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LMA-Loan-Tokenization

Explainable AI + compliant tokenization for syndicated loans—DB-backed, server-enforced controls, and real-chain settlement with Maker/Checker/Agent.

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LMA Loan Tokenization Platform

Inspiration

Syndicated loan settlement still takes ~27+ days on average, driven by manual checks, fragmented systems, and reconciliation-heavy workflows. We wanted to show what “digital-first loans” look like when you combine standardized loan data (NEL), compliant-by-design tokenization (ERC-3643 concepts), and institutional governance (Maker/Checker/Agent) without forcing users to learn crypto UX.

What it does

  • Parses uploaded loan documents into structured terms and covenant metadata using AI, with explainability (evidence snippets + confidence).
  • Standardizes extracted data into a digital representation aligned with industry protocols.
  • Tokenizes loan positions into compliant security tokens (ERC-3643-aligned model) with server-enforced eligibility checks.
  • Executes transfers through an institutional workflow: Propose → Approve/Reject → Execute, with an audit trail.
  • Persists trades, workflow events, and token balances in a database (not hardcoded demo state), surfaced via a dashboard and APIs.

How I built it

  • Frontend: Next.js (App Router) + React UI for document upload, portfolio dashboard, and workflow queue.
  • Backend: Next.js API routes for document parsing, loan lifecycle, participants, balances, and Maker/Checker/Agent workflow endpoints.
  • Data layer: PostgreSQL with Prisma for persisted system-of-record entities (loans, tokenizations, participants, trades, workflow metadata, balances).
  • Smart contracts: Solidity contracts + factory deployment pattern for token creation and settlement on EVM-compatible chains.
  • Operational model: server-side enforcement of role-gated transitions and repeated validation at approval/execution stages.

Challenges I ran into

  • Designing a workflow that feels institutional (segregation of duties) while remaining demo-friendly.
  • Keeping compliance checks enforceable server-side across multiple stages (propose/approve/execute) without relying on client trust.
  • Making AI outputs usable in regulated workflows by adding explainability (evidence + confidence) rather than “black box” extraction.
  • Preserving a clean UX while the system spans AI, databases, and blockchain components.

Accomplishments that I am proud of

  • End-to-end flow from document ingestion → tokenization → role-based trade lifecycle → settlement.
  • Institutional controls baked in (Maker/Checker/Agent) with a persisted audit trail.
  • Database-backed balances and trade state (not hardcoded), enabling realistic operational oversight.
  • A clear path to real-chain deployment (testnet/mainnet) through configuration and deployment tooling.

What I learned

  • In institutional markets, speed matters, but governance and auditability matter just as much.
  • “Explainable AI” is essential when extracted data drives compliance and trading decisions.

What's next for LMA-Loan-Tokenization

  • Pilot deployments with 2–3 early adopters on an EVM testnet or permissioned EVM, validating operational controls with compliance teams.
  • Expand jurisdictional policy modules and reporting outputs for audit/compliance workflows.
  • Integrate with existing loan ops tooling (data feeds, reporting, reconciliation, and custody workflows where applicable).
  • Hardening for production: monitoring, key management procedures, and staged mainnet rollout.

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