The connected lending stack — built for Lending Service Providers (LSPs).
Lending on autopilot. The book sharpens itself.
One platform that runs the full lending operation — underwriting, pricing, disbursal, collections — end to end. AI fast where it helps; a deterministic core where it has to be. From the team behind Apache Fineract and Finflux.
Walk a lender end to end and the same story shows up at every desk in different vocabulary. What sounds like a usability complaint on the front line is architectural debt in engineering and concentration risk in the boardroom. Lokta is built to address all three.
"I literally have to keep a separate PC just for this."
Front-line teams burn hours every day on systems that crash, take minutes to open, and force re-keying across origination, servicing, and collections.
"Three months to add a field. Six to integrate anything new."
The backend was sized for human-speed operations. Every new agentic workload, every regulator change, every integration becomes a multi-quarter project.
"You only get one shot to get this right."
Risk teams ship one policy change a quarter while fintechs ship in weeks. Credit models go stale the moment they're deployed. And the cost of operating the book scales with the book — exactly the dynamics that turn a margin business into a thin one.
Tell Lokta your product and risk appetite. Agents stand up the full operation — underwriting, pricing, disbursal, collections — and run it end to end. Then they never stop improving it: every application, repayment, and recovery feeds a richer borrower model, agents test hundreds of underwriting rules in the time a risk team ships one, and winners get promoted automatically.
None of this works on a guessable core. The math, the state transitions, the postings — those have to be deterministic. AI lives around the core, not inside it: workflow AI for speed, agentic AI for end-to-end work, both inside policy guardrails. AI assists the system. It never becomes it.
Action-bounded. Workflow-gated. Fully observable. Agents are first-class principals — scoped, revocable, maker-checker aware — pulling approved levers under policy.
Read: Agentic Lending and the 5× Problem →Smart intake, policy-guided memos, collections guidance — guided, bounded, reviewable. Workflow AI makes the human faster. It never makes the decision the regulator will ask about.
Read: AI Philosophy in Lending →The ledger you bet the business on. Math is absolute. State transitions are explicit. Every action logged with what, when, and why — captured before the response goes out, not assembled after the fact.
Read: Why Lokta Builds on a Deterministic Core →We have all named these before. Lokta was built to inherit none of them.
Origination data lives one place, servicing data lives another, collections lives a third — and the analytics team rebuilds the truth in a spreadsheet every Monday.
Customizations only the original SI consultant remembers. If-statements written for a regulator change that came and went five years ago. Every new feature risks breaking ten older ones.
One giant gate where every team funnels through the same rate-limited, undocumented surface. Every breaking change is felt by the entire bank at once.
Lokta inverts all three. Customer, loan, risk, and collections are modelled once and used everywhere. Originate, Core, Delight, and your analytics layer all read from — and write back to — the same canonical state through a governed API surface with header versioning, not a monolithic gate. Modules are polylithic — extend or replace one piece at a time, without setting fire to the rest.
Same data. Same context. Same truth.
What is connected lending? →Lokta Core is the source of truth your book runs on. Lokta Originate puts a modern LOS on the same rails. Lokta Delight is the AI servicing agent that operates inside both.
Loan Management System. The deterministic ledger.
Audit-ready by design. Schema-per-tenant isolation. Governed API surface with header versioning. Identity for agents through Keycloak. Cross-module audit trail and field-level PII encryption with key versioning.
Loan Origination System.
Digital journeys, KYC and verification flows, credit memos — with human approvals where they matter, and audit trails everywhere they don't.
See Lokta Originate →AI agent for loan servicing.
Unified inbox across WhatsApp, email, phone, and portal. Confidence-based automation — shadow, then draft, then autonomous. Lending-specific triage in mixed-language conversations.
Whether you're a bank running multi-product books, a fintech launching at fortnight cadence, or an LSP servicing five lenders at once — Lokta is engineered for the shape of your operation, not retrofitted to it.
For lenders who carry the book
You operate the binary — on-prem, in your VPC, or single-tenant cloud. Schema-per-tenant isolation. Field-level PII encryption with key versioning by default. Maker-checker on every policy boundary. Audit trails baked into every state transition — not assembled after the fact.
For teams without legacy debt
Modern primitives, API-first, no migration to dread. Polylithic Gradle modules — extend or replace one piece at a time. OpenAPI 3.1 with header versioning. Keycloak OIDC native. Built for engineers who extend in-tree, not vendor-gate.
For the operators serving multiple partners
One stack across multiple lender partners. Per-partner SOPs, isolation, and audit trails. Co-lending reconciliation native to the data model. Source-agnostic ontology — Delight works with any LMS your partners already run. MCP-first integrations for the partners you'll add next.
LSPs aren't running a single book — you're servicing five lenders at once, each with their own SOPs, partner taxonomy, and audit needs. Lokta is built around that reality, not retrofitted to it.
Tenant isolation by client_id runs through every record — cases, messages, knowledge, audit logs, dashboards. Add a new partner without changing code.
Triage understands KYC, disbursement, repayment, NACH/ECS, foreclosure, hardship, settlement — and what those look like at each lifecycle stage. Not retrofitted from generic CX.
Every AI decision logged with confidence, tools used, and human override. PII masked at ingest. Verbatim templates the AI can reference but never rewrite.
Lokta is built by the original architects of Apache Fineract and Finflux. The Mifos/Fineract ecosystem reached an estimated ~$500B — half a trillion dollars — in cumulative loan principal across 65M+ borrowers in 70 countries.¹ Finflux, our commercial platform, served 60+ lenders across 15 countries and 12M+ borrowers before M2P acquired it in 2022. We learned, at scale, what the next generation has to be.
Co-founder
"You cannot run a bullet train on narrow-gauge rails. The track width is the architecture. That's why we started over."
¹ Aggregate impact attributed to the Mifos/Fineract ecosystem, not Lokta directly. Read the impact essay →
Lokta Core is enterprise-ready. We deploy with a small number of institutions at a time — banks, NBFCs, and fintechs that want a deep, deliberate adoption rather than a vendor SaaS sign-up. Direct line to the people writing the code.
A small cohort, deeply engaged. Not a self-serve flow.
Long-form essays on lending architecture, AI governance, and what we learned the hard way. No newsletter pop-ups.
The math no one is doing. Why agentic operations generate 5–10× more tool calls per account, and why most LMS platforms in production today won't clear that wall.
Read → AI PhilosophyThe system of record cannot guess. How Lokta keeps the math deterministic and lets governed AI live around it — assisting, not autopiloting.
Read → Fineract HeritageA decade powering Apache Fineract revealed three architectural constraints we couldn't fix incrementally. So we rebuilt.
Read → Where Lending Is GoingA practitioner's view on where AI actually pays off across underwriting, operations, and collections — and the vision of programmable, agent-mediated credit that we're building toward.
Read →Lokta Core is enterprise-ready and deploys under engagement. We work with a select group of institutions through a founder-led model — deep adoption, deliberate scope, a delivery window the team commits to in writing.