance and revenue operations departments lose millions annually to uncollected invoices, billing discrepancies, delayed payment cycles, and subtle churn signals. Finance teams can upload raw payment gateway logs, ERP billing histories, contract terms, and dunning communication records. The data preparation engine cleans messy invoice line items while relational metrics calculate the intersecting factors between payment terms, client industry, and late payment probability.

Steeped AI maps nuanced concepts across free-text billing dispute notes and customer correspondence, including pricing confusion language, contract term misalignment, auto-renewal frustration, and payment method friction, with a consistency manual accounts receivable review at scale can never achieve. Finance directors isolate why specific account tiers delay payment, then automate targeted collection workflows, adjust credit terms, and plug the leak.

Base Column
dispute_note ▾
Value Column
leakage_signal ▾
Base Column dispute_note ×Value Column ×
41%ofdispute_note=pricing_confusion
flag for review
revenue impact
100%
flagged count
533
invoice count
1,300
see examples
27%ofdispute_note=term_misalignment
flag for review
revenue impact
76%
flagged count
351
invoice count
1,300
see examples

Data Preparation

Use Steeped AI's data preparation to clean messy invoice descriptions, fix inconsistent date formats, and handle missing values across sprawling ERP billing logs. When one charge is written six ways across three systems, Steeped AI reconciles it before any leakage analysis runs.

Invoice lines from 3 ERP systems were merged into one schema
Date formats DD/MM and MM/DD were normalized
Payment terms "Net30", "NET 30" and "n30" were unified
Removed column "legacy_memo" because 88% were blank
Duplicate dunning entries for one invoice were merged
generating new dataset
NEW DATA

AI Topic Mapping

Use Steeped AI's AI topic mapping to turn free-text dispute notes and customer correspondence into measurable concepts like pricing confusion, term misalignment, and auto-renewal frustration. The reason behind a late payment becomes a column you can cross-tabulate against account tier.

pricing confusionterm misalignmentrenewal friction

Customer Journey Mapping

Use Steeped AI's journey mapping to give every invoice a measurable path from issue through dunning to payment or write-off. See exactly which step adds days to days-sales-outstanding, so collection effort targets the stage that actually stalls.

dispute at day 34invoicepaidDSO 47d

Relational Metrics

Steeped AI's automated metric breakouts calculate the intersecting relationships between invoice payment terms, client contract size, and payment delay probability. Thousands of hardcoded metrics run before you open the platform, with AI never touching the calculation itself.

Predictive Power Score

Steeped AI's regression predictive power pinpoints which early payment behaviors, such as a partial payment on the first invoice, most strongly predict an eventual bad-debt write-off. Credit terms tighten on evidence rather than on a collector's hunch.

Expand
Rank Late-Pay Predictors
Partial First Pay
49%
Net 60 Terms
38%
Disputed Line Item
30%
Manual Invoice
20%
Base Column
payment_behavior ▾
Value Column
write_off ▾
49%ofPartial First Pay=Write-Off
predictive power
90%
write-off count
147
account count
300
see examples

Talk to Your Data

Steeped AI's talk to your data lets a revenue operations lead ask which billing terms drive the lowest days-sales-outstanding and get ranked, mathematically verified findings back. Answers are pulled straight from the real ledger, never generated.

The insights are already in your data.

Ask your data anything. Get real findings ranked by impact, with AI reports your team can present and share on the spot.