Resolve borrower queries in minutes, not hours.
AI loan servicing that cuts costs, holds compliance, and answers borrowers clearly: purpose-built for lending.
These are what the engagement aims at. Thirty days on your own portfolio settles whether they hold.
- <30sAI first response · target
- 60%lower servicing cost · target
- 1 screenfull borrower context
Simple borrower questions take a long path to resolve.
- 15 minsper routine query
- 3 systemsLMS · Helpdesk · Partner portal
- 1 answerthat should take seconds
"Bhai mera EMI bounce ho gaya, kya hua? Please check."
10:42 AMYour agent has to:
- Open helpdesk, find loan ID
- Switch to LMS, check bounce status
- Verify mandate in partner portal
- Return to ticket, type reply manually
- Urgency
High, EMI bounce, immediate financial stress
- Sentiment
Frustrated but trusting, "please check" signals hope
- Context
EMI bounce → needs mandate status, bank response, next steps
- Language
Hindi-English mix: generic NLP would miss the intent entirely
Slow servicing compounds into a business problem.
Slow servicing doesn't just frustrate borrowers: it compounds across your entire business.
Margins are shrinking. Servicing costs shouldn't grow linearly.
Every repeat contact has a direct cost. At scale, manual servicing eats into margins that lending businesses can't afford to lose: especially with tightening NIMs and rising competition.
Agent attrition starts with repetitive, frustrating work.
When 70% of an agent's day is data lookup across 3 systems, you're not just losing efficiency: you're losing people. High attrition means constant retraining and inconsistent service quality.
A slow response is regulatory risk, not just a CX problem.
RBI's increasing scrutiny on borrower grievance handling means every unresolved query is a potential complaint escalation, with audit trail gaps that regulators will notice.
Trust is built in moments of stress, not marketing.
When an EMI bounces and the borrower doesn't get a clear answer quickly, they don't blame the system: they blame you. Fast, accurate resolution is how you earn loyalty.
Every unresolved query costs trust, time, and repeat contact.
Lending borrowers don't call to chat. They call because an EMI bounced, a mandate failed, or a foreclosure notice doesn't make sense. When your servicing team can't resolve it fast, here's what it costs you:
Repeat contacts
Borrowers call back, email again, visit the branch. Each touchpoint costs you and erodes trust. Most of these are data lookups your system should answer instantly.
Slow resolution
Bounce clarifications, charge disputes, KYC corrections sit in queues while borrowers grow frustrated. Every day of delay increases complaint escalation risk.
Misplaced effort
Hardship cases and vulnerable borrowers need human empathy, but your agents spend 70% of their time on data retrieval instead. The cases that need care don't get it.
Lokta eliminates the 60% that shouldn't need a human. So your team can focus on the 40% that does.
A servicing layer built for lending from the ground up.
AI loan servicing is the agent layer that handles borrower communication after disbursal: answering repayment, bounce, and KYC queries, drafting grounded replies, and routing what needs a human. Lokta is an AI loan servicing agent purpose-built for lending, with a unified inbox and full audit trail.
Unified inbox
WhatsApp, email, phone, and portal interactions in one screen, with full borrower and loan context. No more tool-switching.
AI copilot for servicing
AI drafts replies grounded in borrower data. Agents review and send, or automate for high-confidence cases.
Confidence-based automation
Confidence thresholds decide when to auto-reply, draft for review, or escalate. Your team stays in control.
Lending-specific AI triage
Every message classified by intent, sentiment, urgency, and servicing lane, even in mixed-language conversations.
AI insight dashboard
A real-time cockpit for loan servicing. Portfolio health, agent performance, resolution trends, and risk signals: all in one view, built for teams running active books under SLA.
Knowledge grounding
Responses grounded in your FAQs, SOPs, and policy documents. Anti-hallucination guardrails keep every reply accurate.
Generic helpdesks are built to read a ticket, not a loan.
A generic support tool shows the conversation thread. Lokta shows the repayment schedule, NACH mandate status, overdue history, and complaint trail, everything an agent needs in one view.
Generic tools route on keywords. Lokta understands lending intent (foreclosure, part-payment, NACH re-mandate) even in mixed-language messages, so the right query reaches the right agent the first time.
Every servicing reply carries RBI compliance and audit implications. Lokta bakes that in: explainable decisions and full audit trails from day one, assembled before the fact rather than after it.
RBI-ready compliance, built into the servicing path.
Every action an agent takes lands in the audit trail with the inputs it saw and the policy clause that let it through, which is the shape an RBI-regulated servicing environment has to produce on request. Supervisors elsewhere ask for the same evidence under other names, the Federal Reserve's SR 26-2 and the PRA's SS1/23 among them, and it is the same trail that answers them.
Audit trail by default
Every interaction captures agent identifiers, confidence scores, tools invoked, and compliance metadata, ready for RBI audit at any time.
PII protection & guardrails
Borrower data scoping, PII masking, hallucination checks, and tool-grounding verification, applied to every interaction.
Operational compliance
RBI complaint detection, SLA-driven escalation logic, and regulatory response handling built into the workflow, not scattered across tools and spreadsheets.
What the first 90 days are built to deliver.
Engagement targets, not observed averages: grounded in what this team saw operating a commercial lending platform at scale, and measured against your portfolio during the pilot.
- 40%Faster resolution
- 60%Cost reduction
- 3×Agent productivity
- 100%Audit coverage
Before vs. after Lokta
| Metric | Today | With Lokta |
|---|---|---|
| Systems per resolution | 2-3 tools | 1 unified screen |
| Time to first response | Hours | Minutes |
| Routine ticket handling | Fully manual | Selectively automated |
| Agent productivity | Limited by lookup time | Higher throughput |
| Compliance evidence | Manual assembly | Built into workflow |
Every metric above can be measured during a 30-day pilot.Start a 30-day pilot
Works with Lokta's Loan Management System.
Or any lending system you already use.
Our lending ontology layer normalizes data from any LMS, core banking, or data warehouse into one servicing context, so agents and AI always work from the same picture. MCP support coming soon.
Loan management / core banking
Connect your loan management system as the primary data source for borrower and loan context.
Data warehouse
Pull enriched borrower data, payment history, and portfolio signals from your DWH.
Payment infrastructure
NACH, UPI, payment gateways: mapped to loan state transitions and bounce handling.
Document systems
Generate and retrieve statements, NOCs, and servicing documents on demand.
Borrower channels
WhatsApp, email, phone, and borrower portal, all feeding into one unified inbox.
Source-agnostic by design
Lokta's ontology layer maps any lending system's data model. Your LMS stays your source of truth: Lokta's servicing agent becomes the intelligent servicing layer.
Start safely.
Expand with confidence.
This staged rollout helps teams build trust before increasing automation.
- 01Month 1
Shadow
Lokta observes conversations, classifies tickets, and learns workflows in the background. Your team gets visibility with zero operational risk.
- 02Month 2
Copilot
AI begins drafting borrower responses using real loan context and knowledge grounding. Agents review and send.
- 03Month 3
Selective Automation
High-confidence cases can be automated while complex, sensitive, or judgment-heavy cases remain with human agents.
Servicing is one agent in a governed stack.
- Agent proposesThe next move for one account, with the reason attached.
- Core decidesPosts it, or refuses it, against the policy version in force.
- Record proves itProposal, decision, policy version and approver, on the account.
Questions buyers ask first
Rollout, integration, compliance, and the AI surface.
Does Lokta do loan origination or underwriting?
No. The servicing agent works the book after disbursal: borrower conversations, collections, and lifecycle actions on loans that are already live. For the next 6-9 months the post-approval book is the entire build focus. Lenders keep their own loan origination system and hand off to Lokta at disbursement. Origination on the same canonical model is a later horizon on the roadmap, not something you can buy today.
What is AI loan servicing on Lokta?
Lokta's servicing agent is purpose-built for lending operations. It runs a unified inbox across WhatsApp, email, phone, and the borrower portal; classifies and triages messages by lending intent (foreclosure, part-payment, NACH re-mandate, etc.); drafts grounded replies; and automates high-confidence resolutions through a maker-checker rollout, Shadow → Copilot → Selective Automation.
How is it different from a generic AI chatbot or helpdesk?
Generic helpdesks route on keywords and are blind to loan context. Lokta reads the repayment schedule, NACH mandate status, overdue history, and complaint trail; understands lending intent in mixed-language conversations; grounds replies in your policies and SOPs; and treats RBI compliance and audit trails as first-class features, not afterthoughts.
What is the rollout model?
Three phases. Month 1, Shadow: Lokta observes live conversations and produces draft replies that agents can compare against their own. Month 2, Copilot: agents send AI-drafted replies after review. Month 3, Selective Automation: high-confidence cases auto-resolve while complex, sensitive, or judgment-heavy ones stay with human agents.
How does Lokta handle servicing compliance and audit?
Every interaction captures agent identifiers, confidence scores, tools invoked, and compliance metadata: ready for RBI audit at any time. PII masking is applied at ingest; hallucination checks and tool-grounding verification run on every reply; RBI complaint detection and SLA-driven escalation are wired into the workflow rather than scattered across tools and spreadsheets.
Which systems does the servicing agent integrate with?
Your LMS or core banking system stays the source of truth: Lokta's servicing agent connects to it for borrower and loan context. It also pulls from your data warehouse for enriched signals, hooks into payment infrastructure (NACH, UPI, payment gateways), generates servicing documents on demand, and unifies borrower channels (WhatsApp, email, phone, portal). Lokta's ontology layer maps any lending system's data model.
How much can AI loan servicing reduce servicing cost?
The engagement targets are sub-30-second AI first-response times and around 60% lower servicing cost per resolved interaction: driven primarily by deflection of high-confidence routine cases and shorter agent handling times on the rest. These are targets grounded in what this team saw operating a commercial lending platform at scale, not observed Lokta averages; actual numbers are confirmed against your portfolio profile during a 30-day pilot.
AI that reads your policies, knows every loan, and answers in seconds: built for lending, governed by design.