How to Develop a Credit Card Fraud Detection Application Using Memgraph, Flask, and D3.js

Ivan Despot's tutorial provides a comprehensive guide to developing a credit card fraud detection application using Memgraph, Flask, and D3.js, illustrating the advantages of graph databases for handling complex, inter-related datasets often seen in fraud detection scenarios. The tutorial walks through setting up a basic Python web application that simulates credit card transactions and identifies fraudulent activities by leveraging the relationship traversal capabilities of graph databases. It covers the installation of necessary tools like Flask and Docker, the creation of a graph schema to model entities such as credit cards, POS devices, and transactions, and the implementation of business logic to detect and resolve fraudulent transactions. Additionally, it demonstrates how to dockerize the application for consistent deployment across different environments and introduces client-side logic for visualizing compromised POS devices and their transactions using D3.js. The tutorial emphasizes the efficiency of graph databases in real-time scenarios compared to traditional relational databases and encourages users to explore further possibilities with graph-powered applications.