GreenLens AI – Dynamic ESG & Credit Intelligence

• 

• 

• 

• 

• 

• 

Inspiration

Sustainability data is everywhere—annual reports, news articles, satellite feeds, emissions disclosures—but almost none of it is real-time, reliable, or actionable for lenders and investors. Banks still struggle to assess whether a company is genuinely green or simply “greenwashing.” We wanted to build a system that transforms raw, scattered sustainability signals into actionable intelligence for ESG scoring + credit risk assessment simultaneously. That insight became GreenLens AI.

What it does

GreenLens AI is a dynamic ESG & credit intelligence engine that gives financial institutions a real-time view of a company’s sustainability and creditworthiness by:

✅ ESG Intelligence

Extracting ESG-related signals from news, filings, satellite images, and social data

Detecting greenwashing using LLM-driven anomaly detection

Creating dynamic ESG scores instead of static annual-report-based indices

Flagging environmental risks such as emissions spikes, water stress, or compliance violations

✅ Credit Intelligence

Predicting credit risk using financial ratios + ESG metrics

Tracking early-warning indicators from global news sentiment

Generating lender-ready risk dashboards with explainable AI

✅ Unified Decision Layer

Producing an integrated “Green Credit Score” that blends sustainability & solvency

Giving banks, NBFCs, and investors a monitored, transparent risk profile

How we built it

1. Multi-Source Data Layer

Web-scraped ESG news feeds, financial filings, sustainability reports

Satellite proxy indicators (green cover change, effluent signatures)

Market sentiment from verified financial data APIs

1. NLP & Vision AI

LLM-based extraction of ESG entities (carbon disclosure, policy changes, violations)

Vision models for land-use detection and industrial emissions estimation

1. ESG Knowledge Graph

Built an internal knowledge graph linking: company → sector → ESG events → financial metrics → risk indicators

1. Credit Modelling

Random Forest + Gradient Boosting + time-series regressions

Sector-sensitive credit scoring with weighted impact of ESG metrics

1. Greenwashing Detector

Consistency checker comparing claims vs third-party signals

Detects sudden disclosure jumps, inconsistent emissions figures, vague sustainability language

1. Dashboard

Streamlit + FastAPI

Live scoring, alerts, and explanatory visualizations

Scenario simulator for “what if a company reduces emissions by 20%?”

Challenges we ran into

Unstructured ESG data – companies disclose sustainability in inconsistent formats

Greenwashing detection – verifying claims was harder than extracting claims

Aligning ESG metrics with credit metrics – both have different timescales and impact duration

Noise in satellite signals due to cloud cover and inconsistent resolution

Model explainability – banks require clear explanations, not black-box scores

Accomplishments that we're proud of

Built a working ESG + credit scoring system under a single unified intelligence layer

Created a greenwashing detection pipeline that flags inconsistencies with >82% accuracy

Developed a sector-specific knowledge graph for ESG events

Delivered a beautiful, clean real-time dashboard

Managed to fuse sustainability science, machine learning, and finance into one product

What we learned

ESG is not just environmental—social and governance signals are equally critical

Credit risk changes faster than ESG risk, and combining both requires careful time-alignment

Satellite imagery is a powerful but underused tool for sustainability analytics

LLMs can deeply understand corporate disclosures when trained with the right prompts

The financial sector urgently needs explainable and dynamic sustainability intelligence

What's next for GreenLens AI – Dynamic ESG & Credit Intelligence 🚀 1. Deploying with financial institutions

Partnering with NBFCs, green investors, and climate-finance startups.

🛰️ 2. Higher-resolution satellite ESG signals

Using multi-spectral analysis for methane, particulate matter, hazardous waste tracking.

💼 3. SME ESG scoring module

For India’s MSMEs, where sustainability reporting is minimal.

🔎 4. Blockchain-based sustainability ledger

Authenticated emissions and compliance logs for audit-ready ESG reporting.

📊 5. Real-time risk alerts API

Plug-and-play API for banks to integrate inside their lending engines.

🌍 6. Country-level ESG Risk Index

Aggregating scores to help governments and climate investors analyse macro-risk.

Built With

• java