Blockchain-Enhanced Credit Card Fraud Detection Using Machine Learning

Blockchain-Enhanced Credit Card Fraud Detection Using Machine Learning

 

 

Sougandhika Narayan

Assistant Professor

Dept. of Computer Science

 and Engineering,

KSSEM Bengaluru, India

sougandhika@kssem.edu.in,

 

 

Hanoch Christian R

Dept. of Computer Science

and Engineering,

KSSEM Bengaluru, India

hanochchristian@gmail.com

 

Chandan Tavane

Dept. of Computer Science

and Engineering,

KSSEM Bengaluru, India

chandantavane99@gmail.com

 

 

Laksha Senthilkumar

Dept. of Computer Science

and Engineering,

KSSEM Bengaluru, India

lakmyscbe@gmail.com

 

Geoffrey Samuel

Dept. of  Computer Science

and Engineering,

KSSEM Bengaluru, India

geoffreysamuel28@gmail.com

 

 

Abstract— This study integrates blockchain technology and machine learning to enhance credit card fraud detection. ​ Precise fraud prediction is performed using advanced algorithms such as Random Forest, Logistic Regression, XGBoost, and Bayesian models. ​ Tools such as Ganache and MetaMask from Ethereum blockchain facilitate safe and transparent tracking of suspicious transactions. ​ Decentralized and tamper-proof properties of blockchain add reliability, and machine learning adds precision and flexibility. The system is highly accurate and transparent and has the potential to be used to fight financial fraud. ​

Keywords— Credit Card Fraud, Blockchain, Machine Learning, Ethereum, Web3, SMOTE, XGBoost, Streamlit