Early Financial Distress Detection System

Hackathon AMAD – Team Narj

Project Duration: June 2025

A machine learning–based system developed during AMAD Hackathon to predict early financial distress among bank customers. The system analyzes financial indicators and provides clear predictions with explanatory reasons through an interactive Streamlit dashboard.

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🚀 Project Overview

Banks often struggle with early detection of customers at risk of financial distress due to multiple overlapping financial factors.
This project aims to support early intervention by predicting whether a customer is financially stable or at risk, using supervised machine learning techniques.

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🛠 Technologies & Libraries

Programming Language

• Python

Data Processing & Machine Learning

• Pandas
• NumPy
• Scikit-learn
• XGBoost
• imbalanced-learn (SMOTE)

Visualization & Interface

• Matplotlib
• Streamlit

⚙️ How to Run

Step 1: Install libraries

pip install pandas numpy scikit-learn xgboost imbalanced-learn streamlit matplotlib

Step 2: Train the model

python model.py

Step 3: Run Streamlit

streamlit run main.py

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⚠️ Notes

• Educational hackathon project
• First hands-on ML experience for the team
• ChatGPT was used as a learning support tool

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👥 Team

Team Narj

• Abrar Aldakhil
• Jana Faisal Alghamdi
• Ghada Aldakhil

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🔗 Demo Links

• Streamlit App: https://project-amadhackthon-narj.streamlit.app/
• YouTube Demo: https://youtu.be/q78nvJXQLMk

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📸 Screenshot

📄 Report

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