Anomaly Detection on Dynamic (time-evolving) Graphs in Real-time and Streaming manner. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies.

Anomaly Detection on Time-Evolving Streams in Real-time. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies.

Real-time fraud detection system using ensemble ML models, featuring streaming data processing, explainable AI with SHAP, and production-ready deployment with FastAPI and Docker.

This repository implements a real-time credit card fraud detection pipeline using Kafka, Spark and Cassandra. Kafka continuously produces credit card transactions that will be analyzed by the Spark Streaming job in real-time. Meanwhile, classified transaction records will be displayed on the dashboard for visualization.

A real time streaming implementation of markov chain based fraud detection

SentinelX is an AI-powered real-time fraud detection system that combines machine learning, deep learning autoencoders, and graph-based analysis to identify fraudulent transactions. Using Kafka streaming and SHAP explainability, it delivers fast, transparent risk scoring through an interactive web dashboard.

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Content The dataset contains transactions made by credit cards in September 2013 by European cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-sensitive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise. Given the class imbalance ratio, we recommend measuring the accuracy using the Area Under the Precision-Recall Curve (AUPRC). Confusion matrix accuracy is not meaningful for unbalanced classification. Update (03/05/2021) A simulator for transaction data has been released as part of the practical handbook on Machine Learning for Credit Card Fraud Detection - https://fraud-detection-handbook.github.io/fraud-detection-handbook/Chapter_3_GettingStarted/SimulatedDataset.html. We invite all practitioners interested in fraud detection datasets to also check out this data simulator, and the methodologies for credit card fraud detection presented in the book. Acknowledgements The dataset has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group (http://mlg.ulb.ac.be) of ULB (Université Libre de Bruxelles) on big data mining and fraud detection. More details on current and past projects on related topics are available on https://www.researchgate.net/project/Fraud-detection-5 and the page of the DefeatFraud project Please cite the following works: Andrea Dal Pozzolo, Olivier Caelen, Reid A. Johnson and Gianluca Bontempi. Calibrating Probability with Undersampling for Unbalanced Classification. In Symposium on Computational Intelligence and Data Mining (CIDM), IEEE, 2015 Dal Pozzolo, Andrea; Caelen, Olivier; Le Borgne, Yann-Ael; Waterschoot, Serge; Bontempi, Gianluca. Learned lessons in credit card fraud detection from a practitioner perspective, Expert systems with applications,41,10,4915-4928,2014, Pergamon Dal Pozzolo, Andrea; Boracchi, Giacomo; Caelen, Olivier; Alippi, Cesare; Bontempi, Gianluca. Credit card fraud detection: a realistic modeling and a novel learning strategy, IEEE transactions on neural networks and learning systems,29,8,3784-3797,2018,IEEE Dal Pozzolo, Andrea Adaptive Machine learning for credit card fraud detection ULB MLG PhD thesis (supervised by G. Bontempi) Carcillo, Fabrizio; Dal Pozzolo, Andrea; Le Borgne, Yann-Aël; Caelen, Olivier; Mazzer, Yannis; Bontempi, Gianluca. Scarff: a scalable framework for streaming credit card fraud detection with Spark, Information fusion,41, 182-194,2018,Elsevier Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Bontempi, Gianluca. Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization, International Journal of Data Science and Analytics, 5,4,285-300,2018,Springer International Publishing Bertrand Lebichot, Yann-Aël Le Borgne, Liyun He, Frederic Oblé, Gianluca Bontempi Deep-Learning Domain Adaptation Techniques for Credit Cards Fraud Detection, INNSBDDL 2019: Recent Advances in Big Data and Deep Learning, pp 78-88, 2019 Fabrizio Carcillo, Yann-Aël Le Borgne, Olivier Caelen, Frederic Oblé, Gianluca Bontempi Combining Unsupervised and Supervised Learning in Credit Card Fraud Detection Information Sciences, 2019 Yann-Aël Le Borgne, Gianluca Bontempi Machine Learning for Credit Card Fraud Detection - Practical Handbook

Real-time Credit card Fraud detection using Spark Streaming, Spark ML, Spark SQL, Kafka, Cassandra and Airflow

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.

Fraud-detection, machine-learning, kafka, spark-streaming, anomaly-detection, time-series, model-monitoring, imbalanced-data, production-ml

Production Grade Real time streaming end to end Fraud Detection pipeline built using Pathway.

Multi-agent AI fraud detection with Kafka streaming intelligence, makes decision smarter than tradititonal transaction analysis.

A real-time fraud detection system using Apache Kafka and InfluxDB. This project simulates financial transactions, classifies them as suspicious or not based on amount, and streams them to Kafka. Suspicious transactions are stored in InfluxDB for monitoring and visualized via Grafana.

fraud-detection-kafka-streams

stack: Spark, Kafka, Databricks, SQL

A distributed event streaming application for fleet-monitoring and fraud detection.

Recognition of anomalies in the data stream in real time. Identify peaks. Fraud detection.

⚡ Real-time fraud & anomaly detection system for streaming transactions. Built with Kafka Streams + Isolation Forest ML. Low-latency processing, online learning, and scalable architecture for detecting fraud patterns in transaction data. 🚨🔍

The Near Real-time Fraud and Compliance Analytics Accelerator simplifies the fraud detection and reporting process, cutting down the time to action to prevent fraud as well as enables near real-time dashboards and analytics for streaming data.

Synapse-Lite is a modular, enterprise-grade real-time Bitcoin fraud detection system. It leverages Apache Spark, Neo4j, Kafka, and AI (Google Gemini LLM) to provide streaming analytics, graph-based fraud detection, and a professional Streamlit dashboard for monitoring and investigation.

Realtime AI-powered fraud detection system built with Pathway for live transaction streaming, real-time risk scoring, and LLM-based explainability using RAG over AML & compliance rules.

A production-grade real-time transaction fraud detection system leveraging advanced machine learning and distributed stream processing for high-scale financial security.

Advanced fraud detection platform with TabNet, Transformers, GNNs & VAEs. Real-time Kafka streaming, WebSocket alerts, federated learning & differential privacy. FastAPI REST API, drift detection, SHAP explainability, and Kubernetes-ready deployment. 99.2% AUC-ROC · <50ms latency.

Implementations for the lab assignments of Cyber Data Analytics (CS4035), a MSc course in TU Delft. The topics that we worked on are credit card fraud detection, anomaly detection in SCADA networks and streams and malware detection.

A real-time credit card fraud detection system built with PySpark MLlib that processes transactions through Kafka streams and provides live monitoring via Grafana dashboards.

Fraud Detection ETL Pipeline is an end-to-end solution that detects fraudulent transactions using Apache Spark, Kafka, and Azure Blob Storage. It includes real-time streaming and batch processing to identify suspicious activity efficiently.

This project demonstrates a complete, end-to-end real-time fraud detection system built with a modern streaming data stack. It simulates a stream of financial transactions, processes them using a stateful machine learning model in Apache Flink, and displays detected fraudulent activity on a live dashboard.

This project demonstrates how to perform real-time credit card fraud detection using Apache Kafka, Spark Streaming, and Flask. The system dynamically adapts to schema changes in the input dataset, detects fraudulent transactions in real-time, and displays the results on a web dashboard.

Streaming fraud detection system

This project defines how to use Kafka Streams to resolve a common use case like fraud detection in real time