Voice AI

Voice AI for Loan Collections—A Hands-On Playbook for Regulated Lenders

Ramkumar Venkataraman

29 Sep 2025 • 11 min read

AI for collections

No doom-and-gloom—just practical ways to modernize collections without breaking what already works.

TL;DR

Collections volumes are rising while consumer delinquencies remain elevated in 2025—which makes smarter, compliant outreach a priority for banks, credit unions, mortgage servicers, and fintech lenders.
Sei AI builds compliance-first voice and chat agents purpose-built for regulated financial institutions—trained on your policies and sector regulations (FDCPA/Reg F, UDAAP, RESPA, TILA, Fair Housing) and designed for full auditability.
The game-changer isn’t “bots that talk”—it’s compliance-by-design agent orchestration: every call, message, and payment path is governed by guardrails that encode your policies and the law, logged end-to-end for QA and exams.
Expect a 6–10 week pilot to production for targeted use-cases (e.g., broken promise-to-pay follow-ups, right-party contact scheduling, hardship intake), with measurable lifts in RPC and right-time contacts, and lower handle times driven by tight integrations to your LMS/CRM/CCaaS/payments stack. (Timelines below.)
This post is a field guide: architecture, modules you can turn on, metrics to track, a rollout plan, and an FAQ tailored to regulated lenders—so you can modernize collections without replacing your current strategy.

Why collections teams are adding Voice AI now

When I sit with collections leaders, two realities keep coming up: rising outreach workloads and a broader need to personalize at scale without adding risk. A glance at 2025 macro data explains why:

The New York Fed reports household debt at $18.39T in Q2 2025, with ~4.4% delinquent—“elevated” versus prior years. That pressure flows straight to early- and mid-stage collections.
Student-loan repayment resumption materially shifted delinquency dynamics in early 2025, spiking student-loan delinquencies and adding noise to contact strategies across portfolios that share borrowers.

Rather than replacing what works, Voice AI complements existing dialer/agent workflows:

Boring, repetitive steps (reminders, right-time callbacks, balance confirmations, payment-path routing) move to agents that never tire or forget scripts.
Human collectors get fewer but higher-value conversations (negotiations, hardship plans, sensitive escalations).
Every interaction is logged, searchable, and quality-assured—so compliance and operations gain continuous visibility instead of sample-based audits.

What makes regulated lending different (and how Sei AI handles it)

Collections in regulated finance isn’t “just outbound calls.” It’s outreach inside a tight frame: FDCPA/Reg F rules, UDAAP concerns, state recording laws, TCPA consent, PCI during payments, and mortgage-specific rules (RESPA/TILA/Fair Housing) for servicers.

Here’s the short version of how Sei AI leans into that reality:

Policy- and regulation-aware agents: Agents are configured against your internal SOPs and sector regulations, with built-in guardrails and full auditability.
Reg F “7-in-7” and inconvenient times: Outreach logic respects call-attempt and timing limitations (no calls before 8 a.m. or after 9 p.m. local time; frequency rules baked in).
Limited-content messages: Voicemail content can be constrained to Reg F’s limited-content definitions when applicable.
Payment security (PCI): During card capture, agents can pause/blackout recording and route DTMF-masked input so sensitive authentication data (like CVV) isn’t stored—aligned to PCI DSS guidance.
Mortgage & servicing overlays: Workflows honor the mortgage stack’s regulatory overlay (RESPA/TILA/UDAP/Fair Housing) and servicer-specific scripts.
QA coverage & audit trails: 100% monitoring vs. sampling, with searchable transcripts and flagging on 30+ compliance dimensions—so audits and exams pull from system of record, not sticky notes.

You control policy. The agent executes it the same way every time, logs it, and escalates when the policy says so.

How Sei AI’s collections agents actually work, under the hood

When I evaluated Sei AI for a regulated shop, I mapped it like a mini platform sitting on the stack you already have:

Signal → Decide → Speak → Record → Automate

Signal: Triggers from the LMS (days past due, broken PTP), CRM events, and CCaaS dispositions feed the agent queue with reason codes and customer consent metadata.
Decide: Policy-aware decisioning picks the right action (call, text, email), enforces Reg F frequency/time windows, and sets the correct disclosure/scripting.
Speak: Real-time speech (ASR/TTS) with dialog management for identity verification, balance detail, payment options, hardship triage, and courteous hand-offs to human collectors as needed.
Record: Transcripts, decisions, policy checks, and outcomes write back to QA and analytics so supervisors see what happened (and why). 100% audit, not samples.
Automate: After-call tasks (notes, LMS updates, dunning-level moves, PTP timers, confirmation letters) are done automatically, reducing swivel-chair work.

From the data-flow view, nothing exotic: authenticated webhooks, queue orchestration, and connectors to LMS/CRM/CCaaS and payment gateways. The difference is how compliance guardrails wrap each step, so what the agent can’t do is just as important as what it can.

The game-changer: compliance-by-design agent orchestration

Plenty of teams have “bots that talk.” What moves the needle for regulated lenders is agent orchestration with compliance baked in:

Every dialog turn checks a rulebook: disclosures, inconvenient-time checks, call-attempt counters, call-recording controls, and channel eligibility (e.g., when text is OK).
Policy versions are tracked: When Risk updates a line in the hardship script, it’s versioned and live in minutes—no tribal knowledge required.
QA is automatic: 100% of interactions are evaluated against 30+ dimensions; exceptions route to supervisors with the clip and context attached.
Payments are PCI-aware: Masked DTMF, paused recording, and no storage of sensitive auth data.

That’s what lets you scale outreach without scaling risk.

Sei AI Collections Toolkit — 8 modules you can deploy today

1. Outbound Early-Stage Collections Agent

Prioritizes 1–29 DPD with right-time calling and Reg F-compliant windows.
Confirms identity, summarizes amount due and due-date grace details.
Offers self-serve payment or short-term extensions within your policy limits.
Books callbacks when the borrower is busy; no-answer handling follows frequency caps.
Writes outcomes to LMS and CRM; sets PTP timers and reminders.
Escalates to a human collector when hardship keywords or dispute cues appear.
Feeds QA with full transcript and pass/fail on script adherence.

2. Broken Promise-to-Pay (“PTP Keeper”)

Monitors PTP timers and launches gentle, policy-compliant reminders.
Offers to reschedule PTP once with disclosure if it’s within your policy bands.
Routes repeat breaks to a human queue with full context.
Captures reason codes (cash-flow timing, employer delay, system issue) for analytics.
Updates dunning levels and next contact window automatically.
Generates confirmation SMS/email when permitted by consent records.

3. Inbound Payment & Escrow Assistant (Servicing)

Handles “what’s my balance,” “how do I pay,” “escrow shortage” and similar FAQs 24/7.
Skips IVR trees: speaks naturally, authenticates, and completes the workflow.
Collects payments with PCI-aligned controls (DTMF masking/recording pause).
Creates tickets only when policy requires a human review.
Logs everything for QA and customer-journey analytics.
Reduces AHT and improves CSAT for peak volumes.

Cross-checks consent, preferred channels, and time-of-day rules.
Validates addresses/emails and flags risky contact paths.
Applies limited-content voicemail rules when leaving messages.
Drops events into your compliance log for audits.
Requests updated consent when allowed by policy.
Keeps a living “contact strategy” profile per account.

5. Hardship & Loss-Mitigation Triage

Listens for hardship markers and pivots to empathetic scripts.
Gathers required data for forbearance/repayment options (mortgage & unsecured).
Books appointments with specialists; ships a summary to the CSR.
Tracks timelines so nothing stalls in limbo.
Helps avoid UDAAP traps by keeping language and offers within policy.
Surfaces population-level insights to Risk and Servicing Ops.

6. Disputes & Complaints Intake

Captures dispute narratives and documents needed to investigate.
Classifies and routes per your complaint-handling policy.
Flags sensitive language or potential conduct risk in real time.
Keeps evidence chains intact for exams and remediation.
Feeds back system issues that create repeat complaints.
Provides 100% QA coverage so nothing falls through the cracks.

7. Skip-Contact Hygiene & Locator Assist

Checks for stale numbers/emails and requests confirmation in compliant ways.
Suggests best contact windows based on past answer patterns.
Avoids repeat attempts that would violate frequency rules.
Syncs clean contact data back to CRM/LMS.
Tags risky endpoints for legal review (e.g., shared lines).
Reduces wasted dials and collector frustration.

8. Supervisor Console & Auto-QA

Live dashboards across call/chat/email with 100% coverage.
Alerts for missed disclosures, deviations, and potential UDAAP issues.
One-click coaching moments with transcript snippets.
Side-by-side comparisons of agent vs. policy text for calibration.
Policy version history and change logs.
Export packs for audits and board reporting.

The metrics that matter (and realistic timelines)

When we run pilots, I recommend agreeing the scoreboard on day one. Typical measures:

RPC (Right-Party Contacts): Target an uplift via better right-time windows and consent enforcement.
PTP Rate & PTP Kept: Not just “promises made”—promises kept within defined windows.
CPC/CPA (Cost per Connect/Arrangement): Blended cost with AI handling a chunk of volume.
AHT & FCR: First-contact resolution for inbound; shorter time on repetitive calls. Sei’s site cites up to 60–75% reduction in handle times in relevant workflows; use your baseline to set realistic goals.
Compliance Exceptions per 1,000 contacts: Should decline as scripts are executed consistently and QA moves from samples to 100%.
Recovery Rate / Cure Rate: Tracked by DPD bands; early-stage lift matters most.
Customer Effort & NPS: Especially for servicers; the site references NPS gains alongside throughput improvements.

Timelines you can plan against (pilot → production):

Weeks 0–2: Use-case scoping, policy mapping, sample scripts, success metrics locked.
Weeks 2–4: Integrations to CCaaS/LMS/CRM/payments; QA rubric & exception flows.
Weeks 4–6: Sandbox runs with staff; red-team compliance; tweak scripts & limits.
Weeks 6–8: Soft-launch to a segment; daily QA, report-out, and pivots.
Weeks 8–10: Scale to additional bands/segments; enable more modules.

A 6–10 week rollout plan that respects risk & controls

Define your “thin slice.” Start with broken PTP follow-ups or 5–15 DPD reminder calls—impactful, bounded risk, easy to measure.
Map policy to dialog. Disclosure text, hardship triggers, inconvenient-time checks, and voicemail content must be explicit.
Wire the stack. LMS (events), CCaaS (routing/recordings), CRM (notes), payment processor (DTMF masking/hosted flows).
Rehearse failure modes. No-consent numbers, third-party answers, card capture miskeys, edge-case disclosures.
QA every single interaction. Use the auto-QA console; export weekly compliance packs.
Expand deliberately. Add inbound payment calls, then hardship triage, then skip-contact hygiene.

Integration blueprint: LMS, CRM, CCaaS & payments

Loan Management System (LMS): Source of truth for DPD, placements, dunning levels. Agents subscribe to events and write back outcomes and reason codes.
CRM: Contact history, consent, preferences; stores the “customer 360” view used by agents.
CCaaS/Telephony: SIP trunks, call routing, live hand-offs to humans, and call recording controls.
Payment Processor/Gateway: Hosted payment pages or tokenized rails, DTMF masking, and paused recordings for PCI scope reduction.
QA & Analytics: 100% coverage with policy checks; supervisors get exception queues instead of raw call dumps.
Sei AI connectors: The platform integrates with payment processors, LMS, and CCaaS systems and supports custom connectors at onboarding.

Security, privacy & compliance (PCI, SOC2, auditability)

SOC 2 Type II posture and private VPC deployments with tenant isolation.
100% auditability with searchable transcripts and policy-check logs.
PCI DSS alignment on card capture: never store CVV/CVC, redact PANs from recordings/logs, and prefer masked input with hosted flows.
Reg F adherence: attempt limits, inconvenient-time rules, and limited-content voicemail.
Mortgage/Servicing regs: workflows designed around RESPA, TILA, UDAAP, Fair Housing and internal policies.

“Best For”: where regulated institutions get outsized ROI first

Banks & credit unions looking to modernize early-stage collections without scaling headcount.
Mortgage servicers facing seasonal spikes in escrow or payment inquiries alongside delinquency waves.
Card & personal-loan lenders who need strict Reg F compliance while improving RPC/PTP rates.
Fintech lenders that want consistent scripting, 24/7 inbound, and clean QA data for partner reporting.

Industry lenses: mortgage, credit cards, auto, credit unions

Mortgage servicing

Start with inbound payment & escrow FAQs; then add early-stage delinquency reminders.
Build hardship triage that collects the data your specialists need the first time.
Use 100% QA to coach for empathy and compliance in sensitive conversations.

Credit cards & unsecured

Focus on right-time calling and seamless payment capture with PCI controls.
Tighten limited-content voicemail and consent checks to reduce exceptions.

Auto lending

Automate early reminders and broken-PTP follow-ups; surface insurance/lien issues quickly.
Use Disputes Intake to capture repo-adjacent concerns before they escalate.

Credit unions

Lean on policy-aware scripting to protect member experience and brand.
Use analytics to spot hardship trends and adapt member-friendly offers within policy.

Change-management playbook for collections leaders

Pick one KPI to win first. PTP Kept is my pick; it’s honest and outcome-centric.
Align with Compliance early. Co-draft the scripts and voicemail policies; publish the rubric.
Train the trainers. Supervisors learn the console and how to coach from transcripts.
Instrument your stack. If it’s not in LMS/CRM, it didn’t happen.
Document hand-offs. Humans should get a tidy summary, not start over with the borrower.
Debrief weekly. Review exceptions, capturable wins, and expand scope deliberately.

FAQ for regulated lenders

1) How do you enforce Reg F frequency and timing rules?

Sei AI’s orchestration layer tracks attempts across channels, enforces “7-in-7,” and checks local time windows (no calls before 8 a.m. or after 9 p.m.), then logs each attempt for QA.

2) Can agents leave voicemail?

Yes—configured to limited-content messages where applicable (business name that doesn’t reveal the business of debt collection, a callback number, etc.), with your counsel approving exact wording.

3) How is card data handled during payments?

Agents can pause/blackout recording and collect tones via DTMF masking; sensitive auth data like CVV is never stored, aligning to PCI DSS guidance.

4) What about SOC2 and deployment isolation?

Sei runs in private VPCs with tenant isolation and SOC 2 Type II controls; the platform is built for 100% auditability.

5) Do you support mortgage-specific rules?

Yes—Sei’s mortgage and servicer offerings are built around RESPA, TILA, UDAAP, Fair Housing and your internal policies.

6) Can we start with inbound only?

Absolutely. Many servicers begin with inbound payment & escrow FAQs, then add outbound early-stage reminders and PTP Keeper once QA shows stability.

7) How do supervisors coach with AI in the loop?

The console provides 100% QA coverage, highlighting missed disclosures and moments to coach. Exports support internal audits and board reporting.

8) How are consent preferences tracked?

Consent and preferred channels/time windows live in CRM; the agent consults them before engagement and updates them when allowed.

9) What happens when someone mentions hardship?

The agent pivots to an approved hardship script, collects required data, and books a specialist hand-off—reducing repeats and improving documentation.

10) How long to pilot?

A focused pilot usually lands in 6–10 weeks depending on integrations, with clear go/no-go metrics set on day one (PTP kept, compliance exceptions, AHT).

11) Can we customize language to match our brand?

Yes—Sei agents are policy-aware and brand-tuned, so wording, tone, and offer sets reflect your guidelines.

12) Do we have to rip out our dialer or IVR?

No. Start as an overlay: use Sei for selected queues, then scale as metrics justify.

Closing thoughts & next steps

Collections leaders aren’t chasing novelty. You’re chasing consistency, empathy, and measurable lift—without adding risk or headcount. That’s why we built Sei AI specifically for regulated financial institutions: policy-aware agents, real-time compliance, 100% QA, and integrations that make your existing stack more valuable.

If increasing PTP kept and right-time contacts is on your roadmap this quarter, start small:

Pick one DPD band or one queue (broken PTP follow-ups).
Instrument the KPIs you care about.
Run a 6–10 week pilot with daily QA, then expand what works.

Want a working demo mapped to your policies? Book a short session and we’ll show the modules above on your data flows and controls.

Research & validation notes (for your risk/compliance team)

Market backdrop: New York Fed 2025 Q2 report for debt levels and delinquency.
Student-loan dynamics: Q1 2025 delinquency spike coverage for context.
Regulatory references: CFPB FDCPA/Reg F (timing, frequency, limited-content messages).
PCI DSS: Guidance for telephone-based payment card data and recording controls.