Give every team the same view of the loan.
Bring borrower details, repayments and risk changes onto a shared loan record. Lokta’s lending ontology connects the information your servicing, finance and collections teams need to act on an account.
Spend less time chasing mismatched loan records
When each system holds a different version of the loan, your team has to reconcile the history before it can answer a borrower or resolve an exception.
Loan origination
Keeps the loan as it was approved.
Loan management
Keeps the loan as it is serviced.
Core ledger
Keeps the loan as it is booked.
Collections tool
Keeps the loan as it is chased.
Data warehouse
Keeps the loan as it was yesterday.
Keep the account context together
Open a loan and see the borrower, approved terms, repayments and risk flags together. The canonical loan object keeps those relationships available to your teams and agents as they work.
Use the live account record to prepare a follow-up, review a restructure or explain a balance. Required approvals and the history of each change stay attached to that work.
Trust the balance behind the next action
Lokta’s deterministic core applies your configured rules consistently. Re-run the same account events with the same rules and get the same balances and postings.
Replay any decision.
Every decision can be replayed from its inputs and reproduced exactly.
Apply twice, safely.
The same instruction applied twice does not double-charge, double-disburse, or double-book.
Nothing off the record.
Every state change is recorded, in order, on an append-only trail.
The account details your teams work from
Borrower
One identity, fed by bureau, bank statements, and behavioural signals.
Application
The request, its terms, and the policy version that decided it.
Repayment
Every scheduled and actual payment, reconciled against the core and the GL.
Risk flag
The signals and breaches attached to the loan, live.
Give agents the context to prepare useful work
Agents read the same account as your team. They can prepare a borrower response or collections action with the repayment history and relevant policy already in view.
Pick up work from the live account.
Read the borrower, repayment and risk details together before preparing a response or next step.
Follow what changed and why.
Use the links between a missed payment, a borrower request and a risk flag to prepare the appropriate follow-up.
Keep approval with your team.
Route restructures, settlements and write-offs through maker-checker. Record the proposal and the approved outcome on the account.
The book sharpens itself the more it lends
Use repayment and recovery outcomes to review your servicing policy. Test a proposed treatment against the book before your team approves a new version.
Capture what happened
Repayment and recovery outcomes stay with the account that received the treatment.
Compare treatments
Test servicing and collections variants against recorded outcomes and review the differences.
Approve the next version
Your authorised reviewer promotes a policy change, with the decision preserved in the audit trail.
Work towards higher profit per loan disbursed by reviewing recovery, delinquency and servicing cost together. Use your portfolio results to decide which policy changes to make.
Put the shared record to work across servicing
Handle early warnings, restructures and borrower requests from the account where the work belongs. Your teams can follow each action through review and approval.
Real-time early warning
Move a live account onto a new servicing treatment the moment its risk profile moves.
Policy-bounded · maker-checker on consequential changes · logged
Restructuring
Reshape the terms on the shared loan record.
Maker-checker authorised · every change on the audit trail
Policy versioning and promotion
Version every policy and promote a new one cleanly.
Human sign-off to promote · the promotion is itself an audited event
Audit and regulatory reporting
Produce the record from the same trail the system ran on.
Audit-by-design · append-only · nothing reconstructed after the fact
Agentic servicing
Let agents handle servicing actions on the live loan.
Policy-bounded actions · consequential calls go to maker-checker
Continuous policy testing
Compare proposed rules against recorded loan outcomes.
Winners promoted only under maker-checker · deterministic core makes results reproducible
Built by the people who built the rails
Work with the team that helped create Apache Fineract and later built and scaled a commercial lending platform.
The open-source lending core.
Our team built it: the OSS core used across hundreds of institutions and dozens of countries.
From core to commercial platform.
The same team went on to build and scale a commercial lending platform on those foundations, run in production by lenders across multiple markets.
Move your live book onto a connected platform
Keep your existing LOS for origination and move the approved book onto Lokta through a reconciled migration. Loan Management and AI Loan Servicing then work from the shared record. Lokta Loan Origination is on the roadmap.
Keep the book in your environment.
Deploy on-prem or in your VPC. Keep account history and approval records available for your operations, risk and audit teams.
Connect through documented APIs.
Build against an OpenAPI surface and extend the platform in your own codebase as your lending products change.
Serve each lender on its own terms.
Apply per-lender policies and approvals while keeping a separate audit view for each lender you service.
Frequently asked questions
- What is a lending ontology?
- A lending ontology is one canonical model of the loan (borrower, application, repayment, and risk, plus the relationships between them) that the whole lending stack reads and writes through. Instead of a separate copy of the loan in every system, there is a single canonical loan object that governed AI and people act on, with every change logged.
- Why do LOS, LMS, and core banking systems disagree on loan data?
- They disagree because each keeps its own copy of the loan and updates it on its own schedule. The loan origination system, loan management system, core or general ledger, and collections tool all model the same loan slightly differently, so the records drift and have to be reconciled, which is slow and, in regulated lending, an audit liability.
- What is a deterministic core in lending?
- A deterministic core means the same inputs always produce the same outputs: run a decision twice and you get the same answer, with no hidden state. It is reproducible, idempotent, and event-logged, which is what makes it safe to move regulated money on and to certify after the fact.
- What does “audit-by-design” mean for a lending platform?
- Audit-by-design means the audit trail is produced by the system as it runs, not assembled afterward to satisfy a regulator. Every state change lands on one append-only log in order, so the record a compliance officer certifies is the same record the system acted on.
- What is “profit per loan disbursed”?
- Profit per loan disbursed is the outcome the architecture is designed to produce: lower delinquency, and better recovery on the accounts that still go bad, as the book learns from its own lending. Because every repayment and recovery writes back to one canonical model, agents can test and promote better servicing and collections policy under maker-checker, and the book sharpens itself the more it lends. Use your portfolio results to assess the effect of each approved policy change.
See your loan workflow on one record
Bring an account that takes your team across several systems today. We’ll walk through its repayments, servicing history and next action in Lokta with you.