1. Get Crystal Clear on Your Objectives
Define success from the start. Pick one or two focus areas to keep vendors honest:
- Higher Contact Rates
Get more people to answer calls or reply to texts. - Improved Promise-to-Pay
Move more consumers to commit to a payment date. - Rock-Solid Compliance
Ensure every outreach follows legal scripts and call-time rules. - Better Customer Experience
Reduce disputes and complaints through empathetic, respectful messaging.
Having a narrow, measurable goal ensures every test and vendor conversation stays on track.
2. Run a Quick “Smoke Test” on Core Performance
Before deep dives, see if the AI really can handle your typical calls:
- Sample Scenarios
- Friendly reminder, firm payment request, dispute inquiry
- Compare the AI’s tone and accuracy against your best human agent or current bot.
- Key Metrics
- Accuracy: Does it answer questions correctly?
- Completion Rate: How often does a call lead to a next-step promise?
- Fallbacks: How often does it say “I don’t know” or hand off to a human?
If your LLM flunks these baseline checks, you’ll catch major red flags fast.
3. Test for Fairness and Bias
Collections need to be fair. Biased or tone-deaf messaging can hurt your reputation - and risk legal trouble.
- Uniform Script Use
Verify the AI treats high- and low-balance accounts, new and old delinquencies, and different regions with the same respectful tone. - Vulnerability Safeguards
Confirm it flags and handles seniors, service members, or hardship cases per your policy, rather than blasting everyone with the same script.
Ask vendors, “How do you train your model to ensure every customer gets equally respectful outreach?”
4. Check Robustness with Simple “Perturbations”
Inputs vary in the real world—your AI should stay consistent.
- Paraphrase Tests
“I can’t pay right now” versus “I’m unable to settle today”—does it give the right response both times? - Edge Cases
Try zero-balance, bankruptcy-flagged, or disputed accounts and verify it follows your exact protocol (e.g., “This account is disputed—please direct inquiries to our dispute team.”).
These nudge tests reveal if the model will “break” under slight real-world wording changes.
5. Audit for Policy Compliance
Regulations in collections are non-negotiable.
- Script Lockdown
Ensure the AI can only use approved language and can’t improvise unauthorized content. - Time-Window Enforcement
Confirm it never calls before 8 AM or after 9 PM (or whatever your local rules require). - Policy Edge Tests
Feed it “do not call” numbers or protected-status addresses - verify it still skips or uses the correct legal disclosures.
Request an “audit snapshot” from your vendor showing these tests in action.
6. Demand Explainability
Even non-experts need visibility into AI decisions.
- Decision Logs
After each call, get a brief summary: “Escalated because customer mentioned ‘dispute.’” - Version History
Maintain a changelog of model updates and policy tweaks - so you can trace any unexpected behavior back to its source.
Ready to Transform Your Collections Operation?
By focusing on clear objectives, basic performance and bias checks, robustness testing, policy audits, transparency, and continuous monitoring - and by demanding ironclad vendor promises - you can confidently deploy LLMs in your collections workflow. The right framework keeps customers treated fairly, protects your compliance profile, and unlocks the efficiency gains of cutting-edge AI.
Want to see how AI-powered collections can boost your recovery rates while staying compliant? Let's chat.
