{"repo":"AmirhosseinHonardoust/Financial-Fraud-Risk-Engine","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/Financial-Fraud-Risk-Engine","clone":"git clone https://github.com/AmirhosseinHonardoust/Financial-Fraud-Risk-Engine.git","description":"A complete end-to-end fraud detection system for financial transactions, featuring data pipelines, cost-sensitive ML modeling, explainability with SHAP, threshold optimization, batch scoring, and an interactive Streamlit dashboard. Designed to simulate real-world fintech fraud-risk workflows.","language":"Python","stars":21,"topics":["anomaly-detection","cost-sensitive-learning","credit-card-fraud","data-pipeline","data-science","explainable-ai","financial-data","fintech","fraud-detection","imbalanced-data"],"license":"MIT","category":"data-pipelines","readme_excerpt":"Financial Fraud Risk Engine A production-minded fraud-risk workflow for detecting suspicious transactions with cost-sensitive thresholding , validation , explainability , reason codes , dashboard review , and threshold policy artifacts . Important: This project is a portfolio and research demo , not a production fraud detection system. The data is synthetic. The model, thresholds, and reason codes are designed to demonstrate a professional fraud-risk workflow, not to make real financial decisions without expert validation, monitoring, compliance review, and security controls. --- Table of Contents - Project Overview - What This Project Does - What This Project Does Not Do - Key Features - System Workflow - Project Structure - Installation - Quick Start - Synthetic Data Generator - Training and Evaluation - Threshold Policy Artifacts - Batch Scoring - Streamlit Dashboard - Explainability and Reason Codes - Evaluation Metrics - Visual Reports - Testing and CI - Code Quality - Limitations - Responsible Use - Future Improvements - Tech Stack - Author - License --- Project Overview Fraud detection is not only a classification problem. Real fraud systems require careful handling of: - class imbalance - changing fraud patterns - false-positive cost - missed-fraud cost - human review capacity - model interpretability - batch scoring and triage - validation and monitoring This project demonstrates an end-to-end fraud-risk workflow using synthetic transaction data. It includes data pre","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/Financial-Fraud-Risk-Engine/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."}