Kept-promise rate
Promises matched to the correct amount and date, not promises merely captured.
B2C collections use case · Promise recovery
AI follow-up for broken payment promises: detect, recover, and escalate the next action
AI follow-up for broken payment promises compares the promised amount and date with actual payment evidence, then chooses the next permitted action. The agent can verify a pending transfer, request a receipt, reschedule within policy, call after WhatsApp non-response, or escalate repeated failure, dispute, or hardship with the full account history.
Reviewed: Aug 17, 2026Voice + WhatsApp + CRM/ERPB2C focus
01 / Problem
Where this workflow creates value
The customer previously committed to pay, but the promise date passed without a matched payment. A generic reminder can damage trust when a transfer is merely pending, while a collector may waste time reconstructing the prior conversation. The agent should resolve the evidence gap first and only then decide how to follow up.
- Consumer portfolios with large volumes of promises-to-pay across several channels
- Teams where promised payments and bank or ERP transactions reconcile slowly
- Operations that escalate from WhatsApp to voice after non-response
- Collections leaders measuring kept promises instead of raw contact volume
02 / Workflow
From eligible account to recorded outcome
Detect the missed commitment
Compare the promised amount, date, and account with posted payments, pending bank evidence, reversals, and reconciliation status.
Result: A verified broken promise or a payment-matching exception—not an assumption.
Reconstruct the promise context
Load the original conversation, terms, channel, consent, prior failures, dispute history, and reason given by the customer.
Result: The next contact acknowledges what was actually agreed.
Choose the recovery action
Request a receipt, resend a link, ask for a revised date within policy, initiate a voice callback, or route the case to a collector.
Result: A proportionate next action based on evidence and customer history.
Handle exceptions
Pause and escalate when the customer reports payment, disputes the balance, signals hardship, or repeatedly breaks promises.
Result: Sensitive cases leave automation with a complete decision packet.
Update priority and schedule
Write the outcome back, close false misses, update the account risk, and calculate the next allowed contact.
Result: Promise performance becomes usable operating data.
03 / Control
Automate without losing authority
Minimum controls
- Payment-status verification before labeling a promise as broken
- Frequency caps and suppression after payment, dispute, opt-out, or human ownership
- Limits on how many times the agent may reschedule without review
- Consistent history across WhatsApp, voice, CRM, ERP, and collection systems
- Supervisor queue for repeated failure, vulnerability, hardship, complaints, and data conflicts
How SOBERAN fits
SOBERAN connects voice and WhatsApp with identity, policy, CRM, ERP, and the human queue. The agent can execute the routine path through approved tools; exceptions preserve evidence, ownership, the decision, and structured writeback.
04 / Measurement
Metrics that prove resolution
False-miss rate
Promises flagged as broken when payment was already made, pending, misapplied, or awaiting reconciliation.
Recovered-promise rate
Broken promises that become verified payments or valid revised commitments after follow-up.
Time to next action
Elapsed time between verified promise failure and the next policy-compliant contact or review.
Repeat reschedule rate
Accounts repeatedly moved to a new date without payment, useful for tightening escalation policy.
The demo test
Use an account whose promise expired yesterday but whose bank transfer is pending reconciliation. The agent should check payment evidence before contacting the customer. Then remove the pending payment: it should reference the prior promise, offer only the allowed recovery action, and update the account without duplicating outreach.
05 / FAQ
Evaluation questions
How does AI detect a broken promise to pay?
It compares the promised amount and date with posted payments, bank or receipt evidence, reversals, and reconciliation status. A safe system verifies this evidence before changing account risk or contacting the customer.
What should happen after a customer misses a promise?
The next action depends on payment evidence, consent, prior failures, customer response, policy, and sensitivity. The system may request proof, resend a link, allow a bounded reschedule, switch channels, or escalate to a human.
Why measure false broken promises?
Contacting someone who already paid creates complaints and brand damage. False-miss measurement exposes reconciliation delays, identity mismatches, and bad system integration that conversation automation alone cannot solve.
06 / Sources
Evidence and references
- Hyperbots collections copilot
Current market evidence for promise tracking, prioritization, disputes, follow-up, and ERP updates in collections.
- SOBERAN collections prioritization
SOBERAN workflow for next-best collections action, promises, escalation, and system writeback.
- SOBERAN collections ROI calculator
Transparent calculator for modeling recovery, operating cost, and payback assumptions.
07 / Next
Other AI collections workflows
B2C collections use case · Days 1–30
Early delinquency
AI collections for early delinquency use voice and WhatsApp agents to contact eligible customers during the first 1–30 overdue days, explain the verified balance, offer policy-approved payment options, capture a payment or promise, and update the collection system. Sensitive, disputed, or unaffordable cases move to a person with context.
B2C collections use case · Negotiation
Payment-plan negotiation
AI payment-plan negotiation lets a voice or WhatsApp agent propose dates, installments, or settlement options that have already been approved for a customer segment. The agent verifies affordability inputs, explains the offer, captures explicit acceptance, schedules follow-up, and writes the plan back. Anything outside policy requires human approval.
B2C collections use case · Payment evidence
Payment-receipt validation
AI payment-receipt validation lets a customer send a transfer receipt through WhatsApp, extracts the amount, date, payer, reference, and destination, and compares them with the account and bank or ERP evidence. Clear matches suppress further contact and move to reconciliation; duplicates, altered images, partial payments, or mismatches go to review.
Find the leakage in your promise-to-pay loop
Bring an anonymized sample of kept, broken, and disputed promises. We will map payment matching, follow-up rules, channel escalation, false misses, and the authoritative writeback.
30 minutes · Anonymized data · One concrete workflow