What is an agent-native lending platform?

An agent-native lending platform is a lending core built to be operated by agents (software that reads the loan, decides, and acts) rather than by people clicking through screens. The platform is shaped around what an agent needs to act safely: a governing control plane that authorises, scopes, logs, and verifies every action, on a deterministic core where the same inputs always produce the same outputs.

What is the governing control plane?

The governing control plane is the layer every agent action passes through before it reaches the book. It does four things for every action: authorises it, scopes it, logs it, and verifies it against policy. It provides identity for agents, maker-checker so a second authority signs off before a consequential change lands, and cross-module audit so every write sits on one trail in order. It is one of the guarantees the book runs on, together with the deterministic core it checks against. That combination is one a bolt-on AI layer structurally cannot fake.

Can Lokta migrate an existing loan book?

Yes. Legacy migrations feed the deterministic core through a disciplined, AI-assisted path you run in parallel with your live system: map the legacy schema to the canonical ontology, ETL the book across with the mapping logged, reconcile old and new until they agree to the rupee, then cut over when the evidence says you can, not before.

The platform · for Lenders, Fintechs, and LSPs

The agentic loan servicing platform
for the live loan book.

Lokta is built to be run by agents from disbursal to closure. Servicing agents keep the book performing, watch it for early warnings, collect it, and keep it compliant. Underneath sit the guarantees that make automating it safe: a governing control plane, a deterministic core, and a book that sharpens itself the more it lends.

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Agent-native

Agent-native means an agent is always at the controls.

Most lending platforms were built for people clicking through screens, then had AI bolted on the side. Lokta is built the other way: agents (software that reads the loan, decides, and acts) are the operators, and the platform is shaped around what they need to act safely.

Every layer assumes an agent, not a human, is at the controls, and is governed accordingly.

The architecture

The architecture: servicing on top, a deterministic ledger underneath.

Servicing on top. The deterministic ledger at the foundation. One AI control plane across every app.

Lokta post-disbursal apps

Servicing apps

Loan Servicing

Collateral Management

Collections

Compliance

Loan Product Studio

Retail lending products, composed as versioned configuration on the ledger below.

Loan Against PropertyHome LoanPersonal LoanBusiness / MSME LoansConsumer Durable & BNPLVehicle FinanceSupply-chain & Invoice Finance

Deterministic layer

Loan Ledgerschedule & obligation

Accounting Ledgerdouble-entry GL

The deterministic ledger, and the plane that governs it Post-disbursal app layers

Deterministic layer

One ledger under every app

Everything above reads and writes one engine. Both ledgers are deterministic: the same inputs always produce the same outputs, and any book can be re-derived from its own history, entry by entry, which is what makes a disputed figure answerable rather than arguable. This is the part of the system the AI never gets to guess at.

How the loan engine is built

Loan ledger: The obligation: what is owed and when, split across principal, interest, fees and penalties.
Accounting ledger: Every event booked double-entry to your mapped GL heads, with no side-system to reconcile at month end.

Loan product studio

A loan product is a versioned contract

Treated as configuration data, a product leaks: the differentiating logic falls to vendor code, the approved rule stops matching the built rule, and the product ends up living in four places at once, the credit memo, the config rows, the GL table and the API doc, with no way to diff them. The Studio keeps the whole product on one versioned record, so authors, reviewers and consumers read the same version.

Five stages on one record

01
Build
The full construct in one place: 190 parameters across 19 domains, including the long tail that usually falls to custom code. Contradictory settings are blocked before a product can be tested.
02
Simulate
Scenarios book real loans on a sandbox core and read back schedules, charges and corner cases. The numbers repeat exactly, because there is no AI in the calculation path.
03
Sign-off
The panel is derived from what this product configures rather than a fixed matrix. Each seat gets evidence cut to its function, and the next stage stays locked until every seat signs.
04
Integrate
Endpoints and agent tools are generated from the exact version, carrying the product's own bands, so a change is diffable rather than discovered in production.
05
Pilot
Run it in a bounded slice: which branches may book it, how much it may write in a month, and the delinquency level that pauses it. Recorded against the product version rather than living in a launch note.

Servicing apps

The apps that run the live book

Loan servicing runs every loan, every day: payments and part payments, rate resets, foreclosures and closures, each re-amortised correctly.Collections and compliance are case-driven, acting when a payment is missed, a case escalates or a report falls due. All three read and write the same ledger, so there is no second version of the truth to reconcile.

AI control plane

Agents that work across every app

AI Studio is where agents are built and governed. An Ask agent answers questions over the live book, a Reconciliation agent works the exceptions, and a Monitoring and EWS agent watches the portfolio for signals worth acting on. Every action is authorised, scoped, logged and verified before it reaches the book, and anything that moves money arrives as a proposal needing an explicit operator instruction.

AI proposes, the core decides, the record proves it.

Integrations

Runs inside the estate you already have

The book depends on rails you are not going to replace: bureaus such as CIBIL and CRIF, payments and mandates over NACH and BBPS, Account Aggregator consent, the registries a security interest is filed with, the warehouse your analysts already query, and the tax and accounting system finance closes on. Each connects through a governed API surface and a connector library, and the same routes are exposed to agents over MCP, so an agent reaches a counterparty the way a person would rather than through a private back door.

The full integration catalogue

Migration

The reason lenders stay on a system they have outgrown

Most lenders can name exactly what their current system costs them every month, and stay anyway. Migration is the reason. Moving a live book means every schedule, every part payment, every waiver and every classification has to land correctly, and the cost of getting it wrong is visible to borrowers and to the regulator. So the pain gets carried for another year. We treat that as the engineering problem it is and put agents on it: map the legacy schema to the canonical ontology, move the book with every mapping decision logged, then reconcile the old book against the new one until the two agree to the rupee. It runs in parallel with your existing system, and you cut over when the evidence says you can.

Why it's different

Three guarantees make a live book safe to hand to agents.

Any platform can bolt an agent onto a loan book. Few can let it act on the live book (accrue interest, restructure, write off, report to the regulator) and prove every move was right. Doing that safely takes a governing control plane that authorises, scopes, logs, and verifies; a deterministic core that gives one reproducible source of truth to check each action against; and a self-improving loop that turns every repayment and recovery into a sharper next decision.

The control plane matters, and it needs the other two. On its own it can log that an action happened; it needs the deterministic core to confirm the action was correct, and the loop to make the book better over time. The combination is what a bolt-on AI layer structurally cannot fake.

Every agent action is a governed write, never a guess.

The engineering philosophy behind this

In lending, the autonomy you can audit is the only autonomy that scales.

Not ready for a conversation? Two ways to test this against your own stack.

See the RBI model-risk self-benchmark Get the vendor-neutral LMS RFP toolkit

Migrations in

Bring the book you already have.

Legacy migrations feed the core through a disciplined, AI-assisted path you can run in parallel with your live system: map › ETL › reconcile › cut over. You watch the new book and the old book agree before you move. The cut-over is a decision you make on evidence, not a leap.

Map the legacy schema to the canonical ontology.
ETL the book across, AI-assisted, with the mapping logged.
Reconcile old and new in parallel until they agree to the rupee.
Cut over when the evidence says you can, not before.

Integrations out

Connected to the rails your book already runs on.

The ecosystem connects live through MCP and a connector library, so adding a partner or a data source is configuration, not a six-month integration project.

Tax and accounting: postings reconciled against the core.
Bureaus: CIBIL, CRIF.
Payments: NACH, BBPS.
Account aggregator: consented financial data.
Collateral registries: where the security interest is filed.
Data warehouse: servicing history where your analysts already work.

MCP (Model Context Protocol) is a standard way for agents to call external tools and data sources safely. It lets Lokta's agents reach a bureau or a payment rail through one governed interface instead of bespoke wiring per vendor.

Capabilities

What runs on the platform

A deterministic core and a governing control plane, with servicing agents on top. The core keeps the math exact, the control plane authorises, scopes, and logs every action, and the agents operate the book.

The platform

Deterministic core
Governing control plane
Loan-management ledger

The agents

Servicing agent
Monitoring & early-warning agent
Compliance & model-risk agent
Collections agent
Data-entry agents

Under load

The platform is sized for agentic load, not the human workday.

Agentic operations generate 5-10× more backend calls than human ones: an agent does not take a lunch break, and it asks the system far more questions per decision. Most lending systems in production were sized for human-speed work and start to buckle under that load. Lokta was sized for it from the foundation: idempotent writes, so the same instruction applied twice never double-charges, double-disburses, or double-books; an event-logged core, so the trail holds at volume; a governed API surface with header versioning instead of one rate-limited gate every team funnels through.

Idempotent means an action can be safely repeated: apply it twice and the book lands in the same state as applying it once. It is what keeps a retrying agent from quietly corrupting the ledger.

Read the napkin math on the 5× problem

The team

Built by the team behind Apache Fineract.

We did not arrive at this from the outside. Our team built Apache Fineract, the open-source lending core used across institutions and countries, and went on to build and scale a commercial platform that ran regulated loan books on those foundations. The Mifos/Fineract ecosystem has powered an estimated $500B+ in lending across 65M+ borrowers in 70 countries.^¹Lokta is what we would build if we started today, agent-native from the ground up.

^¹ Aggregate impact attributed to the Mifos/Fineract ecosystem, not Lokta directly.Read the impact essay

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Questions

Common questions about the platform

What is an agent-native lending platform?: An agent-native lending platform is a lending core built to be operated by agents (software that reads the loan, decides, and acts) rather than by people clicking through screens. The platform is shaped around what an agent needs to act safely: a governing control plane that authorises, scopes, logs, and verifies every action, on a deterministic core where the same inputs always produce the same outputs.
Is Lokta AI-native or agent-native?: Agent-native. Lokta is a lending core built to be run by agents, not just assisted by AI. "AI-native" describes a product with AI somewhere inside it. Agent-native means every layer assumes an agent, not a human, is at the controls, and is governed accordingly: each agent action is a governed write, passed through a control plane and checked against a deterministic core before it touches the book.
What is the governing control plane?: The governing control plane is the layer every agent action passes through before it reaches the book. It does four things for every action: authorises it, scopes it, logs it, and verifies it against policy. It provides identity for agents, maker-checker so a second authority signs off before a consequential change lands, and cross-module audit so every write sits on one trail in order. It is one of the guarantees the book runs on, together with the deterministic core it checks against. That combination is one a bolt-on AI layer structurally cannot fake.
Can Lokta migrate an existing loan book?: Yes. Legacy migrations feed the deterministic core through a disciplined, AI-assisted path you run in parallel with your live system: map the legacy schema to the canonical ontology, ETL the book across with the mapping logged, reconcile old and new until they agree to the rupee, then cut over when the evidence says you can, not before.