Secure ML-Powered Financial Transactions

Inspiration

The rise in digital financial transactions has increased the risk of fraud and security breaches. We wanted to create a secure and transparent financial platform that combines the predictive power of Machine Learning with the trust and decentralization of Blockchain.

What it does

Handling real-time transaction data and making accurate fraud predictions.

How we built it

ML Model: Trained a fraud detection model using TensorFlow and Scikit-learn. Applied anomaly detection to identify suspicious transactions.

Blockchain: Developed smart contracts using Solidity on Ethereum. Ensured transaction immutability and transparency using blockchain.

Integration: Built an API using Flask to connect ML and Blockchain layers. Created a frontend using React for user interaction.

Challenges we ran into

1. Ensuring seamless communication between ML and blockchain layers.
2. Optimizing ML model accuracy while minimizing processing time.
3. Securing user data without compromising transaction speed.

Accomplishments that we're proud of

Successfully integrated a Machine Learning model with a Blockchain network for real-time transaction security.

What we learned

How to integrate ML models with blockchain infrastructure. Building secure and automated smart contracts using Solidity.

What's next for Secure ML-Powered Financial Transactions

Improve fraud detection accuracy by incorporating real-time learning and advanced anomaly detection techniques.

Built With

• blockchain
• javascript
• python
• solidity
• tensorflow