Machine Learning Loan Flagging Prediction

Empowering Inclusive Lending With AI-Driven Risk Assessment

Access to fair and affordable credit can be life-changing for underserved communities. But how can lenders accurately evaluate risk while avoiding bias? This project explores using AI for social good in lending.

By training machine learning models on thousands of real loan applications, I built an automated system to flag high-risk loans. The algorithms assess risk based on relevant attributes like income, credit history, and loan details. But importantly, excluded factors that could introduce unfair bias like gender, ethnicity, and geography.

The result is an AI system that acts as a trusted second pair of eyes for lenders. By flagging only loans with objectively high default risk, it helps remove human biases and enables lenders to approve more applications from credit-invisible groups fairly.

This project combined my passion for machine learning and ethical data practices. The models provide explainable, unbiased risk scores that help lenders make informed approvals. In turn, more people access credit to grow businesses and build assets. It's a prime example of how AI's predictive power can be harnessed responsibly - to build an inclusive financial system and uplift communities equitably.

Here is the link to the project: https://github.com/ayusuf9/Loan-Flagging-Prediction