chevron_right Demos chevron_right Fraud Detection
Discover more
Book a live Demo

See yFiles in action

30 min • Online • No install required


For developers, PMs & tech leads

  • Quick-start walkthrough + a real integration example
  • Live Q&A with next-step recommendations

Prefer another tool over Zoom? Just say so when you book.

Opens our scheduling page with Zoom's booking widget embedded. Using it shares your data with Zoom under its Privacy Policy.

Interactive Knowledge Graph Demo Event Timeline Demo Space & Time Demo Isometric Drawing Demo Company Ownership Chart Demo
Demos Overview

See yFiles in action

30 min • Online • No install required


For developers, PMs & tech leads

Prefer another tool over Zoom? Just say so when you book.

Opens our scheduling page with Zoom's booking widget embedded. Using it shares your data with Zoom under its Privacy Policy.

Fraud Rings
close

Business Data

About First-party Bank Fraud

In a first-party bank fraud scenario, fraudsters request legal products from banks, i.e., new accounts, checks, loans or credit cards. For some period of time, they behave like normal customers and pay their debts. However, suddenly they disappear with the money leaving no trace behind, since they have used fake identities or contact information.

In our graph, each person is associated with an address, a phone number, a bank branch and a series of bank products (including possible payments for these products). Each node is visualized in a different manner based on its type and is associated with dates representing the date when an event started and/or finished. For example, when a loan was requested and/or was paid back.

A typical fraud-scenario involves two or more persons that share the same fake personal information such as address or phone number and apply for several bank products.

What to look for

Fraud rings, i.e., persons that form cycles and share contact information. In the demo, they are visualized in red color.

Fraud Detection Demo

This demo shows how yFiles for HTML can be used for detecting fraud cases in time-dependent data. Fraud affects many companies worldwide causing economic loss and liability issues. Fraud detection relies on the analysis of a huge amount of data-sets and thus, visualizations can be valuable for the quick detection of fraud schemes.

Main Graph Component

Timeline Component

Inspection View Component