AI-powered-Financial-Fraud-Detection-System

What it does

With the rise of digital payments, detecting financial fraud has become more critical than ever. Our AI-powered Financial Fraud Detection System efficiently analyzes transaction data in real-time to flag potential fraud. By analyzing factors such as account balances, transaction amounts, and user behavior, the system uses a Random Forest model to predict the likelihood of fraud. Users can input transaction details, and the system instantly assesses whether the transaction is potentially fraudulent.

How we built it

-Frontend: We built a user-friendly interface using Streamlit that allows users to input transaction details and receive real-time fraud detection feedback. -Backend: The backend is powered by Flask, which integrates the trained machine learning model to make real-time predictions. -Machine Learning: We trained a Random Forest model using Scikit-Learn to classify transactions as fraudulent or legitimate, based on historical data. -Data Processing: Pandas was used for cleaning and preprocessing the dataset, and we used Joblib to save and deploy the trained model efficiently. -Database: Transaction data was stored and managed using MongoDB to handle the scale of data and real-time interactions. -GitHub: Version control was maintained throughout the development process using GitHub for collaboration.

Challenges we ran into

One of the main challenges was ensuring real-time predictions while maintaining model performance on a large dataset. We initially worked with a dataset containing 6 million rows, which we had to trim to 100,000 transactions while ensuring there were enough fraudulent examples to train an accurate model. Additionally, integrating real-time predictions with a user-friendly interface in Streamlit was a technical hurdle.

Accomplishments that we're proud of

We successfully built a high-accuracy fraud detection model using a Random Forest algorithm, achieving impressive precision and recall metrics. We’re particularly proud of the easy-to-use Streamlit interface, which processes real-time inputs quickly and efficiently. Training the model and learning how to use new platforms like Streamlit and Flask were significant accomplishments.

What we learned

-We gained experience balancing model complexity with real-time performance. -We learned the importance of feature selection in fraud detection and how it impacts model accuracy. -Our experience with Streamlit gave us insight into building effective and interactive user interfaces.

What's next for AI-powered-Financial-Fraud-Detection-System

Live Data Integration: We plan to integrate live transaction data from financial institutions to make the system more practical. Algorithm Enhancement: Future improvements may include using more advanced machine learning algorithms to further improve detection accuracy. Real-World Deployment: We aim to deploy the system in real-world financial institutions to help combat fraud on a larger scale.

Built With:

-Streamlit (Frontend) -Flask (Backend) -MongoDB (Database) -Pandas & NumPy (Data Processing) -Scikit-Learn (Machine Learning) -GitHub (Version Control)

Built With

• csv
• dataset
• github
• githubdev
• pandas
• python
• scikit-learn