![]() This blog post explores the complex world of financial fraud detection. We look at how ArangoDB’s powerful query language, combined with our data visualization technology, creates an effective anti-fraud application. A powerful financial fraud detection app, built with ReGraph and KronoGraph, for analyzing ArangoDB data The financial fraud detection challengeĭetecting fraud is a difficult business, and investigating fraudulent banking transactions comes with its own complex challenges. The general workflow when executing a query is as follows: A client application ships an AQL query to the ArangoDB server. An individual transaction might seem perfectly legitimate, but when it’s analyzed in the context of multiple transactions, patterns emerge that uncover criminal behavior.īut how do you find a particular pattern of transactions in a huge dataset of background activity? Here’s where a graph query language is very useful.ĪrangoDB are experts in handling scalable, fully managed graph databases. ![]() Test Two Weeks for Free (No Credit Card Needed) Start Your Free ArangoGraph Trial. Their ArangoDB Query Language (AQL) is declarative, client-independent and easy to understand. Enterprise-Grade Security & Automated Backups. It’s both an effective analytical tool (“Is this really a fraudulent pattern?“) and a presentational tool (“How can I explain to others why I came to this conclusion?“) How can we help them to spot false positives and determine the next course of action fast?ĭata visualization has the answers.How can we present query results to analysts so they can easily investigate each candidate pattern?.When they shared some example AQL queries with us, we were keen to run them against a fictional financial fraud dataset. The full ArangoDB package ships with the following programs and tools: ArangoDB server.
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