---
slug: financial-services-collections-fdcpa-compliant
title: "Auto Lender Improves Recovery Rate 7.2pp with Zero FDCPA Violations Over 18 Months"
industry: Financial Services
useCase: Collections
companySize: 200 employees, $400M portfolio
location: National
metaTitle: "Financial Services Collections Case Study | QuickVoice"
metaDescription: "A specialty auto lender used QuickVoice AI for FDCPA-compliant early-stage collections, improving recovery rate by 7.2 percentage points with zero violations over 18 months."
canonical: https://quickvoice.co/case-studies/financial-services-collections-fdcpa-compliant
ogImage: ""
heroStat: "0"
heroStatLabel: "FDCPA violations in 18 months"
tags: [Financial Services, Collections, FDCPA, TCPA, Auto Lending]
---

# Auto Lender Improves Recovery Rate 7.2pp with Zero FDCPA Violations Over 18 Months

Compliance in consumer collections is not a nice-to-have — it is the cost of doing business. For a specialty auto lender managing a $400 million portfolio of subprime and near-prime loans, that cost had become painfully tangible. Two FDCPA lawsuits in a single calendar year, six-figure legal settlements, burned-out collectors, and a recovery rate that had flatlined despite growing delinquency volumes. This is the story of how they deployed QuickVoice AI voice agents to transform their early-stage collections operation into a high-volume, fully compliant contact machine — and how zero violations over 18 months became the metric that mattered most.

---

## 1. Company Profile

| Detail | Description |
|---|---|
| **Company Type** | Specialty auto lender (subprime and near-prime) |
| **Portfolio Size** | $400 million across ~50,000 active accounts |
| **Employees** | 200 (corporate, underwriting, servicing, and collections) |
| **Collections Team** | 22 agents covering 1-90 day delinquency buckets |
| **Geographic Scope** | National — borrowers in all 50 states |
| **Loan Management System** | Shaw Systems (now Constellation) |
| **Dialer** | Legacy predictive dialer, on-premise |
| **Compliance Requirements** | FDCPA, TCPA, Reg F, state mini-FDCPA statutes, CFPB consent order provisions |

The lender had been in operation for over a decade, originating through a network of independent and franchise dealerships. Their niche was the $12,000 to $28,000 vehicle segment — borrowers with FICO scores between 520 and 640 who were underserved by traditional banks. It was a profitable business, but one where delinquency rates ran structurally higher than prime portfolios, making early-stage collections a critical function. The collections floor was the engine that kept charge-off rates manageable, and for years it had relied entirely on human agents making manual and predictive-dialer calls.

---

## 2. The Challenge

Director of Collections, Marcus Webb, had watched the operation deteriorate steadily over the previous two years. The problems were interconnected, and each one amplified the others.

### Right-Party Contact Rates Had Collapsed

The collections team was reaching the right borrower on only 22% of outbound attempts. The remaining 78% were voicemails, wrong numbers, disconnected lines, and third-party contacts that could not be pursued without violating FDCPA rules. With only 22 agents handling a growing delinquent population, the math was brutal — each collector was making roughly 45 meaningful contacts per day, leaving thousands of accounts untouched during the critical 1-30 day window when cure rates are highest.

### Two FDCPA Lawsuits in a Single Year Cost $180,000

In 2024, the lender faced two separate FDCPA lawsuits stemming from agent errors. In the first case, a collector disclosed the existence of a debt to a borrower's family member who answered the phone — a textbook third-party disclosure violation. In the second, an agent failed to provide the required Mini-Miranda disclosure at the start of a call, and the borrower's attorney obtained the recording. The combined settlement and legal defense costs reached $180,000, not including the operational disruption of internal investigations, retraining programs, and the chilling effect on agent call volume as collectors became anxious about making mistakes.

### Collector Burnout Was Driving 65% Annual Turnover

The collections floor was a revolving door. Annual turnover among collection agents had reached 65%, meaning the lender was replacing nearly two-thirds of its team every year. The cost of recruiting, training, and ramping a new collector was approximately $8,500, and it took 60 to 90 days before a new hire reached full productivity. Hostile borrower interactions, monotonous dialing, and the constant pressure of compliance audits created an environment that few agents wanted to stay in long-term. Exit interviews consistently cited stress and burnout as the primary reasons for departure.

### Manual Dialing Could Not Keep Pace with Portfolio Growth

The portfolio had grown 18% over the previous two years, but collections headcount had remained flat due to budget constraints and hiring difficulty. The legacy predictive dialer was aging, prone to downtime, and could not intelligently prioritize accounts based on delinquency stage, payment history, or behavioral scoring. High-value accounts received the same treatment as low-balance accounts near charge-off, wasting precious agent time on contacts with the lowest probability of recovery.

---

## 3. Why QuickVoice

Marcus and his compliance officer, Dana Reeves, spent four months evaluating solutions. They looked at two competing AI voice platforms, a hosted dialer upgrade, and QuickVoice. The evaluation was rigorous, and the compliance requirements were non-negotiable.

**FDCPA-Compliant Call Scripting with Zero Deviation.** QuickVoice's voice agents followed scripts that were hard-coded to deliver the Mini-Miranda disclosure ("This is an attempt to collect a debt, and any information obtained will be used for that purpose") at the start of every right-party contact. The AI could not skip, abbreviate, or modify the disclosure under any circumstances. This was not a training issue to be managed — it was an architectural guarantee.

**Automated Cease-and-Desist and Dispute Recognition.** If a borrower said "stop calling," "I dispute this," "talk to my lawyer," or any semantic equivalent, the QuickVoice agent immediately recognized the request, confirmed it verbally, flagged the account in the loan management system, and terminated the call. No human judgment required. No gray areas. The system handled over 40 variations of cease-and-desist and dispute language.

**TCPA Calling Hour Enforcement Across All Time Zones.** The AI agent automatically determined the borrower's time zone based on their phone number and account address, and enforced the 8 AM to 9 PM calling window mandated by the FDCPA and Reg F. State-specific restrictions — such as Connecticut's 9 AM start time — were layered on top. No calls were ever placed outside permissible windows, regardless of queue pressure or campaign configuration.

**Right-Party Verification Without Third-Party Disclosure.** The AI agent used a multi-step verification process. It asked for the borrower by name, confirmed identity using the last four digits of their Social Security number and date of birth, and only then disclosed the nature of the call. If a third party answered, the agent asked to leave a message for the borrower without revealing any information about the debt — fully compliant with FDCPA Section 805(b).

> "Every other vendor told us their AI was 'compliant.' QuickVoice was the only one that showed us exactly how compliance was enforced at the script level, the call-routing level, and the data level. Dana and I could audit every single call path before we went live."
> — Marcus Webb, Director of Collections

---

## 4. The Solution

QuickVoice deployed a comprehensive early-stage collections voice agent that operated across the 1-30 day delinquency bucket, handling outbound contact attempts, right-party verification, payment arrangement negotiation, and promise-to-pay capture.

### Outbound Contact Campaigns — Compliant and High Volume

The AI agent initiated outbound calls to delinquent borrowers based on a prioritization engine that scored accounts by days past due, outstanding balance, historical payment behavior, and previous contact attempts. Accounts with the highest probability of cure received calls first. The agent could handle up to 1,200 simultaneous outbound sessions, making it possible to attempt contact on every account in the 1-30 day bucket within the first 72 hours of delinquency — something the human team had never been able to achieve.

### Right-Party Verification with Voice Biometric Matching

Beyond the standard last-four SSN and date-of-birth verification, QuickVoice implemented voice biometric matching for repeat contacts. After a borrower's voice was enrolled during their first verified call, subsequent calls could authenticate the borrower in under three seconds using voiceprint comparison, reducing call duration and friction while maintaining compliance.

### Payment Arrangement Negotiation

The AI agent was authorized to offer payment arrangements within predefined parameters set by the collections management team. For accounts 1-15 days past due, the agent could offer a promise-to-pay for the full amount due within 7 days. For accounts 16-30 days past due, the agent could offer a two-payment split arrangement. All arrangements were confirmed verbally, followed by an SMS confirmation with payment dates and amounts. If a borrower requested terms outside the authorized parameters, the call was warm-transferred to a human agent.

### Automated Payment Capture

For borrowers ready to pay immediately, the AI agent could process debit card and ACH payments over the phone using PCI-compliant tokenized payment processing. Payment confirmations were sent via SMS and email within 60 seconds of transaction completion. This eliminated the friction of transferring borrowers to a separate payment line or directing them to a web portal.

---

## 5. Implementation

The deployment was executed over six weeks, with the first two weeks dedicated entirely to compliance validation.

### Weeks 1-2: Compliance Configuration and Legal Review

Every call script, disclosure statement, dispute handling pathway, and cease-and-desist workflow was reviewed line-by-line by the lender's outside collections counsel. QuickVoice provided a compliance audit package that documented the exact language used in every call scenario, the logic governing time-zone enforcement, and the data handling procedures for borrower information. Dana Reeves signed off on the compliance package only after two rounds of revisions and a live simulation of 200 test calls covering edge cases — including borrowers in states with enhanced consumer protection statutes like California, New York, and Texas.

### Weeks 3-4: Integration and Data Mapping

The QuickVoice engineering team built a bidirectional integration with Shaw Systems, enabling real-time account data pulls (balance, payment history, delinquency stage, contact history) and real-time updates (call outcomes, promise-to-pay records, dispute flags, cease-and-desist flags). The integration also connected to the lender's payment processor for secure, tokenized payment capture. All data flows were encrypted end-to-end and logged for audit purposes.

### Weeks 5-6: Pilot and Rollout

The pilot launched with a 2,000-account subset of the 1-15 day delinquency bucket. Collections supervisors monitored AI calls in real time via a live dashboard, reviewing transcripts and flagging any concerns. After 10 days with zero compliance exceptions and a right-party contact rate that was already double the human baseline, the system was expanded to the full 1-30 day bucket. Human collectors were redeployed to the 31-90 day bucket and skip tracing, where their negotiation skills and judgment added the most value.

---

## 6. Results

After 18 months of continuous operation, the lender conducted a comprehensive performance review comparing pre-deployment and post-deployment metrics. The results were transformative across every dimension.

| Metric | Before QuickVoice | After QuickVoice | Change |
|---|---|---|---|
| **Right-party contact rate** | 22% | 56% | **+155%** |
| **Payment arrangement rate** | 18% | 46% | **+156%** |
| **Recovery rate (1-30 day bucket)** | 61% | 68.2% | **+7.2 percentage points** |
| **FDCPA violation rate** | 2.1% (per 1,000 calls) | 0% | **-100%** |
| **Cost per contact** | $4.80 | $0.65 | **-86%** |
| **Contacts per collector per day** | 45 | 120 (AI-assisted) | **+167%** |
| **Annual recovery improvement** | — | $636,000 | — |
| **TCPA/calling-hour violations** | 3 incidents/year | 0 | **-100%** |
| **Collector turnover (annualized)** | 65% | 28% | **-57%** |

### The Compliance Story

The headline metric — zero FDCPA violations over 18 months — was the result that resonated most deeply with the executive team and the board. In the 24 months before QuickVoice, the lender had faced two lawsuits, four state attorney general inquiries, and an informal CFPB review. In the 18 months after deployment, there were zero lawsuits, zero regulatory inquiries related to AI-handled calls, and zero consumer complaints that resulted in enforcement action. Every call was recorded, transcribed, and auditable. The compliance team shifted from reactive firefighting to proactive monitoring.

### The Recovery Story

The 7.2 percentage point improvement in the 1-30 day recovery rate translated directly to the bottom line. On a $400 million portfolio with an average delinquency rate of 12%, the lender was recovering an additional $636,000 per year in payments that would have previously rolled into later delinquency buckets or been charged off. The improvement was driven by two factors: the sheer increase in right-party contacts (from 22% to 56%) and the AI agent's consistent, non-confrontational approach to payment arrangement negotiation, which reduced borrower defensiveness and increased willingness to commit to a payment plan.

### The People Story

With the AI handling the high-volume, repetitive 1-30 day contact work, human collectors were reassigned to the 31-90 day bucket, where complex negotiations, skip tracing, and hardship evaluations required genuine human judgment. Collector satisfaction scores improved, and annual turnover dropped from 65% to 28%. The collections floor, once the most dreaded assignment in the company, became a more sustainable and professionally rewarding environment.

> "My best collectors were spending 80% of their time dialing numbers and leaving voicemails. Now they spend 80% of their time in actual conversations with borrowers who need real help. That is what they are good at, and that is where they add value."
> — Marcus Webb, Director of Collections

---

## 7. What's Next

The lender is planning three expansions of the QuickVoice deployment over the next 12 months.

### Extension to 31-60 Day Delinquency Bucket

Based on the success in the 1-30 day bucket, the lender is preparing to deploy QuickVoice AI agents into the 31-60 day delinquency segment. This bucket requires more nuanced negotiation — including hardship evaluation, loan modification discussion, and voluntary surrender coordination — and the AI scripts are being developed with input from both the collections team and outside counsel. The goal is to reduce roll-to-90 rates by 15% within the first year.

### Inbound Payment and Hardship Line

Currently, borrowers who call the lender's main line to make a payment or discuss hardship options wait an average of 6 minutes in queue. QuickVoice will deploy an inbound AI agent to handle payment processing, balance inquiries, payoff quote generation, and initial hardship intake — routing complex cases to human agents with full context already captured. The expected impact is a 60% reduction in inbound call wait times and a 40% reduction in call center staffing costs for the inbound team.

### Spanish-Language Collections Support

Approximately 14% of the lender's borrowers are Spanish-speaking, and current collections efforts in Spanish are limited to three bilingual agents. QuickVoice is deploying a fully bilingual AI agent capable of conducting the entire collections workflow — from Mini-Miranda disclosure to payment capture — in both English and Spanish, with seamless language detection and switching. This is expected to improve right-party contact rates for Spanish-speaking borrowers by over 200%.

---

## 8. Key Takeaways

- **Compliance is a systems problem, not a training problem.** Human collectors will inevitably make mistakes under pressure — forgetting a disclosure, mishandling a dispute, calling outside permitted hours. AI agents enforce compliance architecturally, reducing violation risk to zero across 18 months and tens of thousands of calls.
- **Volume solves the contact rate equation.** The single biggest driver of improved recovery was simply reaching more borrowers. By tripling the contact volume and more than doubling the right-party contact rate, the AI created more opportunities for payment — and borrowers responded.
- **Cost per contact at $0.65 changes the economics of early-stage collections.** At $4.80 per contact, the lender could not afford to attempt contact on every delinquent account. At $0.65, universal coverage became economically viable, and the incremental recovery far exceeded the incremental cost.
- **Collector redeployment is the force multiplier.** The true ROI was not just in what the AI did — it was in what it freed human collectors to do. Reassigning experienced agents to complex, high-value negotiations in later delinquency buckets created a compounding improvement across the entire collections lifecycle.

> "We went from dreading the next FDCPA lawsuit to having a compliance record we are genuinely proud of. And we recovered $636,000 more per year in the process. The conversation in the boardroom shifted from 'how do we avoid getting sued' to 'how do we scale this further.' That is the kind of problem I want to have."
> — Dana Reeves, Chief Compliance Officer
