{"repo":"shiroonigami23-ui/Bank-Security","free":true,"listed":false,"github":"https://github.com/shiroonigami23-ui/Bank-Security","clone":"git clone https://github.com/shiroonigami23-ui/Bank-Security.git","description":"AI-fraud-detection-suite-with-Streamlit-dashboard-and-independent-Android-APK","language":"Python","stars":10,"topics":["android","cybersecurity","fintech","fraud-detection","streamlit","xgboost"],"license":null,"category":"security-tools","readme_excerpt":"FinGuard Enterprise - Bank Security AI-driven financial fraud detection system with: - Streamlit analyst dashboard - XGBoost model inference - Forensic PDF report generation - Independent Android APK with offline transaction risk analysis Features - Real-time fraud transaction simulation and scoring - Interactive forensics dashboard and network explorer - Audit log tracking - Fraud report PDF download - Independent Android app ( finguard-mobile.apk ) with native, offline fraud scoring UI Project Structure Setup (Python App) 1. Install dependencies: 2. Run: or use: Dataset Handling - CSV datasets are included in version control for full local reproducibility. - If CSVs are absent, the app still supports synthetic fallback demo data. - Model file fraud model xg.pkl remains part of the repo for inference. Android APK - Source: android/ - Build output in release: finguard-mobile.apk - The APK runs independently from Streamlit with local risk-score calculations. - Streamlit workflow remains unchanged and continues to run from main.py . Release Automation - Tag push v triggers Android build workflow: - Builds APK - Publishes GitHub Release asset License MIT","default_branch":null,"files":null,"tree":[],"storefront":"/r/shiroonigami23-ui","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/shiroonigami23-ui/Bank-Security/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}