MicroTrust — AI-Powered Alternative Credit Scoring
MicroTrust is an AI-driven platform that enables financial inclusion for individuals without traditional credit history. By analyzing alternative behavioral signals such as transaction consistency, bill payment patterns, and income stability, MicroTrust generates a trust-based credit score that helps lenders evaluate creditworthiness beyond conventional systems.
Unlike traditional credit models that rely on past debt, MicroTrust evaluates everyday financial behavior to build a fairer and more inclusive credit system.
Live Product Experience
MicroTrust is designed as a complete user-facing platform, allowing users or financial institutions to generate a trust score directly through an intuitive interface.
Users can:
Enter behavioral financial signals through the app
Generate a MicroTrust Score instantly
View risk classification and model confidence
Understand which behaviors influenced their score
This transforms complex machine learning predictions into a clear, transparent financial trust profile.
Demo Flow
The platform works through the following pipeline:
User Interface (Frontend App) ↓ Node.js Backend API ↓ Python ML Scoring Service ↓ MicroTrust Score + Risk Insights
A user enters behavioral signals through the frontend interface, which are processed by the backend and evaluated by the AI scoring engine to produce an interpretable credit score.
Core Features Full Product Interface
MicroTrust includes a fully functional frontend application that allows users to interact with the scoring system in a simple and intuitive way.
Users can:
Input behavioral financial indicators
Generate a trust score instantly
View risk classification and explanations
This makes the platform accessible to both lenders and individuals.
AI Ensemble Credit Scoring Engine
The platform uses an ensemble machine learning model combining:
Logistic Regression Provides interpretable probability estimates and transparent decision logic.
Random Forest Captures complex nonlinear relationships between financial behavior signals.
The models are combined using probability averaging to produce a robust and stable credit risk prediction.
Explainable AI
MicroTrust provides transparency by returning key behavioral factors that influenced the score.
Example response:
{ "microtrust_score": 742, "risk_bucket": "Low Risk", "model_confidence": 0.87, "top_risk_factors": [ "low_bill_payment_regularity", "low_upi_consistency" ] }
This allows both lenders and users to understand why a score was assigned.
Model Evaluation
The system evaluates model performance using standard machine learning metrics:
ROC-AUC – Measures how well the model distinguishes between reliable and risky users.
F1 Score – Balances precision and recall to ensure fair classification.
Our ensemble model achieves strong performance with a ROC-AUC above 0.9, demonstrating effective risk prediction.
Technology Stack Frontend
Flutter / React Native (or specify your framework)
Responsive UI for interactive scoring experience
Backend
Node.js
Express.js
REST API architecture
Machine Learning Service
Python
FastAPI
Scikit-learn
NumPy
Pandas
ML Models
Logistic Regression
Random Forest
Ensemble probability scoring
Infrastructure
Microservice architecture
Cloud deployment (Render)
Running the Project Locally
Clone the repository:
git clone cd microtrust Start the ML Scoring Service cd ml-service python -m venv .venv source .venv/bin/activate pip install -r requirements.txt uvicorn app.main:app --reload
The ML API will be available at:
http://127.0.0.1:8000/docs Start the Backend Server cd backend npm install npm start Start the Frontend App cd frontend npm install npm start
The application interface will launch locally, allowing users to interact with the MicroTrust scoring system.
Impact
MicroTrust aims to bridge the gap between the informal economy and formal financial systems by transforming everyday financial behavior into measurable trust signals.
By leveraging AI, explainable models, and alternative financial data, MicroTrust enables fairer access to credit for underserved populations.
Team
Built by a team of engineers focused on AI, fintech, and financial inclusion.