---
slug: financial-services-banking-customer-service
title: "Community Bank Automates 82% of Tier-1 Calls, Redirects Staff to Advisory Services"
industry: Financial Services
useCase: Banking Customer Service
companySize: 350 employees, 45,000 retail accounts
location: Midwest US
metaTitle: "Banking Customer Service Automation Case Study | QuickVoice"
metaDescription: "A 12-branch community bank used QuickVoice AI to automate 82% of tier-1 calls, cutting wait times by 95% and generating $420K/year in new advisory revenue."
canonical: https://quickvoice.co/case-studies/financial-services-banking-customer-service
ogImage: ""
heroStat: "82%"
heroStatLabel: "Tier-1 call automation rate"
tags: [Financial Services, Banking, Customer Service, Voice Biometrics, Jack Henry]
---

# Community Bank Automates 82% of Tier-1 Calls, Redirects Staff to Advisory Services

Community banks live and die by the quality of their customer relationships. But for a 12-branch institution in the Midwest, the call center had become a paradox — the very team responsible for maintaining those relationships was drowning in low-value, repetitive inquiries that left no time for the conversations that actually mattered. Balance checks, transaction lookups, card activations, and password resets consumed 65% of inbound call volume, while customers with complex financial needs waited in queue behind callers who just wanted to know if their direct deposit had posted. This is the story of how QuickVoice AI voice agents broke that cycle, automating 82% of tier-1 calls, slashing wait times by 95%, and freeing seven full-time employees to generate $420,000 in new advisory revenue.

---

## 1. Company Profile

| Detail | Description |
|---|---|
| **Institution Type** | Community bank (state-chartered, FDIC-insured) |
| **Total Assets** | $1.8 billion |
| **Branches** | 12 across a three-county metropolitan area |
| **Employees** | 350 (branch, corporate, and operations) |
| **Retail Accounts** | 45,000 (checking, savings, CD, money market) |
| **Commercial Accounts** | 3,200 |
| **Core Banking System** | Jack Henry Symitar (Episys) |
| **Call Center Staff** | 18 FTEs handling ~1,400 inbound calls/day |
| **Compliance Requirements** | GLBA, Reg E, BSA/AML, FFIEC authentication guidance |

The bank had been a fixture in the community for over 80 years, growing through a series of small acquisitions and organic branch expansion. Their competitive advantage had always been personal service — the branch manager who knew your name, the teller who asked about your kids, the loan officer who returned your call the same day. But the call center had become a bottleneck that threatened that reputation. Customers who called with simple questions waited 4 or more minutes to reach a human, and customers with genuinely complex needs — mortgage questions, estate planning, fraud disputes — waited just as long, stuck behind a queue of balance inquiries.

---

## 2. The Challenge

Chief Operating Officer Jennifer Park had been tracking call center performance for three years, and the trend lines were uniformly negative.

### 65% of Call Volume Was Tier-1 Inquiries That Required No Human Judgment

The bank categorized inbound calls into three tiers. Tier-1 calls were simple, transactional inquiries: balance checks, recent transaction lookups, card activation, PIN resets, branch hours and locations, and direct deposit confirmations. Tier-2 calls required some judgment or account modification: address changes, stop payments, fee disputes, and wire transfer requests. Tier-3 calls involved complex advisory or problem-solving: mortgage applications, commercial loan discussions, fraud investigations, and estate account management.

Analysis of six months of call data revealed that 65% of all inbound calls were Tier-1. These calls averaged 3.2 minutes each, including hold time, identity verification, and the actual inquiry. They required no decision-making from the agent — just the ability to look up information in the core banking system and read it back. Yet each one consumed the same agent resources as a complex advisory call.

### Wait Times Were Eroding Customer Satisfaction

The average wait time across all call types had reached 4.2 minutes, with peak periods (Monday mornings, month-end, tax season) pushing waits above 8 minutes. Customer satisfaction surveys, conducted quarterly, showed that the bank's overall CSAT had declined from 4.1 out of 5.0 to 3.8 over the previous 18 months. Free-text comments on the surveys told the story clearly: "I just wanted to check my balance and waited 6 minutes," "Why can't I get a simple answer without sitting on hold?" and "Love the bank but the phone service is getting worse."

### The Call Center Was the Bank's Largest Non-Interest Expense Line

With 18 FTEs, the call center represented an annual fully loaded cost of approximately $1.26 million — the single largest non-interest operating expense outside of branch operations. Every budget cycle, the COO faced the same impossible choice: add headcount to reduce wait times (increasing costs), or hold headcount flat and accept declining service levels. Neither option was strategically viable for a community bank competing against national institutions with massive digital self-service platforms.

### Missed Revenue Opportunity in Advisory Services

The bank's wealth management and financial advisory practice was understaffed and underperforming relative to its potential. The three-county market had a significant concentration of affluent retirees and small business owners — a demographic that valued personal advisory relationships. The bank had the licenses, the products, and the market opportunity, but not enough relationship managers to capitalize on it. Meanwhile, seven experienced call center agents had Series 6, Series 63, or insurance licenses that were going completely unused because their days were consumed by balance checks and card activations.

---

## 3. Why QuickVoice

Jennifer evaluated three solutions over a two-month period: upgrading the existing IVR system, deploying a digital banking chatbot, and implementing QuickVoice AI voice agents. The IVR upgrade was rejected because the bank's existing IVR had a 34% abandonment rate — customers hated navigating touchtone menus and would not tolerate a more complex version. The chatbot was rejected because 62% of the bank's customer base was over 55, and call volume data showed a strong preference for voice interaction over digital channels.

**Natural Conversational Interface.** QuickVoice's AI agents spoke conversationally, not in the stilted, menu-driven cadence of a traditional IVR. Customers could say "What's my checking balance?" or "Did my paycheck come in yet?" in natural language, and the agent understood and responded appropriately. Early testing with a focus group of 30 customers aged 55 to 78 showed an 89% successful interaction rate on first attempt.

**Voice Biometric Authentication with Knowledge-Based Fallback.** QuickVoice implemented voice biometric enrollment and matching that complied with FFIEC authentication guidance. After initial enrollment (during an opt-in call), returning customers could be authenticated in under four seconds by voiceprint alone. For customers who had not enrolled or whose voiceprint confidence was below threshold, the system fell back to last-four SSN and date-of-birth verification. This eliminated the frustrating "please enter your 16-digit account number" step that plagued the old IVR.

**Native Jack Henry Symitar Integration.** QuickVoice built a direct API integration with the bank's Episys core, enabling real-time balance lookups, transaction history pulls, card status checks, and account metadata retrieval. The integration was read-only for Tier-1 functions, minimizing operational risk, with write access limited to card activation and PIN reset workflows that had their own authentication and confirmation safeguards.

**Seamless Warm Transfer for Tier-2 and Tier-3 Calls.** When a customer's request exceeded Tier-1 scope, the AI agent did not simply dump them back into a queue. It collected the customer's identity, the nature of their request, and any relevant account details, then performed a warm transfer to the appropriate human agent — who received a screen pop with full context. The customer never had to repeat themselves.

> "We were not looking for a way to make our call center cheaper. We were looking for a way to make our call center smarter — so that human time went to human-value work. QuickVoice understood that distinction from the first conversation."
> — Jennifer Park, Chief Operating Officer

---

## 4. The Solution

QuickVoice deployed a Tier-1 voice automation platform that sat in front of the bank's existing call center, handling routine inquiries end-to-end and routing everything else to human agents with full context.

### Automated Tier-1 Call Handling

The AI agent handled the following Tier-1 functions without any human involvement:

- **Balance inquiries:** Checking, savings, money market, and CD accounts, including available balance and current balance.
- **Recent transaction lookups:** Last 5, 10, or 30 transactions, with amount, merchant or description, and date. Customers could ask for specific transactions ("Did a charge from Amazon post yesterday?") and the agent would search and respond.
- **Card activation:** New debit cards activated via voice confirmation after authentication, with the card status updated in Episys in real time.
- **PIN reset:** Temporary PINs issued via voice after enhanced authentication (voiceprint plus knowledge factor), with instructions to set a permanent PIN at the next ATM transaction.
- **Direct deposit confirmation:** Real-time check of whether a pending ACH credit had posted, with amount and date.
- **Branch hours and locations:** Nearest branch by zip code, holiday hours, and drive-through availability.

### Intelligent Call Routing for Tier-2 and Tier-3

For calls that required human intervention, the AI agent categorized the request during the conversation and routed it to the appropriate team. Fraud reports went directly to the fraud team. Wire transfer requests went to operations. Mortgage inquiries went to the lending team. Each transfer included a complete interaction summary, authenticated customer identity, and the specific nature of the request — eliminating the need for the customer to re-explain their issue.

### Voice Biometric Enrollment Campaign

To build the voice biometric database, QuickVoice worked with the bank to implement a voluntary enrollment process. During the first 90 days, every customer who called and was authenticated via knowledge factors was invited to enroll in voice biometrics. The enrollment took less than 15 seconds and required the customer to repeat a short passphrase. By the end of the 90-day campaign, 38% of active callers had enrolled, reducing average authentication time from 45 seconds to under 4 seconds for enrolled customers.

---

## 5. Implementation

The full deployment was completed in five weeks.

### Week 1: Core Integration and Security Review

The QuickVoice engineering team established the API connection with Jack Henry Symitar, working within the bank's existing middleware layer. The bank's Information Security Officer conducted a full security review, including penetration testing of the API endpoints, encryption validation (AES-256 at rest, TLS 1.3 in transit), and access control audit. The voice biometric system was configured to store voiceprints as encrypted mathematical models — not recordings — in compliance with Illinois BIPA and similar state biometric privacy laws.

### Week 2: Call Flow Design and Compliance Mapping

Call flows for all six Tier-1 functions were designed in collaboration with the call center manager and compliance officer. Each flow was mapped against GLBA privacy requirements, Reg E disclosure rules for electronic fund transfer inquiries, and the bank's internal customer information security policy. The warm transfer protocol was designed with the bank's existing Mitel phone system, ensuring call metadata and screen pops carried through seamlessly.

### Week 3: Agent Training and Dashboard Setup

While the AI system was being tested, the 18 call center agents received training on the new workflow. The training covered three areas: how to handle warm transfers from the AI agent (reading the context summary, not re-asking questions), how to monitor the AI dashboard for escalated calls, and how the voice biometric enrollment process worked so they could answer customer questions about it. Total training time was four hours per agent, spread across two sessions.

### Weeks 4-5: Phased Rollout

The AI agent went live on a single toll-free number during Week 4, handling only balance inquiries and branch location questions. Call center supervisors monitored every interaction in real time via the QuickVoice dashboard, reviewing transcripts and listening to call recordings. After five days with a 94% successful resolution rate on those two functions, the remaining four Tier-1 functions were activated. By the end of Week 5, all 12 branch numbers and the main toll-free line were routing through QuickVoice, with full Tier-1 automation and intelligent Tier-2/3 routing active.

---

## 6. Results

After six months of full operation, the bank conducted a comprehensive performance review. The results fundamentally altered the economics and strategic direction of the call center.

| Metric | Before QuickVoice | After QuickVoice | Change |
|---|---|---|---|
| **Tier-1 call automation rate** | 0% | 82% | **+82 percentage points** |
| **Average wait time (all calls)** | 4.2 minutes | 12 seconds | **-95%** |
| **Cost per call (Tier-1)** | $8.50 | $1.10 | **-87%** |
| **Call center FTEs** | 18 | 11 | **-39%** |
| **Staff redeployed to advisory** | 0 | 7 FTEs | — |
| **New advisory revenue (annualized)** | $0 | $420,000/year | — |
| **Customer satisfaction (CSAT)** | 3.8/5.0 | 4.3/5.0 | **+13%** |
| **Voice biometric enrollment** | 0% | 38% of active callers | — |
| **Average authentication time (enrolled)** | 45 seconds | 3.8 seconds | **-92%** |

### The Automation Story

The 82% Tier-1 automation rate meant that more than 900 calls per day were being handled end-to-end by the AI agent without any human involvement. The remaining 18% of Tier-1 calls that required human escalation fell into three categories: customers who specifically requested a human agent (8%), calls where the AI could not authenticate the customer after three attempts (6%), and edge cases involving account types or transaction formats not yet supported (4%). The bank plans to address the third category in future updates.

### The Revenue Story

Seven call center agents with financial services licenses — Series 6, Series 63, and state insurance licenses — were retrained over a 12-week period and redeployed as financial advisors and relationship managers. They were assigned to the bank's wealth management and retirement planning practice, focusing on customers with $100,000 or more in deposits or investments. In their first six months, these seven former call center agents generated $420,000 in annualized advisory and insurance revenue — fee income that went straight to the bottom line. The bank had turned a cost center into a profit center.

### The Customer Experience Story

Customer satisfaction rebounded sharply. The 12-second average wait time — driven by the AI agent's ability to answer instantly — eliminated the single most common complaint in customer surveys. Qualitative feedback was overwhelmingly positive, particularly among the 55+ demographic that the bank had worried might resist an AI interaction. Common survey comments included: "Much faster than before," "I just said what I needed and got an answer," and "I didn't even realize it wasn't a person at first."

> "Our customers are not tech-early-adopters — they are farmers, retirees, and small business owners who have banked with us for decades. The fact that they embraced the AI voice agent so quickly tells you everything about how natural and easy QuickVoice made the experience. Zero resistance. Just relief that they didn't have to wait on hold anymore."
> — Tom Dahlgren, Branch Network Director

---

## 7. What's Next

The bank is planning three additional QuickVoice deployments over the next year.

### Proactive Fraud Alert Calls

Currently, the bank's fraud detection system flags suspicious transactions and sends text alerts. Customers who miss the text often do not realize there is an issue until their card is declined. QuickVoice will deploy outbound AI voice calls for real-time fraud alerts, calling the customer within seconds of a flagged transaction to confirm or deny the activity. Confirmed fraud triggers an immediate card block and replacement order. The expected impact is a 40% reduction in fraud losses from delayed response.

### Commercial Account Services

The bank's 3,200 commercial accounts generate a disproportionate share of call volume relative to their numbers — treasury management questions, wire status inquiries, ACH batch confirmations, and positive pay exception handling. QuickVoice will deploy a dedicated commercial voice agent integrated with the bank's treasury management platform, handling routine commercial inquiries and freeing the commercial banking team to focus on relationship management and loan origination.

### Loan Application Status and Document Collection

Mortgage and consumer loan applicants currently call the bank an average of 3.2 times during the application process to check status and ask about required documents. QuickVoice will deploy an outbound and inbound AI agent that proactively calls applicants with status updates, requests missing documents via SMS follow-up, and answers common questions about the process — reducing applicant anxiety and loan officer call volume simultaneously.

---

## 8. Key Takeaways

- **Tier-1 automation is the highest-ROI entry point for banking AI.** Automating balance checks, transaction lookups, and card activations delivered immediate, measurable value — 87% cost reduction per call, 95% wait time reduction, and 13% CSAT improvement — with minimal operational risk.
- **Staff redeployment is where the strategic value lives.** The $420,000 in new advisory revenue generated by seven redeployed call center agents dwarfed the direct cost savings of the AI deployment. The real ROI was in converting licensed, experienced employees from cost-center roles into revenue-generating advisory positions.
- **Voice biometrics solve the authentication problem that kills IVR adoption.** The bank's old IVR had a 34% abandonment rate because customers refused to punch in long account numbers. Voice biometric authentication reduced authentication time to under 4 seconds and eliminated the single biggest source of customer friction.
- **Community bank customers will embrace AI if the experience is genuinely better.** The assumption that older, relationship-oriented customers would reject AI voice agents proved completely wrong. When the alternative is a 4-minute hold time, a fast and accurate AI interaction is not a compromise — it is an upgrade.

> "We spent years agonizing over whether to add headcount or accept worse service. QuickVoice gave us a third option we had not considered: automate the routine, redeploy the talent, and grow revenue instead of costs. Our call center went from our biggest operational headache to our most exciting strategic asset in less than six months."
> — Jennifer Park, Chief Operating Officer
