#Fraud-detection
Showing 43 of 43 repositories tagged #fraud-detection, ranked by stars
A Python library for anomaly detection across tabular, time series, graph, text, image, and audio data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.
Anomaly detection related books, papers, videos, and toolboxes. Last update late 2025 for LLM and VLM works!
MISP (core software) - Open Source Threat Intelligence and Sharing Platform
A curated list of Graph/Transformer-based fraud, anomaly, and outlier detection papers & resources
A curated list of data mining papers about fraud detection.
A Python Library for Graph Outlier Detection (Anomaly Detection)
Extract and aggregate threat intelligence.
A tool to detect illegitimate stars from bot accounts on GitHub projects
A Deep Graph-based Toolbox for Fraud Detection
Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook
Slides, scripts and materials for the Machine Learning in Finance Course at NYU Tandon, 2022
Open-source reference implementations for AI-enabled payment security, blockchain fraud detection, and AML/CFT compliance reasoning.
Code & Data for "Tabular Transformers for Modeling Multivariate Time Series" (ICASSP, 2021)
A free cryptowallet risk scoring tool with fully explainable scoring.
A data science project to predict whether a transaction is a fraud or not.
Protect your SIP Servers from bad actors at https://sentrypeer.org
Python implementation of Benford's Law tests.
ThreatBite is a real-time service that detects unwanted web users.
Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks - A lab we prepared for the KDD'19 Workshop on Anomaly Detection in Finance that will walk you through the detection of interpretable accounting anomalies using adversarial autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.
Open source AML and Fraud Detection using Machine Learning for Real-Time Transaction Monitoring
Exploring Neo4j and Graph Data Science for Fraud Detection
Official React Native client for Fingerprint. 100% accurate device identification for fraud detection.
Can we predict accurately on the skewed data? What are the sampling techniques that can be used. Which models/techniques can be used in this scenario? Find the answers in this code pattern!
Sequence-based Target Coin Prediction for Cryptocurrency Pump-and-Dump (SIGMOD 23)
Credit Card Fraud Detection App built with Streamlit, FastAPI and Docker.
Distributed Networks Institute
"Very simple but works well" Computer Vision based ID verification solution provided by LibraX.
AI-powered fraud detection and prevention system using GANs and Random Forest for secure digital transactions.
Detect and classify fraudulent transactions using SQL and Python. Generate behavioral features with SQLite, train a Logistic Regression model, and evaluate performance with AUC, precision, recall, and ROC analysis. A complete supervised fraud detection workflow.
Detect suspicious financial transactions using SQL and Python. Build user-level behavioral features in SQLite, apply Isolation Forest for anomaly detection, and visualize high-risk patterns. Demonstrates unsupervised fraud analytics and SQL-driven data science workflow.
High-performance Certificate Transparency (CT) monitoring tool written in Rust. Real-time stream of newly issued SSL/TLS certificates from CT logs. Rust implementation of certstream-server with improved performance and memory efficiency.
Real-time fraud detection engine - ML ensemble scoring, FastAPI REST API, 288 tests, CI/CD pipeline | CPUT Software Engineering
Fraud Detection for VoIP. Use SentryPeer® HQ to help prevent VoIP cyberattacks and fraudulent VoIP phone calls (toll fraud) at https://sentrypeer.com
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.
A deep exploration of how human psychology shapes fraud behavior and how those patterns become measurable signals in transaction data. This article reveals the behavioral, cognitive, and economic forces behind fraud, explaining how ML models detect deviations, anomalies, and intent hidden within financial transactions.
A machine learning system designed to identify fraudulent credit card transactions using Python and statistical analysis.
MER is a software that identifies and highlights manipulative communication in text from human conversations and AI-generated responses. MER can evaluate LLM responses for manipulative expressions, fostering development of transparency and safety in AI. It also supports manipulation victims by detecting manipulative patterns in human communication.
Real-time fraud detection lakehouse with Kafka, medallion pipelines, data quality, explainable scoring, and dashboards.
This project automates bank credit risk assessment using AI and machine learning models to predict loan defaults. It streamlines the credit process with predictive analytics, model evaluation, explainability (SHAP), and deployment readiness.
Databricks Real-Time Fintech Monitoring Pipeline: Hands-on lab to build a streaming fraud detection system using Auto Loader, watermarked deduplication, stream-static joins, and windowed rules engines in Databricks. Covers dual-SLA architecture for real-time alerts and batch compliance reporting.
Pre-crime intelligence system for mule account detection. Catches the warming phase 72 hours before illicit funds arrive.| iDEA 2.0 | PS3
AI-fraud-detection-suite-with-Streamlit-dashboard-and-independent-Android-APK