We will be undergoing planned maintenance on January 16th, 2026 at 1:00pm UTC. Please make sure to save your work.

EcoLedger Pro: High-Tech Suite for Sustainable Debt

The platform alters green lending from a manual, trust-based process into an automated, data-verified market letting banks ratify loans in minutes & investors confidently buy ESG-credible portfolios.

Comment

Inspiration

The project was inspired by the greenwashing problem in sustainable finance where investors cannot verify green debt authenticity. Traditional loan processing inefficiency and lack of borrower incentives for environmental performance created an opportunity to combine AI, OCR and blockchain technology into an integrated platform that solves multiple pain points in the green lending market simultaneously.

Operation Overview: Users originate loans by inputting borrower data for AI analysis combining financial metrics with ESG scores. Documents upload for OCR processing that extracts structured data and verifies authenticity. Approved loans package into portfolios for listing on the trading exchange where institutional buyers review verified metrics before purchase. The system continuously monitors loan covenant compliance through performance data, generating alerts for violations and automatically adjusting interest rates when borrowers achieve carbon reduction milestones. All activities record to a blockchain audit ledger providing immutable transaction history.

What it does

EcoLedger Pro automates green loan origination using AI credit scoring that combines financial metrics with ESG analysis, verifies documents through OCR processing, enables portfolio trading on a secondary market exchange, monitors covenant compliance in real-time and adjusts interest rates dynamically when borrowers achieve carbon reduction milestones. All transactions record to a blockchain ledger providing immutable audit trails for regulators and investors.

Functionality

01. AI-Powered Credit Scoring

  • Analyzes financial health metrics and ESG scores
  • Uses Random Forest, XGBoost, LightGBM and Gradient Boosting models
  • Calculates combined credit score weighted 60% financial, 40% ESG
  • Provides instant approval/rejection decisions

02. Automated Document Vault

  • Processes documents via Tesseract OCR
  • Extracts structured data from property deeds, energy certificates, financial statements
  • Classifies document types automatically
  • Calculates confidence scores for verification
  • Stores documents with SHA-256 hash integrity

03. Loan Portfolio Trading Platform

  • Creates portfolios by packaging multiple loans
  • Calculates portfolio yield, risk and ESG metrics
  • Lists portfolios on exchange for institutional buyers
  • Executes trades with settlement dates
  • Records all transactions to blockchain ledger

04. Covenant Monitoring System

  • Tracks energy savings, carbon reduction, renewable energy usage
  • Compares performance against threshold requirements
  • Generates compliance alerts for violations
  • Provides time-series performance data
  • Supports IoT sensor, utility report and audit data sources

05. Dynamic Interest Rate Engine

  • Calculates base rates from credit score, loan term and ESG factors
  • Determines milestone tier based on carbon reduction achievement
  • Applies rate discounts for energy and renewable energy bonuses
  • Calculates total borrower savings
  • Adjusts rates monthly based on performance

06. Blockchain Audit Ledger

  • Implements hash-chained block structure with SHA-256
  • Records portfolio creation and trade execution
  • Provides proof-of-work mining for block validation
  • Enables full chain validation for integrity verification
  • Maintains immutable transaction history

07. Analytics Dashboard

  • Displays loan application trends and approval rates
  • Shows ESG score distribution across portfolio
  • Visualizes portfolio performance and yields
  • Tracks carbon reduction impact by project type
  • Provides geographical distribution analysis

08. Risk Prediction

  • Calculates default probability scores
  • Segments loans into risk categories
  • Uses ensemble regression models
  • Provides risk-adjusted pricing recommendations

How we built it

The platform uses Flask API backend coordinating Python services for machine learning credit models (Random Forest, XGBoost, LightGBM), Tesseract OCR for document processing and blockchain logic for ledger management. The Electron.js frontend provides desktop interface with Chart.js and Plotly visualizations. Machine learning models train on borrower financial data, ESG scores and external World Bank API data for debt statistics, emissions and energy metrics. SQLAlchemy ORM manages PostgreSQL database operations. Databases: PostgreSQL, file system storage.

Challenges we ran into

Integrating multiple machine learning models while maintaining fast response times required careful feature engineering and model optimization. OCR accuracy varied significantly across document types, necessitating preprocessing pipelines with image enhancement and adaptive thresholding. Implementing blockchain validation without cryptocurrency infrastructure required designing a custom hash-chaining system that provides audit integrity without mining overhead. Coordinating real-time covenant monitoring with rate adjustment calculations across different time scales created synchronization complexity.

Accomplishments that we're proud of

Successfully reduced loan processing time from weeks to minutes through AI automation. Achieved 95% OCR confidence on document verification. Built working secondary market platform enabling portfolio trading with verified ESG metrics. Implemented dynamic rate engine that calculates borrower savings based on actual environmental performance. Created immutable audit trail using blockchain principles without cryptocurrency complexity. Integrated external APIs providing real emissions and debt data for credit models.

What we learned

Learned that combining multiple technologies (AI, OCR, blockchain) requires careful architectural planning to avoid bottlenecks. Discovered OCR preprocessing significantly impacts accuracy more than model selection. Understood that financial incentives drive behavior more effectively than compliance requirements alone. Recognized importance of external data validation for ESG claims. Gained experience in training ensemble machine learning models for credit risk prediction. Learned blockchain concepts can provide audit value without full decentralization.

What's next for EcoLedger Pro

Add deep learning models for improved document classification and fraud detection. Integrate IoT sensor data directly for real-time energy monitoring rather than manual data entry. Implement smart contracts for automated covenant enforcement and rate adjustments. Expand external data sources to include carbon credit markets and renewable energy certificates. Build mobile application for borrowers to track their environmental performance and savings. Add portfolio optimization algorithms using Modern Portfolio Theory for institutional investors. Integrate with existing banking core systems through APIs.

Built With

Share this project:

Updates