Credit card fraud detection through logistic regression, k-means, and deep learning.

Credit Card Fraud Detection using ML: IEEE style paper + Jupyter Notebook

End-to-end Credit Card Fraud Detection using Machine Learning with SMOTE, Random Forest, and ROC-AUC evaluation.

The OWASP Top 5 Machine Learning Risks[edit | edit source] The idea is to build the required resources which help software security community to understand the emerging technology of machine learning and how it is related to security, warn them about the risk associated with using ML, and discuss the defending techniques. Description[edit | edit source] Machine Learning has recently re-emerged as a powerful tool in multiple business sectors, especially when it is used for Predictive Analytics at the scale of Big Data. This technique becomes vital when it is harnessed for the Security services and applications like Fraud Detection, Anomaly Detection, Behavioral Analysis

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Fraud detection in bank transactions using graph databases and machine learning.

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.

A fully featured banking API built with FastAPI,Docker,Celery,Redis,RabbitMQ with an AI/ML transaction analysis and fraud detection system

Safeguarding Payments: Fraud Detection, AI/ML and Data Insights

Online-lending fraud detection with customers' sequential behavioral data (End-to-end ML and NLP project).

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 scalable, declarative, low-code framework for real-time and batch feature calculation/management (quant finance, anomaly/fraud detection, etc.), predictive ML training/inference and simulation. Built on top of Ray

In this study, we aimed to detect fraudulent activities in the supply chain through the use of neural networks. The study focused on building two machine learning models using the MLPClassifier algorithm from the scikit-learn library and a custom neural network using the Keras library in Python.

Streamlit web app for AI/ML-based anomaly detection of fraud in financial transactions — created for Pandora Paradox @ KIIT E-Summit 2025 by Team Binary Brains.

Turn production logs into test coverage using AI/ML. Automatically generates Gherkin test scenarios from application logs with banking-specific compliance and fraud detection modules.

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

Preempt fraud with rule-based patterns and select ML algorithms for reliable fraud detection. Use anomaly detection and fraud prediction to respond to bad actors rapidly.

Here is the project of credit card fraud detection using ML

The Fraud Detection project aims to improve identification of fraudulent activities in e-commerce and banking by developing advanced machine learning models that analyze transaction data, employ feature engineering, and implement real-time monitoring for high accuracy fraud detection.

Apply Machine Learning algorithms for fraud detection and fraud prevention.

Comprehensive portfolio showcasing AI/ML applications in fraud detection, including foundational EDA, transaction fraud, identity fraud, and KYC/AML compliance systems.

🛒 Provide a robust model that assists in flagging suspicious transactions, ultimately helping businesses improve security and reduce financial losses.

Fraud Detection and Prevention (FDP) Agentic Platform is leveraging artificial intelligence (AI) and machine learning (ML) capabilities to empower business users with ability to detect, prevent, and mitigate fraudulent activities across Financial Services Industry (FSI) business operations.

Analyzing data and provide insights on Financial Fraud Detection using Spark ML.

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

Accompanying repo for a hands on workshop on building a ML infrastructure for fraud detection at GopherCon 2020

Real-time fraud detection platform – 4-model ML ensemble, sub-second scoring, interactive Streamlit dashboard. Open-source, Docker-ready.