Finance organisations were hemorrhaging $1.2M annually on contact centres drowning in repetitive queries, while legacy IVR systems with 800–1,200 ms delays frustrated customers and drove up abandonment rates. Strict PCI DSS compliance added layers of cost and complexity, and agent churn near 75% meant continuous recruiting and retraining cycles. By deploying a no-code, PCI-compliant voice AI platform with sub-300 ms latency, the team automated 85% of routine calls—slashing operating costs by 46% and achieving payback in just 1.5 months. Customer satisfaction jumped 25 points as response times fell from minutes to seconds, transaction volumes rose 25%, and days sales outstanding improved by 40%.

Case Study Source: Retell AI (retellai.com)

Problem Statement

Finance organisations were overspending on contact centre operations dominated by repetitive calls, while legacy IVR delays and strict PCI DSS requirements strained compliance and customer experience.

Goal

Deploy PCI‑compliant voice agents with sub‑**300 ms** responses to automate routine finance calls, reduce spend by **30–40%**, and lift customer satisfaction.

Challenges

Average enterprise spend of **$1.2M** per year on contact centres, with **60–70%** tied to routine queries that are ripe for automation.

Legacy IVR latency at **800–1,200 ms**, causing delays, abandonment and costly escalations.

Significant PCI DSS compliance burden (separate tools, audits, training, and hardened infrastructure).

High people costs and churn: **$35k–$50k** per FTE, **6–8 weeks** onboarding costing **$8k–$12k**, and around **75%** annual turnover.

Reliability and accuracy risks in voice AI (external API latency, hallucinations, and speech recognition challenges with accents/noise).

Actions

Implemented a no‑code voice agent platform with native HIPAA/PCI options and <**300 ms** voice‑to‑voice latency.

Designed core flows: identity verification via voice biometrics, real‑time balance queries, PCI‑compliant card capture, and automated dispute creation.

Integrated with existing telephony and systems (e.g., Twilio, Vonage, SIP, Cal.com, Make, n8n) and enabled knowledge‑base auto‑sync.

Enabled real‑time transcription, sentiment tracking, consent logging, audit‑ready reporting, and live QA dashboards.

Set up warm transfers with clear escalation triggers so complex or high‑value cases reach human agents with full context.

Ran a pilot on **10–20%** of calls, tuned responses, latency and escalation thresholds, then scaled automation in **~20%** weekly increments.

Impact:

Manual staffing needs reduced by **30–40%** as automated channels complete more customer tasks.

Friction‑free journeys delivered a **25%** uplift in transaction volumes, easing inbound call loads.

Always‑on service improved cash collection, with days sales outstanding down by **40%**, and boosted retention.

The Challenge

Financial services firms were haemorrhaging money on call centres. Most businesses spent roughly $1.2 million each year handling queries, yet two-thirds of that budget went toward answering the same basic questions over and over.

The old interactive voice systems didn’t help. Response delays stretched beyond a full second—sometimes reaching 1,200 milliseconds—frustrating callers and driving them to hang up or demand a live agent. Meeting strict payment-card security standards added another layer of expense through specialist tools, regular audits, and locked-down infrastructure.

Staff costs compounded the problem. Each agent earned between $35,000 and $50,000, took six to eight weeks to train at a cost of up to $12,000, then often left within the year. Turnover hovered around 75% annually, creating a perpetual recruitment treadmill.

The Solution

The team introduced a no-code voice platform built specifically for regulated industries. Response time dropped to under 300 milliseconds—fast enough that conversations felt natural. Native payment-security features meant no separate compliance stack to maintain.

They built automated workflows for the most common tasks: checking account balances, verifying identity through voice patterns, capturing card details securely, and logging disputes. The platform plugged directly into existing phone systems and synced with internal knowledge bases, so agents always had current information.

Real-time transcripts, mood tracking, and consent records gave managers visibility into every interaction. When a call grew too complex, the system handed off to a person—with full context already passed along.

Rather than flip a switch across the entire operation, the pilot ran on just 10 to 20% of incoming volume. After fine-tuning speed and escalation rules over a few weeks, automation expanded by roughly a fifth each week until it covered the full queue.

What Changed

Cost and Speed

The annual call-centre bill fell 46% after deployment. Investment paid back in six weeks, delivering a first-year return of 720%. Many similar programmes break even within two to three months.

Callers who used to wait two or three minutes now heard an answer in 30 seconds. The system resolved 85% of enquiries without involving a person.

Experience and Revenue

Customer satisfaction scores jumped 25 points. Instant availability and smoother journeys meant people actually enjoyed calling in—or at least didn’t dread it.

The improvements rippled outward. Staffing requirements dropped by up to 40%. Smoother experiences drove a 25% rise in transaction volume, which paradoxically reduced pressure on the queue. Round-the-clock service accelerated collections, cutting outstanding payment days by 40% and improving customer retention.

The Insight

What stands out here isn’t the technology itself—it’s the disciplined rollout. Starting small and scaling in measured increments let the team learn and adjust without risking the entire operation. The platform’s speed mattered less than its ability to hand off intelligently; keeping humans in the loop for edge cases preserved quality while still capturing the vast majority of efficiency gains. For finance teams drowning in repetitive queries, that pragmatic approach turned a daunting transformation into a series of manageable steps.