63 million MSMEs. ₹20–25 trillion credit gap. Zero early warning system. MSMEWatch is the analytical layer that should exist between raw financial data and credit decisions.
India's MSMEs contribute 29% of GDP and employ over 110 million people — yet face a ₹20–25 trillion credit gap. Banks rely on collateral and credit history. Most MSMEs have neither.
By the time an MSME defaults, it's too late. MSMEWatch flags stress signals before the default — using the same 5 financial ratios credit analysts at PSU banks and NBFCs already use.
GST compliance is the single biggest differentiator in MSME financial health.
| GST Status | Avg Financial Stress Score |
|---|---|
| ✅ GST Compliant | 78.2 / 100 |
| ❌ GST Non-Compliant | 64.6 / 100 |
| Compliance Premium | +13.6 points |
A 13.6 point gap — from a dataset of 300 businesses across 6 sectors — that banks should be pricing into every credit decision.
MSMEWatch calculates a Financial Stress Score (0–100) using 5 ratios — the same methodology credit analysts at PSU banks and NBFCs use.
| Ratio | Weight | What It Measures | Green Threshold |
|---|---|---|---|
| 📈 Current Ratio | 25% | Ability to pay short-term obligations | ≥ 1.5 |
| 💳 DSCR | 25% | Debt service coverage — can they repay loans? | ≥ 1.5 |
| 💰 Profit Margin | 20% | Revenue remaining after all costs | ≥ 15% |
| 🏦 Debt-to-Revenue | 15% | Debt load relative to income | Lower is better |
| 🧾 GST Compliance | 15% | Filing on time = financial discipline signal | Compliant |
70 – 100 ██████████████████ 🟢 GREEN — Low Risk
45 – 69 ████████████ 🟡 AMBER — Watch
0 – 44 ██████ 🔴 RED — High Risk / Immediate Action
Total MSMEs Analysed → 300
Average Stress Score → 74.9 / 100
Sectors Covered → 6
Cities Mapped → 15
| Status | Count | Portfolio % |
|---|---|---|
| 🟢 Low Risk | 198 | 66% |
| 🟡 Watch | 92 | 31% |
| 🔴 High Risk | 10 | 3% |
| Sector | Avg Score | High Risk | Status |
|---|---|---|---|
| 🏥 Healthcare | 77.7 | 0 | 🟢 Safest |
| 🏗️ Construction | 77.3 | 1 | 🟢 Low Risk |
| 🏭 Manufacturing | 75.4 | 2 | 🟢 Low Risk |
| 🍱 Food & Beverage | 75.1 | 2 | 🟡 Watch |
| 💻 IT Services | 72.2 | 2 | 🟡 Watch |
| 🛒 Retail Kirana | 71.1 | 3 | 🔴 Most Stressed |
msmewatch/
├── data/
│ ├── msme_data.csv # 300 MSME synthetic dataset
│ ├── scored_msmes.json # Scored output with all ratios
│ └── msmewatch.db # SQLite financial database
│
├── scripts/
│ ├── generate_msme_data.py # Dataset generator — RBI/SIDBI calibrated
│ ├── scoring_engine.py # 5-ratio Financial Stress Score calculator
│ ├── load_to_sqlite.py # Loads CSV → SQLite (2 tables)
│ └── analysis.py # Sector analysis, charts, key insights
│
├── screenshots/ # Platform screenshots
│ ├── 1.homepage.png
│ ├── 2.overview.png
│ ├── 3.dashboard.png
│ ├── 4.MSME_lookup.png
│ ├── 5.Risl_monitor.png
│ └── 6.About.png
│
└── msmewatch-app/ # React 19 frontend
└── src/
└── App.js # Full platform — 6 pages, bold brutalist UI
| Layer | Tool | Purpose |
|---|---|---|
| 🐍 Dataset | Python (random, csv) |
300 synthetic MSMEs, RBI/SIDBI calibrated |
| 🗄️ Database | SQLite | msme_master + msme_scores tables |
| 📊 Analysis | pandas + matplotlib | Sector analysis, deviation charts |
| ⚛️ Frontend | React 19 | Bold Brutalist dashboard — 6 pages |
| 🚀 Deployment | Vercel | Production deployment |
| 🔧 Version Control | GitHub | Full project history |
All synthetic data is calibrated against real Indian regulatory and research benchmarks:
- 🏦 RBI — MSME credit and financial ratio guidelines
- 📑 SIDBI MSME Pulse Reports — Sectoral benchmarks
- 🧾 GST Portal — Compliance rate statistics
- 📋 PLFS Annual Reports (MoSPI) — Labour and business data
- ⚖️ Fairwork India 2024 — Platform and gig economy data
# Clone the repo
git clone https://github.com/jessicamathew31-coder/-msmewatch.git
cd -msmewatch
# Generate dataset
python3 scripts/generate_msme_data.py
# Run scoring engine
python3 scripts/scoring_engine.py
# Load to SQLite
python3 scripts/load_to_sqlite.py
# Run analysis
python3 scripts/analysis.py
# Start React dashboard
cd msmewatch-app
npm install
PORT=3001 npm start- "I built a financial stress early warning system for MSMEs using the same 5 ratios credit analysts at PSU banks and NBFCs use"
- "My analysis found that GST compliance alone accounts for a 13.6 point score advantage — a data-backed finding from a dataset of 300 businesses"
- "The Bold Brutalist UI was a deliberate design choice — MSMEWatch is meant to feel like a tool a banker would actually use, not a student project"
- "The project covers the full BA stack — data generation, SQL analysis, Python scoring, financial ratio methodology, and a deployed interactive dashboard"
| Project | Description | Live |
|---|---|---|
| GigLens | India's Gig Worker Financial Health Audit Engine | giglens-74r9.vercel.app |
| MSMEWatch | India's MSME Financial Stress Intelligence Platform | msmewatch.vercel.app |
Jessica Mathew — MBA Finance & Technology, MIT ADT University, Pune (2025)
CBAP CAP Microsoft Project Management Advanced Excel Power BI SQL Python React
MSMEWatch is a production-grade portfolio project that demonstrates end-to-end Business Analyst capability — from data generation and financial modelling to SQL analysis, Python scoring engines, and a deployed interactive dashboard.
Built with ◈ by Jessica Mathew
300 MSMEs · 6 Sectors · 15 Cities · RBI/SIDBI Calibrated