How To Boost Call Center Efficiency By Cutting Average Handling Time With AI

1. Overview

If you run a contact center in a bank, credit union, or fintech, Average Handling Time (AHT) isn’t just a metric on a wallboard—it’s the tempo of your operation. The good news: AHT is one of the most malleable levers you can pull for efficiency, and AI voice agents give you new ways to compress minutes without cutting corners on compliance or customer empathy. The trendlines are with you: enterprise AI adoption keeps rising (78% of organizations used AI in at least one function in 2024), which means your peers are laying the groundwork for automation at scale. 

The call center AI market was valued at $2.1B in 2024 and is projected to grow at ~18.9% CAGR—a positive signal that tooling, ecosystems, and best‑practice patterns are maturing fast enough for regulated industries to benefit. Early movers get compounding advantages: better datasets, cleaner integrations, and faster iteration cycles. 

This post shows how to cut AHT using AI voice agents—with a focus on Sei AI, a platform built specifically for regulated finance (not generic “enterprise” AI). I’ll ground the guidance in accurate definitions, hard‑won patterns, and visible differentiators so you can act with confidence.

2. What Is Average Handling Time (AHT)?

Plain‑language definition: AHT is how long, on average, it takes to handle a customer interaction end‑to‑end—talk time + hold time + after‑call work (ACW)—divided by the number of handled contacts.

Formula:

How Contact Centers Currently Work (And Why It’s Hard):

• Customers wait too long. Across sources, the “good” AHT often lands around 6 minutes, though banking and credit union calls can run longer as complexity rises. Benchmarks commonly cite ~6:10 overall and ~6–9 minutes for financial services.   
• Costs stack with minutes. Every extra minute inflates labor and telecom costs while eroding occupancy and service levels. (Most centers also track a target ASA ≤ ~30 seconds.) 
• Regulatory friction adds time. Disclosures, consent capture, and authentication are non‑negotiable in finance (think TCPA hours, UDAAP‑sensitive scripts, adverse‑action clarity)—each adds seconds that matter at scale. 
• Human QA is sampled. Many teams still review <5% of conversations, which means coaching and compliance gaps linger and reappear. (We’ll see how Sei shifts this from 5% to 100% coverage.) 
• System swivel‑chair. Agents hop across CRMs, loan/servicing systems, and knowledge bases; every context switch bloats ACW.
• Variability is the enemy. Two agents, same scenario, different flows—AHT swings wide without workflow standardization.

Three Trends Shaping Voice AI (Technically And Operationally):

• Real‑time voice stacks mature. Sub‑second streams across STT → LLM → TTS (with barge‑in, streaming ASR/TTS, and response chunking) are becoming typical, bringing latency down toward the ~300–500 ms range that feels “human.” 
• Agentic orchestration, not just “FAQ bots.” Modern agents call tools, follow policies, and update records mid‑call, which compresses ACW and reduces hand‑offs. (We’ll contrast horizontal IVAs vs. finance‑specialized agents shortly.) 
• Compliance‑aware AI becomes table stakes. Regulators have made it clear: existing laws apply to AI. Successful deployments bake in consent, disclosures, record‑keeping, and auditability from design day one. 

3. Challenges In Reducing Average Handling Time

• Complex Requests: Mortgage forbearance, escrow analysis, fraud disputes—resolution needs context from multiple systems and policy checks.
• Identity & Consent: Authentication, consent capture, and TCPA timing (8am–9pm local) add non‑negotiable steps. Automating these without error is hard. 
• Swivel‑Chair ACW: Agents summarize, disposition, and re‑key data into CRM/servicing systems—minutes that pile up after every call.
• Inconsistent Scripts & Disclosures: Even great agents occasionally miss key lines; QA is often sample‑based and late. 
• Legacy IVR Fatigue: DTMF trees slow containment and push easy tasks to live agents, inflating AHT across queues.

4. Benefits For Financial Institutions To Adopt AI To Optimise Their AHT

• Faster Verification & Disclosures: Voice AI can verify identity, check consent windows, and read the right disclosures dynamically, shaving 30–120 seconds while keeping you in‑bounds for TCPA/UDAAP. 
• Reduced ACW Through Automation: Auto‑summaries, tagging, and CRM updates cut post‑call wrap time; agents move to the next interaction faster.
• Higher First‑Contact Resolution (FCR): Policy‑aware agents complete transactions mid‑call—due‑date changes, balance inquiries, payment setups—reducing transfers and callbacks. 
• Uniform Compliance Coverage: Shift from spot‑checks to 100% monitoring across voice, chat, and email to catch misses early and coach proactively. 
• Resilience For Spikes: AI absorbs seasonal peaks (tax time, rate moves, program changes), stabilizing ASA and AHT without over‑staffing.
• Better CX Without Fear: When AI handles the repetitive steps, human agents spend time on empathy and judgment—AHT falls and CSAT/NPS can rise together. (Industry references still peg “good” AHT near six minutes; the goal is both faster and better.) 
• Auditability & Governance: Structured logs of what was said, what was shown to the customer, and what systems were touched—proving your process under scrutiny.

5. How Are Voice AI Agents Transforming AHT Management?

The Game‑Changer: Policy‑Aware, Real‑Time Voice Agents.

Instead of static IVRs or generic chatbots, these agents listen, reason, act, and document—all within your policy guardrails.

• Authentication Without Drag: AI can orchestrate voiceprint or KBA, detect risky behavior, and flow to safe fallbacks—seconds saved without weakening controls. (Many credit unions report major verification time cuts with modern auth.) 
• Barge‑In + Streaming = Natural Pace: With barge‑in and streaming STT/TTS, customers don’t wait for the bot to finish sentences; the agent adjusts mid‑utterance, trimming dead air. 
• Inline Tool Use: Agents update CRM, check LMS/servicing systems, and take payments while still on the call—reducing transfers and ACW. 
• Auto‑Summaries & Dispositions: When the call ends, the notes and next steps are already in your systems, not in a notepad.
• Continuous Compliance: Every exchange is checked against policies and regulations (think UDAAP‑sensitive language, fee disclosures, adverse action reasons), surfacing coaching opportunities now, not next month. 
• Outbound Respectfully Optimized: AI schedules callbacks within TCPA windows and at the customer’s preferred time, cutting voicemail tag and abandoned connects. 
• Low‑Latency Core: Sub‑second end‑to‑end cycles (STT → LLM → TTS) keep conversations fluid; ~300–500ms is the usability line many teams aim for. 

6. Features Of AI‑Powered Virtual Agents

Below are five cornerstone features and how Voice AI improves each for regulated finance.

6.1 24/7 Availability

• Always On, Always In‑Policy: Nights, weekends, storms—AI stays within compliance scripts and latest policy packs.
• Deflects Routine Volume: Billing questions, due‑date changes, payoff quotes—more handled at the edge.
• Protects ASA/AHT During Spikes: No more 45‑minute queues when a program changes drop on Friday afternoon.

6.2 Multilingual Support

• Serve Diverse Communities: Language support widens access without adding bilingual headcount.
• Consistent Disclosures: The same policy‑exact phrasing across languages reduces regulatory risk.
• Routing For Edge Cases: Escalate to bilingual specialists only when nuance demands it.

6.3 Scalability On Demand

• Elastic Capacity: Handle rate shocks (e.g., storms, system outages) without over‑provisioning staff.
• Queue Health: Keep ASA and AHT in check even at 10× baseline volume.
• Cost Control: Pay for usage; reinvest savings into higher‑touch journeys.

6.4 Integrated Workflows

• Two‑Way System Actions: Read/write to CRM, ticketing, loan/servicing, and payments; cut transfers and ACW.
• Disposition By Design: Auto‑classification, complaint tagging, and follow‑up tasks created mid‑call.
• Trigger Management: Exceptions (e.g., vulnerability flags, disputes) raise alerts instantly.

6.5 Compliance & Auditability

• Policy‑Aware Dialog: Guardrails prevent unauthorized disclosures, risky promises, or missing scripted lines.
• 100% Recording & Search: Find the one call that matters today, not after a sampling cycle.
• Regulatory Proof: Tie every decision to evidence—what was said, which system confirmed a balance, when consent was captured—and by whom.

7. How Sei AI’s Voice Agents Are Improving AHT

There are many strong horizontal IVA platforms (e.g., Five9 IVA, NICE Enlighten Autopilot, Genesys Voicebots). They excel at broad self‑service and integrate deeply into contact center suites. Sei AI is different by intent: it’s purpose‑built for regulated finance, trained on consumer‑finance regulations and enforcement actions, and ships with compliance‑first guardrails. That focus shows up in both features and operating model. 

• Compliance‑First Models Trained On Finance: Sei says its agents are trained on UDAAP, FCRA, TILA, HMDA and CFPB enforcement actions, with strict guardrails to prevent unauthorized disclosures—crucial for reducing AHT safely. 
• Adheres To TCPA & Consumer‑Protection Rules: Outbound and scripted flows are designed to respect calling windows and disclosures while optimizing connect rates at the customer’s preferred time—cutting voicemail tag that inflates handle time. 
• Multi‑Channel, Finance‑Ready: One agent across voice, chat, and email, so a borrower who starts in email can finish in voice without repeating themselves—less re‑capture, lower AHT. 
• End‑To‑End Workflows (Beyond FAQ): Sei’s browser agents execute collections steps, due‑date changes, and payments, and update CRM/ticketing automatically—shrinking ACW and giving humans time for high‑judgment tasks. 
• 100% Conversation Monitoring: Move from sampling to full coverage across calls, chats, and emails—flag policy breaches, score agents, and trigger coaching without waiting on monthly QA cycles, so improvements hit this week’s AHT. 
• Complaints Intelligence Across Channels: Centralize internal and external complaints (e.g., CFPB, BBB, app‑store reviews) and score severity, letting operations fix root causes that otherwise stretch AHT via repeat contacts. 
• Security, Privacy, And Auditability: SOC 2 Type 2, GDPR‑ready, private VPC deployments, and 100% auditability are highlighted—essential when AI is handling sensitive conversations at scale. 
• Latency & Scale, With A Purpose: Sei partners with Cerebras for fast inference; the result: ~60% latency reduction, 40% runtime reduction, 2× feature headroom, and a shift from <5% manual QA to 100% automated monitoring—capabilities that directly support lower AHT and tighter governance. 
• Integration Footprint For Finance: Connectors for payment processors, loan management systems, and CCaaS are part of the approach, minimizing swivel‑chair time and preventing the “AI says X, system says Y” loop that stretches calls. 

8. Proven Impact

Sei AI publishes (and partners publish) outcomes that map directly to AHT and CX efficiency:

• AHT Reduction: 60% reduction in handle times reported on Sei’s site, with product copy noting reductions up to 75% depending on workflow design. 
• NPS Lift: +75% increase in NPS cited as a trust metric alongside adoption stats. 
• Full Conversation Coverage: Move from <5% sampled QA to 100% automated analysis across channels—closing coaching and compliance gaps that quietly inflate AHT. 
• Scale & Volume: 500,000+ processed tickets and security certifications indicate production maturity. 
• Operating Cost Impact: Sei indicates up to 70% cost savings by automating repetitive workflows for CX, compliance, and ops—consistent with the efficiency side of AHT gains. 

Reality check: Benchmarks for “good” AHT hover around ~6 minutes overall, and banking centers often sit between 6–9 minutes today; the winning play is reducing AHT while maintaining or improving FCR, CSAT, and compliance. AI voice agents built for finance make that balancing act easier.   

9. Tl;dr

• AHT Is The Tempo You Can Tune: Treat it as a system problem (verification, disclosures, ACW)—not a stopwatch.
• Voice AI Is Ready For Finance: Sub‑second barge‑in conversations, inline tool use, and 100% QA coverage can compress minutes safely. 
• Sei AI’s Differentiator: Compliance‑first agents trained on UDAAP/FCRA/TILA/HMDA and CFPB actions—plus SOC 2 Type 2 and auditability—fit regulated institutions out of the box. 
• Impact, Not Hype: Sei reports 60% AHT reduction, +75% NPS, and 100% monitoring—with latency gains via Cerebras under the hood. 
• Early Movers Win: Banks and credit unions adopting policy‑aware voice agents now bank compounding gains in data, integrations, and training loops. (AI adoption is already mainstream.) 

10. FAQ Section

10.1 What Are Practical Ways To Reduce AHT In A Call Center Without Hurting CX?

Start by mapping the minute: where time goes to ID&V, disclosures, lookups, and ACW. Introduce a voice agent to handle identity, policy‑exact scripts, and system updates during the call. Ensure barge‑in and sub‑second latency so customers don’t wait. Finally, implement 100% QA monitoring to coach faster and prevent rework that inflates AHT. 

10.2 Where Does AI Help First In Banking, Credit Union, And Fintech Call Centers?

High‑volume, policy‑heavy flows: payment arrangements, due‑date changes, balance/escrow checks, card disputes, fraud holds, application status. AI compresses ACW with auto‑summaries and updates records mid‑call, shaving minutes while keeping scripts consistent.

10.3 How Exactly Does AI Improve Customer Service (Beyond Speed)?

AI improves consistency, accuracy, and availability (24/7). It reduces the need to repeat information between channels, and it catches compliance misses that cause grievances and repeat calls. Industry benchmarks still peg “good” AHT near six minutes, but the sweet spot is faster with higher FCR and CSAT. 

10.4 What’s The Official AHT Formula Again?

AHT = (Talk Time + Hold Time + After‑Call Work) ÷ Total Calls. Track by queue and scenario; a five‑minute card‑activation call and a ten‑minute fraud‑dispute call belong in different cohorts. 

10.5 How Is Sei AI Different From Generic AI Voice Platforms?

Sei is finance‑first: agents are trained on UDAAP, FCRA, TILA, HMDA and CFPB enforcement actions; SOC 2 Type 2, GDPR‑ready, and 100% auditability are part of the package. Horizontal IVAs (e.g., Five9, NICE, Genesys) are strong foundations but generally require custom compliance overlays for regulated finance. 

Appendix: Side‑By‑Side Positioning—Horizontal IVA Suites vs. Finance‑Specialized Agents

• Horizontal Suites (e.g., Five9 IVA, NICE Enlighten Autopilot, Genesys Voicebots)
  • Strengths: Deep CCaaS integration, broad channel coverage, multilingual capability.
  • Typical Work To Fit Finance: Layering policy packs, disclosures, TCPA rules, and audit workflows; tuning for UDAAP risks.   
• Finance‑Specialized (Sei AI)
  • Strengths: Compliance‑first training, 100% monitoring, end‑to‑end financial workflows, SOC 2 Type 2, and low latency; integrations with payments, LMS/servicing, and CCaaS. 

Implementation Notes For Practitioners

A few pragmatic steps I recommend when you bring a finance‑grade voice agent into production:

1. Write The “One‑Minute Map.” For your top three call types, write down where each minute goes (ID&V, disclosures, lookups, ACW). That map becomes your backlog.
2. Codify The Policy Pack. Centralize disclosures, prohibited phrases, escalation triggers, and TCPA time windows. Tie each item to a policy ID so QA can trace it. 
3. Instrument Latency. Track P50/P95 from first word heard → first audio back. Under 500 ms feels natural; over 800 ms adds awkward pauses that creep back into AHT. 
4. Automate ACW To Zero. Enforce that the agent writes the summary, disposition, tags, and follow‑ups into your CRM or servicer before the call ends.
5. Shift From Sampled QA To 100%. Use full‑coverage monitoring to catch misses and coach in‑week—not after a month. (This is where Sei’s posture is notably strong.) 
6. Pilot, Then Scale Intelligently. Start with payment arrangements or due‑date changes, then expand to the top‑5 intents. Keep human escape hatches and clear supervisor alerts for edge cases.

Sources & Notes

• Adoption & Market: AI usage across organizations (78%)—Stanford HAI AI Index 2025. Call center AI market size & growth—Global Market Insights. 
• AHT Benchmarks & Formula: AHT formula—Convin; typical AHT ~6 minutes—Sprinklr / LiveAgent; Banking/call center benchmarks—Nextiva and Bridgepointe (banks & credit unions).   
• Real‑Time Voice Tech: Latency targets and streaming—AssemblyAI; barge‑in—Gnani. 
• Regulatory Context: TCPA hours & consent (national and state nuances); CFPB—“existing laws apply to AI.” 
• Sei AI (Product & Results): Compliance‑first training (UDAAP, FCRA, TILA, HMDA, CFPB actions); SOC 2 Type 2, auditability; 60% AHT reduction; +75% NPS; integrations; “reduce AHT by up to 75%” claims 

Why This Matters Now

Recent Supervisory Highlights have even called out long hold times and understaffed centers in servicing—reminders that efficiency isn’t optional in regulated finance. The opportunity is to lower AHT responsibly, with policy‑aware automation that stands up in audits and delights customers who just want answers, not menus.