NewNew: The enterprise guide to Agentic AI — 24 min read.
Read →Banks, credit unions, fintechs and wealth firms. Every model separates automation savings from risk and remediation exposure, keeps human review on adverse decisions, and produces a scenario range instead of a single optimistic number.
Every calculator is ungated, gives conservative, base and aggressive scenarios, and exports an enterprise-styled PDF with your inputs.
Containment, dispute handling, automated QA coverage and remediation exposure in one auditable servicing model.
Open calculator →Voice AI, agent assist and automated QA across servicing, collections, card, deposit and lending inquiries.
Open calculator →Onboarding, KYC refresh, dispute packets and reconciliation queues — hours and dollars returned.
Open calculator →Three-year TCO for an in-house AI team versus platform plus partner delivery, including hiring, attrition and time to value.
Open calculator →Share of servicing and payment inquiries resolved without a queue.
Intake, documentation and status effort removed per dispute case.
Exposure reduction from full QA coverage and consistent first-pass decisions.
Earlier detection of systemic issues from 100% contact coverage.
Handle-time returned per contact, and where a licensed human must stay in the loop.
Blended cost per contact across channels, before and after automation.
Three financial services programmes where servicing, disputes and QA coverage were modelled with the same assumptions this calculator uses.
Card and deposit servicing containment with agent assist and automated QA across every contact.
Contacts contained
Contacts QA'd
Payback
"Full QA coverage was the part risk cared about. The savings were the easy half of the case."
SVP Contact Center, retail bank
Dispute intake and documentation automated, with adverse decisions routed to a human reviewer.
Dispute effort removed
Annualised benefit
Unsupervised denials
"Nothing adverse happens without a person. That single control made the model approvable."
Director of Member Operations, credit union
Onboarding, KYC refresh and payment-status inquiries automated across chat and voice.
Onboarding touches removed
Cost per contact down
CSAT
"We grew the book without growing the servicing team, and the audit trail improved."
Head of Operations, digital lender
Client names withheld under NDA. Outcomes are measured programme results, not calculator projections.
Bring your contact mix and dispute volumes. In 30 minutes we test the assumptions with you, map the controls your second line will require, and outline a 90-day path to a production-ready pilot.
They separate automation savings from risk-and-remediation exposure, and keep a human review gate on adverse decisions — so nothing in the model assumes an unsupervised outcome your regulator would question.
The servicing model treats automated QA coverage as risk reduction: consistent coverage across 100% of contacts lowers the probability and size of remediation, which is modelled separately from headcount savings.
Balance and transaction inquiries, card servicing, payment status, dispute intake and document collection — high-volume, deterministic intents with clean system-of-record integration.
Yes. The AI Build vs Buy TCO calculator models three years of in-house team cost including hiring and attrition against platform plus partner delivery.
