Servicing capacity is trapped in repeatable work
Calls, summaries, follow-ups, and case preparation consume time that experienced teams need for hardship, disputes, and exceptions.
Now onboarding select design partners
Loan Servicing on Autopilot
Every decision governed. Every action traceable.
LendEasy orchestrates loan servicing operations with AI agents bounded by a lender’s policies and backed by human judgment when needed. The result: audit-ready operations by design, the ability to scale a portfolio without scaling headcount, and a seamless experience for borrowers, 24/7
Keep your current lending core—or use ours. No forced migration.
Signals → governed work → outcomes
Incoming work signals
Borrower · core · externalSMS hardship requestBorrower · “I lost my job…”
Payoff quote requestedBorrower portal · dated today
Bankruptcy filing matchedExternal · court record feed
FEMA disaster declarationExternal · borrower county affected
SCRA status changeExternal · military status feed
Death registry matchExternal · deceased borrower alert
Federal litigation / injunctionPACER · court docket match
Credit bureau disputee-OSCAR · ACDV received
Your team · one governed interfaceSee · approve · override · take over through one layerLive
Judgment surfacedDecision returned · traced
One governed operating layer
AI agentsSpecialists execute work; orchestration coordinates the flow
AI capabilities
Voice calling
Document ingestion
Workflow orchestration
Case assignment
Omnichannel communications
TriageInvestigateActMonitor
Full context assembled · every action checked before execution
One servicing workspaceEvery item has an owner, state, next step, and complete context
CasesDecisionsEvidence
Two-way lending-core connectionLive signals · governed updates
Signals from the lending core
Flow into the governed operating layerPayment returnedACH return posted · recovery workflow opened
Loan overduePayment passed due · delinquency workflow opened
Governed updates
Approved account and schedule changes flow back to the coreLending coreUse the LendEasy lending core—or connect your existing one.
Auditable outcomes delivered
Hardship extension executedEligible offer accepted · Policy HS-4.2
Payoff quote deliveredExact figure generated · Dated today
Payment recovery completedReturn playbook executed · Funds re-cleared
SCRA rate applied & pausedMilitary status verified · Evidence logged
FEMA forbearance enrolledDisaster rule applied · Schedule updated
Proof of claim filed & hold setCourt match verified · PACER record logged
FCRA dispute resolvedTradeline verified · e-OSCAR response logged
Outreach suppressedProtected borrower rule enforced · Audit ready
Every signalBorrower, lending-core, and external events
One workspaceAI and humans share cases and controls
Policy firstChecked before the action happens
Tracked to conclusionOwner, state, outcome, and evidence
Why now
AI is entering servicing. The operating model has to catch up.
Lenders need more capacity without creating a parallel AI operation. People and AI should work from the same permissions, policy checks, ownership, and evidence.
Borrowers expect timely service across channels
Routine questions and next steps cannot always wait for business hours or a handoff between disconnected systems.
AI cannot become a parallel operation
People and AI need the same permissions, policy checks, accountable ownership, and evidence—not another isolated toolchain.
Why governed servicing changes the outcome
Maya says, “Stop calling—I need help.” What happens next?
Compare the same call across today’s fragmented servicing landscape and one governed flow on LendEasy—from Maya’s instruction through protection, policy-bound approvals, borrower communication, core execution, and confirmation.
Same call · two operating models · Illustrative scenario — fictional borrower
MJ
Outbound call connectedMaya Johnson
Loan ••••1842 · 30 days overdue
“I’m in the hospital after emergency surgery. I can’t talk. Stop calling me—I need help with a payment deferral.”
Today’s servicing patchworkStatic list · partial context · delayed controls
FragmentedControl gapMaya’s request never reaches the active collection campaign.
Where the context breaks
four partial viewsLending core / LMS
The system of record exports yesterday’s delinquency snapshot for the dialer.
scheduled extractPredictive dialer
The fixed payment script continues.
place callCRM disposition
The hardship note stays inside the CRM.
log noteCase tracker
The hardship request advances separately from the active dialer campaign.
open caseCONTROL COLLISIONAnother collection call is queued. The dialer never received Maya’s instruction.outreach active
While the request moves across teams
- Call endsThe request waits in the CRM without a shared owner.
- Request discoveredAn agent finds the note and opens a separate hardship case.
- Loan facts re-enteredBalance, status, and call details are copied into the case.
- Controls alignedOutreach holds and supporting evidence are reconciled manually.
Fragmented resultDay 3+
The hardship request remains open. Outreach remains active.
Maya’s case moves across teams while the dialer continues from an older LMS snapshot and controls are applied by hand.
Multiplepartial systems
Multiplemanual handoffs
Activeoutreach
Laterevidence rebuilt
LendEasy governed servicing flowVoice AI · evidence · policy gates · core execution
GovernedAI protects immediatelyConfigured, reversible controls
Policy sets the gateA person approves when required
Four governed stages
Open any stage to inspect the workINTENT CAPTUREDMedical hardship + no further calls
Distress recognizedInstruction recordedHold outreach
VOICE AI“I’m ending this call and pausing outreach now. I’ll text a secure hardship link for whenever you’re ready.”
HOLD ACKNOWLEDGEDConnected collection channels paused4 / 4
VoiceSMSEmailDialer
CHANNEL PERMISSION CHECKEDSecure hardship link sent by verified SMS
The case advances on Maya’s verified channel while voice and campaign outreach remain held.
SMS verifiedSecure linkVoice remains held
MAYA RETURNS WHEN READYEvidence received · call context preservedconnected
Sensitive clinical detail excludedThe decision receives proof—not Maya’s diagnosis.
Document intelligence separates required servicing facts from unrelated medical detail before the case advances.
Human judgmentone decision
Maya qualifies for a three-month medical hardship deferral under Policy MH-2.
MH-2 matched3 payments movedschedule impact shown
Deferral approvedApproved by JL
- Terms to MayaApproved deferral terms delivered on her verified channel.
- Acceptance gateMaya accepts when policy requires it; otherwise delivery is recorded.
- Core acknowledgmentSchedule recalculated using the final approved terms.
- Confirmation deliveredMaya receives the effective dates and updated schedule.
Borrower communication, any required acceptance, the core update, and final confirmation remain one traced outcome.
LendEasy resultprotected + resolved
Protected immediately. Resolved with control.
The reversible hold happens first. Policy determines whether the loan change needs a person’s approval before the outcome executes with its evidence.
- Borrower instruction
- No further calls · medical hardship
- 10:14:06
- Outreach hold
- Voice, SMS, email, and dialer acknowledged
- 4 / 4
- Evidence retained
- Provider and leave-through date only
- Minimized
- Policy evaluation
- Medical Hardship Policy MH-2
- Matched
- Human decision
- JL · three-month deferral
- 10:32:16
- Borrower delivery
- Approved terms sent on Maya’s verified channel
- Delivered
- Acceptance gate
- Borrower acceptance captured when required by policy
- Satisfied
- Core outcome
- Schedule recalculated from the final terms
- Recorded
- Confirmation
- Effective schedule delivered to Maya
- Delivered
1coordinated record
0re-keys in this flow
Heldconnected outreach
Readyevidence with action
End-to-end governance
Keep the entire servicing flow inside policy
From the first signal through eligibility decisions, human approvals, borrower communications, payments, and lending-core updates, LendEasy applies current facts, permissions, and rules — Reg F, SCRA, Regulation Z, Regulation E, and your state rules — before the workflow moves forward.
Rules with source and history
Each rule records its source, jurisdiction, effective date, and version, so past actions stay linked to the rule used at the time.
Checked before consequential work
Eligibility decisions, borrower communications, approvals, payments, and lending-core updates are checked against current facts and permissions before the workflow advances.
Controls follow the case
Protected-borrower restrictions, contact limits, and approval requirements stay active from the opening signal through resolution. When a bankruptcy filing matches at 9 a.m., the 10 a.m. dialer list already knows.
One connected evidence trail
Every decision, approval, action, and outcome stays connected in tamper-evident records your auditors can search and export.
- Shadow
- Assist
- Human approval
- Autonomous within policy
Each task type gets its own level. A scoped kill switch can stop affected AI work instantly.
Where it works
Start where the operational pressure is highest
Most design partners start by replacing manual queues with AI workers in first-party collections, then expand across every operational flow — running all servicing tasks through the exact same policy gates, unified cases, and audit trails.
Adopt without a forced migration
Keep your current lending core—or use ours.
LendEasy adds the same governed servicing layer either way. Current account facts flow in; approved updates flow back.
The founding story
Built by architects of global core banking, lending, and cloud infrastructure
LendEasy's founders spent their careers building lending and banking platforms used worldwide. They started LendEasy to fix the compliance and scaling bottlenecks they experienced building core banking systems—creating one operating layer for people, AI, and policy enforcement.
A serial fintech founder and executive, Vishwas co-founded Finflux (acquired by M2P Fintech) and served as Chief Product Officer at Mbanq and Chief Platform Officer at LendAPI—building full-stack U.S. platforms for deposit accounts, card issuing, loan servicing, and payment rails. As VP of Apache Fineract and Chief Community Engineer at the Mifos Initiative, he co-architected open core banking systems serving hundreds of millions of customers worldwide.
Ullas is a distributed systems architect with over 14 years at Amazon Web Services, where he built high-scale data platforms for AWS Commerce and Billing, handling millions of transactions per second and petabyte-scale data. At LendEasy, he leads engineering, building AI agents and the control plane for automated, compliance-native loan servicing.
Founder-led workflow demo
See your servicing workflow on LendEasy
Meet Lena, LendEasy’s first-party collections voice agent. A founder will run the workflow against your operating model—not a generic product tour.
Bring one real servicing scenario
We will trace where AI works, where a person decides, what reaches the lending core, and what the final audit record contains.
Founder-ledTailored to your workflow
FAQ
Questions lenders ask us
LendEasy is an end-to-end loan servicing platform built around a single governed workspace where AI workers and human teams operate side by side. It continuously listens for servicing signals—from missed payments and borrower calls to external events like bankruptcy filings and military service status changes—and automatically executes full-cycle servicing workflows, including first-party collections, payment resolution, and hardship assistance. Whether using our built-in loan management system or your existing core, LendEasy evaluates every action against your policies and approval limits, escalates to human judgment when required, and maintains a complete, auditable record.
See governed servicing in action
We're building LendEasy alongside a small group of design partners who service loans every day. Walk through the AI agents, policy checks, and servicing workspace with the founding team.