Project Z-Score: Dynamic Trust-Based Credit Framework

A hackathon prototype for PSB's FinTech Cybersecurity Hackathon 2025 - Credit Risk Management Track

Overview

Project Z-Score addresses India's dual crisis of credit exclusion (451M individuals lack formal credit access) and rising microfinance delinquencies (163% YoY surge in defaults). Our solution combines alternative data sources, explainable AI, and gamified financial literacy to create a comprehensive credit infrastructure for the underbanked.

Key Innovation

• Trust-Based Scoring: Dynamic assessment using alternative data (utility bills, social proof, digital footprints)
• Explainable AI: SHAP-powered decision transparency for regulatory compliance
• Gamified Journey: Transform credit building from intimidating process to engaging experience
• DPDPA Compliant: Built with privacy-by-design and regulatory compliance as core principles

Team Z-Row

Quick Start

Installation

1. Clone and setup:

   git clone https://github.com/Rizzy1857/Z-Cred.git
   cd Z-Cred
   make setup-dev

2. Run the application:

   make run          # Main application
   make run-user     # User interface
   make run-admin    # Admin dashboard

3. Run tests:

   make test         # Run all tests
   make test-cov     # With coverage

For detailed setup instructions, see DEVELOPMENT.md.

Features

Core Functionality

• Dynamic Trust Scoring: Multi-component assessment (Behavioral, Social, Digital Trust)
• Obscurity Model: Guided journey from credit-invisible to scorable status
• ML Pipeline: Logistic Regression baseline + XGBoost ensemble with SHAP explainability
• Gamification: Z-Credits system with missions, achievements, and Trust Bar visualization

Compliance & Security

• DPDPA Compliance: Granular consent management, data minimization, withdrawal mechanisms
• RBI Guidelines: Direct fund flow, cooling-off periods, Key Fact Statement generation
• Secure Architecture: PBKDF2 password hashing, session management, input validation

Alternative Data Integration

• F1 - Payment History: BBPS utility payment data for financial discipline assessment
• F2 - Loan Performance: MFI/NGO loan history from informal/semi-formal sources
• F3 - Social Proof: Community trust metrics from SHG/NGO endorsements
• F4 - Digital Footprint: Telecom data, transaction SMS patterns, device stability

Technical Architecture

Stack

• Frontend: Streamlit with custom CSS for professional UI
• Backend: Python with SQLite for offline-first architecture
• ML Pipeline: scikit-learn, XGBoost, SHAP for explainable predictions
• Security: bcrypt, session management, DPDPA-compliant consent flows
• Visualization: Plotly, Matplotlib for interactive charts and explanations

Project Structure

zscore/
 app.py                 # Main Streamlit application
 auth.py               # Authentication system
 local_db.py           # Database operations
 model_pipeline.py     # ML models and training
 requirements.txt      # Dependencies
 README.md            # This file
 data/
     applicants.db    # Main SQLite database
     sample_data/     # Demo datasets

Installation & Setup

Prerequisites

• Python 3.8+ (recommended: Python 3.10+)
• Git
• 4GB+ available disk space
• 8GB+ RAM (recommended for optimal performance)

Quick Start (Recommended)

Use our automated setup script for the fastest deployment:

# Clone the repository
git clone <repository-url>
cd Z-Cred

# Run automated setup and launch
./start.sh

# Alternative: Skip setup if already configured
./start.sh --skip-setup

# Start specific application
./start.sh --app=user    # User interface (port 8502)
./start.sh --app=admin   # Admin interface (port 8503)
./start.sh --app=main    # Main interface (port 8501)

Manual Setup (Advanced Users)

1. Clone the repository

   git clone <repository-url>
   cd Z-Cred

2. Create virtual environment

   python3 -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install dependencies (pinned versions)

   pip install --upgrade pip
   pip install -r requirements.txt

4. Initialize database and demo data

   python setup_demo_data.py

5. Cache SHAP explainers (optional, for better performance)

   python -c "
   from model_integration import model_integrator
   from shap_cache import cache_shap_explainers
   model = model_integrator.get_credit_model()
   cache_shap_explainers(model)
   print(' SHAP explainers cached for optimal performance')
   "

6. Run the application

   # Main application
   streamlit run app.py --server.port 8501
   
   # User-focused interface
   streamlit run app_user.py --server.port 8502
   
   # Admin interface
   streamlit run app_admin.py --server.port 8503

7. Access the application

   • Main App: http://localhost:8501
   • User App: http://localhost:8502
   • Admin App: http://localhost:8503

Production Deployment

For production deployment, use the optimized commands:

# Build production environment
python -m venv prod_env
source prod_env/bin/activate
pip install -r requirements.txt

# Run with production settings
streamlit run app.py --server.port 8501 --server.headless true \
  --server.enableCORS false --server.enableXsrfProtection true

# Or use the launcher script
python launcher.py --production --port 8501

Development Setup

For development with hot-reload and debugging:

# Install development dependencies
pip install pytest pytest-cov black flake8

# Run tests
python -m pytest test_unified_scoring.py -v
python -m pytest test_*.py

# Code formatting
black *.py

# Performance profiling
python -c "
import cProfile
import app_user
cProfile.run('app_user.main()', 'profile_output.prof')
"

Docker Setup (Optional)

# Build Docker image
docker build -t z-cred .

# Run container
docker run -p 8501:8501 z-cred

# Docker Compose for full stack
docker-compose up -d

Troubleshooting

Common Issues:

1. Port already in use:

   # Find and kill process using port
   lsof -ti:8501 | xargs kill -9
   # Or use different port
   streamlit run app.py --server.port 8504

2. Database locked errors:

   # Reset database
   rm data/applicants.db
   python setup_demo_data.py

3. SHAP explainer errors:

   # Clear SHAP cache
   rm -rf cache/shap/
   python -c "from shap_cache import shap_cache; shap_cache.clear_cache()"

4. Memory issues:

   # Run with memory optimization
   export STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
   streamlit run app.py --server.maxUploadSize 1

Performance Optimization

For optimal performance:

1. Pre-cache SHAP explainers: Run the caching command above
2. Use SSD storage: Ensure database is on SSD for faster I/O
3. Increase RAM: 8GB+ recommended for large datasets
4. Browser cache: Enable browser caching for faster subsequent loads

Verified Environments

Tested Configurations:

• macOS 12+ with Python 3.10+
• Ubuntu 20.04+ with Python 3.8+
• Windows 10+ with Python 3.9+
• Docker on Linux/macOS

Performance Benchmarks:

• Cold start: <30 seconds
• Warm start: <5 seconds
• Trust score calculation: <1 second
• SHAP explanation: <2 seconds (cached)
• UI response time: <500ms

Demo Scenarios

Z-Cred showcases three realistic scenarios representing different segments of India's credit-invisible population, each demonstrating unique aspects of alternative credit scoring.

Scenario 1: Rural Entrepreneur (Meera - SHG Leader)

Profile: 32-year-old handicraft artisan from Rajasthan, leading a Self-Help Group Credit Need: ₹25,000 for business equipment expansion Trust Score: 77/100

Key Demo Points:

• Social Proof: SHG leadership and community endorsements
• Payment History: Consistent utility bill payments despite seasonal income
• Alternative Data: Government scheme participation and community ratings
• AI Explanation: SHAP shows community trust as top positive factor

Login: meera@selfhelp.in / demo123

Scenario 2: Urban Gig Worker (Arjun - Delivery Partner)

Profile: 26-year-old food delivery partner from Bangalore Credit Need: ₹80,000 for electric vehicle purchase Trust Score: 83/100

Key Demo Points:

• Digital Footprint: High platform ratings (4.7+ on Swiggy/Zomato)
• Income Diversification: Multiple gig platform earnings
• Real-time Data: GPS tracking, transaction velocity, customer feedback
• AI Explanation: Platform consistency drives high trust score

Login: arjun@delivery.in / demo123

Scenario 3: Small Business Owner (Fatima - Tailoring Business)

Profile: 38-year-old tailoring business owner from Kerala with 12 years experience Credit Need: ₹1,50,000 for business expansion and equipment Trust Score: 85/100

Key Demo Points:

• Business Track Record: 12-year operational history with growth trajectory
• Customer Loyalty: 89% retention rate, excellent online reviews
• Financial Discipline: Perfect rent payments, supplier relationship management
• AI Explanation: Business stability and customer trust drive approval

Login: fatima@tailoring.in / demo123

Demo Flow

1. User Selection: Choose from three distinct personas
2. Trust Score Analysis: See breakdown of behavioral, social, and digital components
3. AI Explanations: SHAP-powered transparency for each decision factor
4. Credit Journey: Gamified missions and achievements for score improvement
5. Compliance Demo: DPDPA consent management and data transparency

Quick Access: All demo documentation available at docs/DEMO_SCENARIOS.md

Key Metrics & Performance

Model Performance (Synthetic Data)

• Logistic Regression: AUC ~0.92, F1-Score ~0.89
• XGBoost Ensemble: AUC ~0.96, F1-Score ~0.93
• Response Time: <1s per applicant assessment
• Explainability: 100% decisions explained via SHAP

Business Impact Potential

• Target Addressable Market: 451M credit-invisible Indians
• Partner Ecosystem: MFIs, NGOs, Rural Banks, SHGs
• Risk Reduction Goal: 15-20% improvement in Portfolio at Risk (PAR)

Regulatory Compliance

DPDPA 2023 Compliance

• Valid consent (free, specific, informed, unambiguous)
• Purpose limitation (credit assessment only)
• Data minimization (collect only necessary data)
• Data localization (India-based storage)
• Consent withdrawal mechanisms

RBI Digital Lending Guidelines 2025

• LSP partnership model with regulated entities
• Direct fund flow (no intermediary handling)
• Key Fact Statement generation
• Mandatory cooling-off period
• Grievance redressal mechanism

Development Status

Completed Features

• Authentication system with role management
• SQLite database with offline-first architecture
• ML pipeline with Logistic Regression + XGBoost
• Trust scoring framework (Behavioral, Social, Digital components)
• Basic gamification (Z-Credits, Trust Bar)
• DPDPA-compliant consent management
• Professional Streamlit UI

In Progress

• SHAP integration for explainable AI
• Advanced visualizations (Trust Bar animations, SHAP plots)
• Demo data scenarios refinement

Planned Enhancements

• PDF credit reports generation
• Offline/online data synchronization
• Mobile-optimized interface for field agents
• Account Aggregator framework integration

Usage Guide

for detailed usage guide check Usage Documentation

For Developers

1. Model training: python model_pipeline.py
2. Database reset: python -c "from local_db import reset_database; reset_database()"
3. Add sample data: python -c "from local_db import add_sample_data; add_sample_data()"

API Documentation

Core Functions

• calculate_trust_score(applicant_data): Returns Trust Bar components and overall score
• predict_credit_risk(features): ML prediction with confidence intervals
• generate_shap_explanation(features, prediction): Explainable AI breakdown
• log_consent(user_id, consent_type, granted): DPDPA compliance logging

Refer detailed documentaion API Documentation

Contributing

This is a hackathon prototype. For the competition:

1. Focus on demo-ready scenarios
2. Prioritize visual polish and stability
3. Ensure all compliance features work smoothly
4. Test end-to-end user journeys

Refer detailed documentaion Contribution Documentaion*

License

MIT License

Support

For project-related questions:

• Team Lead: Rizzy

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Built for PSB's FinTech Cybersecurity Hackathon 2025 - Credit Risk Management Track

Empowering India's underbanked through trust, technology, and transparency.