TL;DR

• Voice AI isn’t here to “kill” IVR—it’s here to complement it and take the hard, high-friction workflows off your agents’ plates.
• For regulated FIs, the tradeoff is not just cost; it’s auditability, consent, disclosures, and operational resilience—areas where Sei AI is purpose-built. 
• Benchmarks put live inbound calls at ~$6.9–$7.2 per call on average, with many centers targeting 5–8% abandonment and struggling to keep IVR containment high. That’s the math Voice AI can meaningfully bend. 
• Telephony minutes still matter (pennies per minute add up at scale), but most of the savings come from containment, faster resolution, and fewer escalations/repeats—and from compliance-safe automation.  

1) Overview

• Legacy IVR shines at structured routing and high-volume, predictable menus. It’s great for account balance, simple payments, or branch lookups.
• Voice AI agents interpret natural language, fetch data, complete tasks end-to-end, and log the right disclosures/consents—critical in regulated journeys.
• Across industries, most companies report IVR containment ≤30%, which means a large share still lands with a human—expensive and slow during peaks. Voice AI’s job is to lift self-service containment and shorten the escalations that do occur. 
• For financial services, the differentiator isn’t clever small talk; it’s policy-aware execution: honoring TCPA consent, GLBA privacy, and ECOA/Reg B notification workflows—with an audit trail. 
• You don’t have to rip and replace: think co-existence. Keep IVR for simple paths; insert Voice AI where intent varies, documents are referenced, or disclosures must be read.
• The outcome you’re aiming for: containment up, abandonment down, resolution time and repeat contacts down—and every second meets your compliance standard.

2) Cost anatomy: where the money actually goes

• Live-agent inbound call cost averages ~$6.9–$7.2 per call (industry studies vary). This is the anchor cost Voice AI competes with. 
• Telephony minutes: U.S. local calling typically runs fractions of a cent to low cents per minute; volume tiers matter. At scale, pennies are profit. 
• Labor: not just wages—coaching, QA, shrinkage, schedule inflex, and re-work from repeat calls.
• IVR platform: licenses + menu maintenance + change windows + analytic tooling.
• Voice AI stack: telephony + ASR/TTS/LLM inference + orchestration + observability + redaction + secure connectors (CRM/LMS/LOS/CCaaS).
• Compliance: TCPA consent capture/lookup, pause-resume/redaction for card data, call recording policies, audit exports—these reduce legal exposure and QA time. 
• Operational drag: repeats from misrouting, missing disclosures, or back-office handoffs—often invisible until you measure them.

3) The hidden costs (and savings) nobody budgets for

• Abandonment: many centers target ~5–8%; in practice this swings widely with queue times and menu depth. Voice AI reduces “dead time” and menu churn. 
• Repetition: contact-center research pegs mean inbound cost ~$6.9 and shows repetition adds real dollars across billions of calls—automation that summarizes, confirms, and validates reduces this. 
• Containment slippage: when IVR fails to resolve, it double-pays (IVR + agent). Voice AI either completes the task or escalates with full context so the agent’s handle time drops. 
• Compliance re-work: missing or mis-timed ECOA/Reg B or Reg E steps force repeats and manual remediation; automation guards against misses. 
• Investigations: when something goes wrong, the audit trail (what was read, recorded, redacted, consented) determines time-to-closure; agents rarely document as cleanly as software.
• After-call work (ACW): Voice AI writes the summary, reasons, dispositions, and next steps—less swivel-chair, fewer errors.
• Training drift: high turnover means re-training; Voice AI “remembers” disclosures and flows so quality stays consistent.

4) What Voice AI changes technically (without the hype)

• Understanding: Voice AI uses ASR + NLU/LLM to parse free speech, not just DTMF—so callers say what they want; no memorized menus.
• Action: Agents are wired to perform work (collect payments, validate identity, change due dates, send statements) via secure integrations—in regulated finance, that work must include proper consent, disclosures, and logging. 
• Guardrails: In regulated use cases, models are trained and constrained on UDAAP/FCRA/TILA/HMDA context and CFPB actions, with strict privacy controls. 
• Observability: every turn is auditable—who said what, which disclosure was read, the exact wording, time stamps, and downstream system updates. 
• Co-existence with IVR: best practice is front-door triage or targeted transfers from IVR to Voice AI for intent-heavy flows (loss-mitigation, escrow inquiries, disputes), while keeping simple IVR options intact.
• Security: PCI telephone-payment guidance and PCI DSS v4.x expectations apply—tokenization, redaction, pause/resume as needed. 
• Telephony economics: minutes still bill, but containment and faster resolution are the big line items Voice AI moves. 

5) Compliance-grade design: the regulated FI checklist

• TCPA consent flow: record source, timestamp, purpose, one-to-one consent expectations, and offer clear opt-out—store proof with the interaction. 
• GLBA privacy: minimize data, access control by role, encrypt in motion/at rest, and present notices as required in your channels. 
• Reg E (error resolution): allow error notices by phone and log them properly; Voice AI should create the required artifacts and route to the right queue within your timelines. 
• ECOA/Reg B: when adverse action is involved, capture oral notice elements and ensure written notice workflows trigger correctly—again, log and export. 
• PCI DSS telephone payments: use pause/resume and redaction correctly; prefer flows that keep card data out of contact center systems entirely (dual-tone entry, secure links). 
• Records & exports: regulators (and internal audit) will ask for who/what/when. Automate redacted transcripts, disclosure logs, and consent proofs into your data lake.
• Change management: show policy versioning (when a disclosure script changed and why) and how the agent updated same-day.
• Vendor posture: prioritize SOC 2 Type II, auditability, and private VPC deployment options. 

6) A simple TCO model you can reuse

Assumptions (illustrative):

• Annual inbound calls: 1,000,000
• Average live-agent inbound cost: $7.00/call (industry mid) → $7,000,000 baseline. 
• Telephony: $0.0085–$0.0140/min; assume 4-minute self-service calls (voice AI or IVR) → ~$0.034–$0.056 per call. 
• Baseline IVR containment: 30%; Voice AI target: 55–65% (program-dependent). 

Back-of-the-envelope:

• Legacy IVR (30% contain): 300k calls×$0.05 (telephony) ≈ $15k + 700k escalations×$7 ≈ $4.9M → ~$4.915M.
• Voice AI (60% contain): 600k self-service×$0.05 ≈ $30k + 400k escalations×(reduced agent cost via shorter AHT; say 15% lower) ≈ $2.38M → ~$2.41M direct vs. ~4.92M.
• Not shown: reductions in abandonment, repeat contacts, ACW, and QA time, plus compliance re-work avoided (material but program-specific).

👉 The point isn’t the exact dollar; it’s the shape: as containment rises and escalations get shorter (better context and summaries), OPEX curves bend fast. Minutes matter, but workflow outcomes pay the real bills. 

7) “Best For” — how to decide where to start

• Mortgage & Servicing: escrow questions, payoff statements, payment changes, loss-mitigation triage, early delinquency nudges (with consent & disclosures).
• Retail Banking & Credit Unions: card disputes (capture Reg E notices), balance/transaction inquiries, travel notices, fee explanations with standardized scripts. 
• Collections & Recoveries: compliant right-party contact, payment arrangements, hardship/vulnerability surfacing—always consent-aware and logged.
• Insurance: FNOL intake, claims status, premium billing questions with accurate disclosures and secure payments.
• Capital Markets/Fintech: account support where GLBA and internal policy require strong privacy practices. 
• Cross-channel QA: even if you start with a small voice flow, monitor 100% of calls/chats/emails for policy adherence on day one. 

8) The rollout plan: 30–90 days with concrete milestones

• Weeks 0–2 — Scoping & Security
  • Pick one high-volume, policy-heavy use case.
  • Data-protection review (SOC 2 Type II, VPC, data flows).
  • Map disclosures/consents/notifications needed; define KPIs (containment, AHT, abandonment, error-notice capture rate). 
• Weeks 2–4 — Integrations & Guardrails
  • Connect CCaaS/CRM/LOS/LMS; enable redaction / pause-resume where payments apply.
  • Configure policy packs and scripted disclosures; stand up dashboards. 
• Weeks 4–6 — Controlled Pilot (10–20% of volume)
  • Measure containment, handoff completeness, compliance hits/misses; train escalation summaries.
  • Target 4–6 week pilot cadence common in AI programs; IVR changes may take longer planning cycles—plan both tracks. 
• Weeks 6–12 — Scale & Expand
  • Expand to 50–70% of eligible calls if KPIs hold; add a second adjacent use case.
  • Turn on 100% monitoring for agents (scorecards + coaching), even outside the pilot path. 

9) The Sei AI toolkit (numbered) for regulated financial institutions

All modules below are built with a compliance-first posture, trained on financial-services regulations, and deliver audit-ready artifacts. Links reference Sei’s product pages for details. 

1. Compliant AI Voice & Chat Agents

• Automate inbound/outbound calls and chats in days, wired into your systems to complete tasks (payments, due date changes, identity verification, status). 
• Policy-aware conversations trained on UDAAP, FCRA, TILA, HMDA, with guardrails to prevent unauthorized disclosures.
• Multi-channel orchestration with consistent quality and compliance across voice/chat/email.
• End-to-end workflows (collect payment, update CRM, log disclosures) without swivel-chair.
• Bring your policies and SOPs; tailor prompts and flows to your rulebooks. 
• Best for: collections outreach, servicing FAQs, disputes intake, refinance pre-qualification hand-offs.
• Outcomes to expect: higher containment, shorter escalations, and cleaner audit trails.

2. Call Monitoring & QA (100% coverage)

• No more spot checks—monitor all calls/chats/emails for policy adherence and customer outcomes. 
• Track complaints & vulnerability signals; alert in real time.
• Auto scorecards and coaching opportunities surfaced per interaction.
• 30+ compliance dimensions classification (e.g., disclosures, advice risk, AML cues).
• Integrates with your stack to ingest and report automatically.
• Best for: heads of QA/compliance who need consistent, audit-ready evidence.

3. Complaints Tracker (internal + external)

• Aggregate calls, chats, emails, plus CFPB, BBB, Trustpilot, app stores—one pane of glass. 
• Context-aware labeling lowers false positives; severity scoring for triage.
• Alerts & trending to catch issues before they escalate.
• Redaction of PII while preserving context for investigation.
• Best for: CX leaders, risk & compliance teams, Legal.

4. Underwriting & QC Agents (Mortgage-ready)

• Ingest and assemble unstructured loan files, annotate, and check against Fannie/Freddie/HUD guidelines. 
• Flag discrepancies in real time to reduce back-and-forth with borrowers.
• Context-aware rules to minimize false positives.
• Outcome: loans to close in days, not weeks—with clean evidence trails.
• Best for: lenders and IMBs rationalizing turn-times and QC fail rates.

10) KPIs that prove it worked

• Containment (self-service completion)
  • Baseline: many IVRs ≤30% containment.
  • Target: +15–35 points depending on use case maturity. 
• Abandonment Rate
  • Target band: 2–8% depending on segment and seasonality. 
• Average Handle Time (escalations)
  • -10% to -25% with context-rich handoffs and automated summaries.
• First-Contact Resolution / Repeat Contacts
  • +5–15 points when agents receive structured next steps and commitments.
• Compliance Signals
  • 100% disclosures logged; 100% consent proofs retrievable; 100% error-notice captures routed within timelines. 
• QA Throughput
  • 100% interactions scored, coaching items auto-queued, time-to-insight measured in hours, not weeks. 

11) FAQ for banks, servicers, credit unions, insurers, and collections teams

Q: How do you handle TCPA consent?

A: Store consent source, timestamp, and purpose; enforce one-seller/one-purpose expectations; provide clear opt-out and maintain suppression lists. Every outreach call includes a consent check before dialing. 

Q: How do payments by phone stay PCI-conformant?

A: Use pause/resume and redaction where needed, prefer tokenized flows that keep PAN out of agent/recording systems, and encrypt in transit/at rest. Follow PCI SSC telephone-payment guidance and DSS v4.0.1. 

Q: Can the agent capture Reg E error notices by phone, properly?

A: Yes—Voice AI can capture an oral error notice, log required fields, and route into compliant workflows with timestamps and case IDs. 

Q: What about ECOA/Reg B adverse-action notifications?

A: The agent can provide oral notice, capture address for written notice, and trigger the appropriate written communication workflow with full evidence. 

Q: How fast can we go live?

A: Narrow use cases can launch in days; broader production programs typically follow 4–6 week pilots with progressive scale-up to 90 days. (IVR changes often require longer change windows.) 

Q: What’s the actual cost lever? Minutes or models?

A: Minutes matter (they’re cheap but not free), yet containment and shorter escalations generate most savings. That’s why policy-aware workflows beat raw talk-time optimization. 

Q: Is Sei AI “enterprise-grade” enough for banks?

A: Sei AI is built for regulated finance, offers SOC 2 Type II, private VPC deployment patterns, and audit-first features across modules (voice agents, QA, complaints, underwriting). 

12) The game-changer

Real-time, compliance-grade orchestration.

When the agent can do the work (collect, confirm, disclose, document) and produce audit-ready evidence—in the same second—you unlock a service model that scales without trading off risk posture. That’s the difference between “a smarter IVR” and a regulated-finance Voice AI platform.

Appendix: Implementation notes, pitfalls, and quick wins

(Optional reading for the program owner who will get paged if a disclosure goes missing.)

Quick wins (start here)

• Pick a single use case with high volume and clear disclosures (e.g., escrow questions or payment extensions).
• Wire up pause/resume or tokenized collection for any payment flows on day one. 
• Turn on 100% QA across all interactions—even if your pilot is small—so you see policy misses across the contact center, not just inside the pilot. 
• Measure containment, abandonment, and repeat contacts weekly; publish results to Ops + Compliance.

Common pitfalls (and fixes)

• Ambiguous consent: normalize your consent data model before dialing; one source of truth per phone number. 
• Menu dead-ends: ensure IVR transfers to Voice AI preserve context (intent, account, last utterance) so escalations are shorter, not just different.
• Missing artifacts: ensure the platform exports transcripts, disclosure events, and consent proofs into your case system and data lake nightly.
• Payment leakage: if agents ever see full PAN, your scope balloons—prefer customer-entered details via secure capture. 

Why Sei AI for regulated financial institutions

• Compliance-first agents (trained on finance regs and CFPB actions) with strict privacy guardrails and multi-channel coverage. 
• Auditability as a feature: 100% monitoring, complaints tracking (internal + public sources), and evidence exports. 
• Mortgage-grade underwriting & QC: guideline-aware checks (HUD/Fannie/Freddie) that shorten cycles and reduce QC noise. 
• Security posture: SOC 2 Type II, private VPC, data isolation, and documented SLAs. 

Final validation (sources you can share with Legal & Finance)

• Cost baselines & economics: ContactBabel and partner analyses put mean inbound call cost around $6.9–$7.2, a solid benchmark for TCO modeling. 
• Telephony cost reality: U.S. voice minutes commonly price under two cents/min in volume tiers; it adds up, but containment and AHT are the bigger levers. 
• IVR containment challenge: many enterprises report ≤30% containment, motivating smarter automation. 
• Abandonment targets: leaders aim for 2–8% depending on vertical, seasonality, and staffing. 
• Compliance references: TCPA consent rules, GLBA privacy, Reg E error resolution by phone, ECOA/Reg B adverse-action workflows—these are the constraints your automation must honor and document. 

Ready to try this the “regulated-right” way?

If you want a hands-on walkthrough of Sei AI across Voice Agents, 100% QA, Complaints, and Underwriting/QC—with your disclosures and consent rules wired in—book a 30-minute working session and we’ll load one real use case end-to-end.