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Implementation of an intelligence system to detect the fraud cases on the basis of classification.

A smart payment switch

This repository contains a basic fraud detection system utilising supervised learning techniques to identify potentially fraudulent credit card transactions. The project establishes a baseline model that addresses the challenges of credit card fraud in financial institutions.

In this notebook we take a look at a relevant project that is frequently encountered by insurers: Fraud Detection. For this purpose we use a car data set from a public source and will show the necessary steps to establish an automated fraud detection.

This project showcases how Telco Fraud Detection use cases can be addressed using Snowflake and Streamlit.

The comprehensive study that investigates the implementation of machine learning algorithms for fraud detection in e-commerce using Python.

AI-powered explainable credit card fraud detection system using XGBoost + SHAP, deployed with Flask and React.

ClaimAudit AI uses machine learning to detect potentially inappropriate billing patterns, upcoding, and duplicate services across vast claim datasets. Its predictive models identify providers with outlier billing patterns and automatically flags claims for review before payment, reportedly saving insurers an average of 7-9% on claim payments.

Credit card fraud detection using Random Forest — 99.96% accuracy.

identify fraudulent financial transactions using key behavioral patterns in transaction data. It includes comprehensive data preprocessing, model training using scikit-learn, and a Streamlit web app that allows users to input transaction details and receive instant fraud predictions.

Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials. Fraud Detection in Online Payments using AI leverages machine learning to identify suspicious transactions, prevent fraud, and secure digital payment systems through real-time monitoring and intelligent analysis.

Includes Source Code, PPT, Synopsis, Report, Documents, Base Research Paper & Video tutorials. Deep Learning for Anomaly Detection leverages neural networks to learn normal data patterns and accurately identify anomalies, enabling early detection of faults, threats, and irregular activities.

fraud detection challenge from zindi

Credit card fraud detection model

FraudWatch is a machine learning-based credit card fraud detection system that uses a Random Forest classifier. It visualizes model performance with an interactive confusion matrix heatmap. The system is deployed as a user-friendly Flask web application. 📊

A demo machine learning pipeline for transaction fraud detection using Pandas, Scikit-learn, and feature engineering.

A comprehensive bank fraud detection system using Graph Analytics (Neo4j) and Machine Learning to track fund flows and identify complex money laundering patterns in real-time.