LoanEdge

LoanEdge is the world’s first patented ESG loan intelligence platform, unifying ESG compliance, settlement prediction, and document intelligence into a single machine-learning system.

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Inspiration

The global surge in sustainability-linked loans created a $322B market—but also a major problem: banks had no reliable way to verify ESG performance or detect greenwashing, and no system connected ESG compliance to settlement outcomes. This gap caused billions in mispriced risk, inconsistent reporting, manual document reviews, and delays stretching from T+7 to T+22.

At the same time, our team had just secured a patent that legally protects the integration of ESG verification and settlement intelligence for 20 years. That patent became the spark:

What if we built the world’s first platform that not only evaluates ESG credibility but predicts its real financial impact? LoanEdge (www.loanedge.ai) was born from that idea—combining ML, NLP, ESG science, and fintech engineering into a single intelligence platform.

What it does

Building LoanEdge forced us to deeply understand: How ESG KPIs flow through loan agreements How greenwashing patterns appear in unstructured text Why settlement delays occur and how to quantify them How risk propagates across multi-bank syndicates How sustainability performance affects margin pricing Why current systems (ClearPar, Bloomberg, Versana) don’t capture ESG–settlement interactions

We also learned the limitations of today’s workflows: manual tracking, fragmented systems, and data that lives in PDFs instead of structured fields. That shaped our design decisions and reinforced just how large the opportunity is.

How we built it

LoanEdge is built as a modular intelligence platform with three core engines:

  1. Document Intelligence (NLP Engine) We developed an advanced NLP pipeline that can parse: Loan agreements Sustainability-linked KPIs Margin ratchets Covenant packages Amendment terms ESG verification requirements Using a hybrid of rule-based methods and transformer models, we extract structured data from complex PDFs, Word files, and even scanned images.

  2. ESG Compliance & Greenwashing Detection

We created a 0–100 ESG Integrity Score combining: KPI verifiability Sustainability target alignment Borrower track record Disclosure consistency Keyword + semantic risk indicators This directly feeds the patented ESG–settlement linkage model.

  1. Settlement Intelligence (ML Delay Prediction)

Using historical patterns and engineered features, we trained a model achieving: 87% accuracy in predicting T+ delays $1,500–$18,600 impact estimates Risk factor decomposition Optimization windows for loan ops teams

This system works in real time across live portfolios.

Front-End Engineering React + TypeScript Vite for blazing-fast builds Tailwind CSS for a clean, modern UI Recharts for interactive dashboards Supabase for authentication and (planned) real-time data sync

Challenges we ran into

  1. Parsing messy loan documents Loan agreements vary wildly in format and structure. Extracting KPI definitions, margin rules, and settlement clauses required multi-stage NLP, fallback heuristics, and iterative model tuning.

  2. Ensuring model explainability Banks cannot trust a “black-box” prediction. I built: Feature attribution Risk factor breakdown Transparent KPI scoring Margin impact calculations

  3. Designing around the patent To fully leverage the patent, we architected the system so ESG and settlement intelligence mutually reinforce each other. This required unique data flows unlike any existing loan platform.

  4. Building a platform for banks, not tech demos Banks need security, RLS, auditability, and reliability. Designing a prototype that feels like a production-grade system was a challenge but also a major achievement.

Accomplishments that we're proud of

Secured patent for integrating ESG compliance and loan settlement intelligence Built a document intelligence engine capable of extracting KPIs, covenants, margin ratchets, and ESG terms from messy, unstructured loan agreements. Achieved 87% prediction accuracy on settlement delays, with real-time T+ risk forecasts and financial impact modeling. Designed the first platform that links greenwashing risk → ESG integrity scores → settlement outcomes, creating a unified view of loan performance. Engineered a production-ready React + TypeScript interface featuring dashboards, loan cards, ESG scoring, and settlement analytics with smooth, responsive UI. Delivered a working demo capable of analyzing $1.5B+ in loan value, visualizing trends, and surfacing insights that no existing platform can provide. Successfully validated the platform architecture against enterprise requirements (RLS, auditability, scalability). Created a differentiated, premium brand and clear market position: LoanEdge — The Edge in ESG Loan Intelligence.

What we learned

ESG data is only as strong as the documents behind it — NLP extraction is critical to building trustworthy ESG intelligence.

Settlement delays are surprisingly predictable when you combine: borrower behavior document covenants ESG performance indicators operational patterns Banks struggle not from a lack of data, but from fragmented systems that don’t connect ESG, operations, and legal documents. Designing for financial institutions requires explainable ML, audit trails, and transparent risk decomposition — not just accuracy. The patent fundamentally shapes product design: to maximize value, ESG insights and settlement logic must operate as interconnected intelligence engines, not isolated modules. Sustainability-linked loans suffer from greenwashing confusion, inconsistent KPI reporting, and manual compliance verification — creating a massive opportunity for automated integrity scoring. A clean UI matters: lenders expect clarity, confidence, and credibility on every screen, especially when high-stakes decisions are involved. Bridging ESG metrics with financial outcomes (delays, margin impact, compensation) transforms ESG from a reporting burden into a profit-and-risk driver.

What's next for LoanEdge

Backend Production Build Implement Supabase/Postgres architecture, user authentication, RLS, and real-time data syncing. Enterprise-Grade API Integrations Connect to ClearPar, Versana, Bloomberg, and sustainability data providers to automate ingestion. NLP v2 Engine Contract clause segmentation ESG keyword/semantic comparison against science-based targets Automated margin ratchet detection AI/ML Enhancements Additional settlement models ESG anomaly detection Confidence thresholds and smart alerting Multi-Tenant Architecture Enable secure onboarding of the first institutional pilot customers. SSO + Compliance Features SAML, audit logs, version control, and governance workflows for regulated lenders. Scenario Modeling Simulate ESG performance changes and their expected impact on pricing, margin adjustments, and settlement timelines. Investor & Pilot Engagement Prepare the platform for early banking partners and sustainability-linked loan desks.

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