The aim of this project is to predict fraudulent credit card transactions with the help of different machine learning models.
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Updated
Jan 24, 2023 - Jupyter Notebook
The aim of this project is to predict fraudulent credit card transactions with the help of different machine learning models.
🛡️ Welcome to our Credit Card Fraud Detection project! 💳 Harnessing the formidable prowess machine learning, we're steadfast in our mission to fortify your financial stronghold against deceitful adversaries. Join our crusade for financial resilience,Ensuring every transaction is securely monitored! 🔐💯
Assignments for the semester Jun - Dec 2021 @ IIT Hyderabad
End-to-end Credit Card Fraud Detection project using Python, Scikit-learn, and Streamlit — includes data ingestion, feature engineering, model training, scoring, monitoring, and an interactive dashboard for fraud analysis.
Cloud-native risk scoring (FastAPI + Streamlit), Azure CI/CD, Responsible AI
Procurement risk analytics — Neo4j graph patterns (shared addresses, winner rotation) + anomaly detection and data-quality checks
This repository offers a comprehensive overview of various analytical techniques for fraud detection and provides implementation guidance for an effective fraud prevention solution to help you detect fraud early.
Card Fraud Detection Analysis of European Cardholders from September 2013.
Credit card transaction fraud detection model that utilized credit card transaction data from 2010, incorporating data from both credit card companies and merchants.
Tackle identity fraud in credit card application using intensive data analytics and advanced machine learning method to save companies from financial losses
Credit Card Fraud Detection
Fraud Transaction Detector is a machine learning system that identifies and flags potentially fraudulent transactions, provides risk scoring, analytics summaries via Agentic AI, and actionable insights to help businesses monitor and prevent fraud effectively.
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