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Lokta Next 100, India programme · no platform fee for up to two years, for RBI-registered NBFCs up to ₹100 crore.See the programme
For lenders, fintechs, and LSPs

The agentic loan servicing platform for the live book.

Servicing, monitoring, collections and recovery, inside your guardrails, with the goal of keeping operating cost from scaling linearly with the book.

Talk to usSee the platform

Lokta is the agentic loan servicing platform for NBFCs, banks, fintech lenders and lending service providers (LSPs). Lenders run the live book on it after approval, inside their guardrails: servicing, monitoring, collections and recovery on a deterministic core. Agents propose, the lender's core decides, the record proves it. From the team behind Apache Fineract, for lenders in India and beyond.

The live book in Lokta

The Lokta Today screen: collected and disbursed against the day, the day’s cumulative collection curve against the last thirty comparable days, and a list of accounts in arrears needing a person before 18:00.Lokta Vitals overview: live principal outstanding, active loan accounts, 90-plus exposure, and a ranked findings list with review items first.Lokta Vitals asset quality: an ever-90-plus vintage curve by origination cohort, against the observed range for seasoned cohorts.Lokta Product Studio building a new loan product: a configuration checklist across identity, interest, schedule, charges and accounting, beside a validation panel listing what is still blocking the draft.The Lokta general ledger: journal legs for each loan transaction against bank, penalty receivable, interest receivable and loan portfolio accounts, posted by the core.

Built by the team behind Apache Fineract*, the open-source lending core used by lenders in 70 countries.

~$500B
Cumulative loan principal
65M+
Borrowers served
70
Countries

Aggregate impact attributed to the Mifos/Fineract ecosystem, not Lokta directly.Read the impact essay

Most businesses end at delivery. Lending begins there.
Approval is the start of the exposure, not the end of the sale. Everything below is about the years that follow it.
Why it matters

Profit per loan disbursed.
That is the outcome to measure.

Each application, repayment, and recovery can inform the next reviewed policy. Whether it improves the outcome is measured.

  1. 01

    Improve the book with measured evidence.

    Repayment and recovery evidence can inform reviewed policy hypotheses. Improvement is measured, not assumed.

  2. 02

    Evaluate more servicing and collections policy candidates.

    Agents can propose and evaluate candidates. Measure throughput and quality in the lender environment before claiming an improvement.

  3. 03

    Operating leverage is the target.

    Measure servicing cost per account as volume grows. Do not assume a margin or portfolio outcome before the evidence exists.

Every claim logged, replayable, auditable.Audit by design isn't the pitch: it's the price of admission.

The team

The team that built the rails the industry runs on.
Rebuilt for the agentic era.

Lokta is built by the original architects and builders of Apache Fineract. We learned, at scale, what the next generation has to be, and Lokta is what we'd build if we started today.

"You cannot run a bullet train on narrow-gauge rails. The track width is the architecture. That's why we started over."
Why we started over·Meet the team
How it works

AI proposes. The core decides. The record proves it.

Agent-native means the operators of the book are agents: software that reads the loan, decides, and acts. Every agent action is a governed write, checked against your policy before it touches the book.

Agents read the book

Software that reads the loan, decides what should happen next, and writes the proposal down before anything moves.

Your guardrails decide

Built and governed in AI Studio, the AI control plane authorises, scopes, logs, and verifies agent actions. A kill switch is built in. The agent proposes. It does not post to the ledger. Your core does.

The record proves it

Two ledgers, the same inputs always producing the same outputs, and state changes on the loan record replayable for whoever asks.

See the full architecture·The return is earned in the long loop after approval·Autonomy you can audit
The platform

One platform for servicing, collections and recovery. And it runs on the core you already have.

Servicing, collections, and compliance, all on one ledger, with a loan product studio and servicing agents on the same canonical model. Or keep your existing Loan Management System (LMS) and run the agents on top of it.

Loan Management

Lokta Ledger, the deterministic loan engine your book runs on, with the Loan Product Studio that composes each loan product on top of it. Every product is one versioned contract, not a configuration row. Audit-ready, schema-per-tenant, governed APIs.

See loan management

AI Loan Servicing

One inbox across WhatsApp, email, phone, and portal. Lending-aware triage, confidence-based automation, every resolution feeding the borrower model.

See AI loan servicing

Integrations

Connect Lokta to an existing loan system through a lender-specific data contract. Each rail, bureau, or partner connection is separately scoped, mapped, tested, and accepted before use.

See what Lokta runs on

RBI Model Risk Management

Continuous model-risk governance mapped to RBI's 2026 draft: validation, monitoring, and evidence organised for lender review. The regulated entity owns interpretation, submission, and accountability. Lenders under another supervisor's model-risk regime get the same validation, monitoring, and evidence structure.

See model risk management

Loan Origination is on the roadmap and is not available now. No release date is published. Read the roadmap design

Not ready for a conversation? Three ways to check our thinking against your own.

See the RBI model-risk self-benchmarkGet the vendor-neutral LMS RFP toolkitCompare Lokta with the LMS you run today
Who it's for

Your book runs in one of these three places.

Banks & NBFCs

For the lenders who carry the book: banks, NBFCs, and non-bank lenders in other markets.

On-prem, VPC, or single-tenant cloud; maker-checker at the policy boundary; audit trails on state transitions.

For banks & NBFCs

Fintechs & digital lenders

For teams without legacy debt.

Polylithic modules, OpenAPI 3.1, Keycloak-native; extend in-tree, not by vendor SOW.

For fintechs

LSPs

For lending service providers (LSPs) operating for multiple lending partners.

Multi-partner from the core, per-partner isolation and audit, co-lending reconciliation native to the data model.

For LSPs
Read the napkin math on the 5× problem
In lending, the autonomy you can audit is the only autonomy that scales.
How we engage

Founder-led adoption. Not a vendor pitch.

A small number of institutions at a time: banks, NBFCs, and fintechs building a deliberate adoption, not a SaaS sign-up. If you already run a book, nobody is asked to cut it over.

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  • Audit-ready by default

    Every agent action lands on the record before it lands on the ledger, so a model risk review has something real to check, not a screenshot.

  • Agents that work the case

    Agents read, draft, route, and propose. Deterministic checks and required approvals control record-changing actions.

  • Evidence that can compound

    Repayment and recovery evidence can update reviewed hypotheses. Improvement requires a measured result.

  • Guardrails, built with your team

    We agree what a pilot has to prove with your risk and compliance people first, before a single loan moves.

Every enquiry is read by one of the founders, Ashok Auty or Chandramouli C S. Who they are

Every enquiry gets an answer. Sometimes it is a 30-minute call to dig in, sometimes it is "not yet, and here is what would change that."

Read what we're building toward
Read the thinking behind the build
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