korie-cyber/Fraud-Detection-Model
Fraud detection system using machine learning and deep learning (XGBoost + Autoencoder). Trains on synthetic financial transactions to flag suspicious activity with business-focused evaluation metrics.
This project helps financial institutions and payment processors automatically identify and flag suspicious transactions that might be fraudulent. It takes raw financial transaction data and processes it to output alerts on potentially fraudulent activities, helping fraud analysts focus on genuine threats. This tool is designed for fraud managers, risk analysts, and operations teams in fintech.
No commits in the last 6 months.
Use this if you need to build or understand a robust system for detecting financial transaction fraud that balances catching bad actors with minimizing false alarms.
Not ideal if your primary need is to detect fraud in non-financial domains like insurance claims or cybersecurity breaches, as it's specifically tailored for financial transaction data.
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Last pushed
Sep 10, 2025
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