1. [Home](https://www.g2.com/) 2. ... 3. [Loan Management Software](https://www.g2.com/categories/loan-management) 4. [Lokta Core - Loan Management System Discussions](https://www.g2.com/products/lokta-core-loan-management-system/discuss) [ ![Product Avatar Image](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_14699cc0b3e46dcf362479bb6c85d5ac/lokta-core-loan-management-system.png "Product Avatar Image") ](/products/lokta-core-loan-management-system/reviews) [ Lokta Core - Loan Management System ](/products/lokta-core-loan-management-system/reviews) (3)4.8/5 The profit in a loan is won after approval. Most lending software was built for the decision to lend, and stops paying close attention once the money is out the door. Lokta is the agentic loan servicing platform for lenders, fintechs and lending service providers who run a live book: servicing, monitoring and early warning, collections and recovery, inside the lender's own guardrails. The economics are the point. Servicing cost usually grows with the book, because more accounts mean more people, and that is the shape we are built to change. Every repayment and recovery makes the next servicing decision better informed, inside the policy bounds you set, so the book sharpens itself the more it lends. The metric we hold ourselves to is profit per loan disbursed. Agents do the day-to-day work of the book. A governing control plane, the layer every agent action passes through, authorises, scopes, logs and verifies it. A deterministic core does the math the AI never gets to guess at. AI proposes, the core decides, and the record proves it. The platform runs four agents: servicing, monitoring and early warning, collections, and compliance and model risk. None of them posts to the ledger on its own. Your core does, under your policy, with maker-checker, so one person proposes and another approves. Underneath sits Lokta Ledger, our loan management system: one double-entry, event-sourced loan engine exposed as 190 configuration parameters across 19 domains, from interest method and day-count to appropriation waterfall, moratorium, tranches, foreclosure regime and NPA classification. It runs the full servicing lifecycle: disbursement, schedules and repayments, restructuring, moratorium, asset classification from standard through sub-standard, doubtful and loss, and write-offs. Event-sourced means any book can be replayed entry by entry from its own history. A new product variant is a configuration change, not a release. Built for regulated lenders. Schema-per-tenant isolation, so each lender's data sits in its own database schema. Role-based access control with maker-checker. Cross-module structured audit on one ordered trail. Field-level encryption on personal data, with key versioning. Polylithic Spring Boot modules behind header-versioned OpenAPI 3.1, so your engineers extend it in-tree. It runs on your existing LMS or on Lokta Ledger, in the cloud or private network you choose, with the AI models you approve. For lenders in India, Regulator-readiness is a configuration question, not a custom-build project: data residency, access control and a complete audit trail are settings you own. Agents test hundreds of collection and servicing strategies in the time a risk team ships one, and promote the winners inside the policy bounds you set, so the book sharpens itself the more it lends. Built by the team behind Apache Fineract, the open-source lending core used across 70 countries and, cumulatively, by more than 65 million borrowers. Lokta is what we would build if we started today, agent-native from the ground up. 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