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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.
About Insurance Fraud Detection
In an insurance fraud scenario, fraudsters stage fake car accidents and require reimbursement from the insurance companies for small injuries and damages that cannot be easily confirmed by the companies. Such scenarios may involve some or all passengers of the involved cars, the witnesses of the accidents, doctors or lawyers.
In our graph, each "accident" node is connected to the involved "car" nodes and the persons served as "witnesses". Each person in a car has a role (i.e., driver or passenger) and is connected to a "car" node and, maybe, to a "lawyer"/"doctor" node. Each node is also associated with the date of the event. The colors of the edges represent the relation between the connected nodes as follows:
involves
witness
drives
isPassenger
represents
heals
A typical-fraud scenario involves the same persons that participate to the same series of accidents playing a different role each time, e.g., one time driver, one time witness and two times passenger. In such cases, also, the fraudsters can share the same lawyer and/or doctor.
What to look for
Fraud rings, i.e., "accident" nodes that form cycles with almost the same participants. 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
- Shows the graph according to the current time frame.
- Provides additional information for the nodes on click (displayed in the right-panel) or on hover.
- Highlights fraud rings on hover.
- Graph elements, selection and highlight are rendered using the WebGL rendering technique, if this is supported by the browser.
Timeline Component
- Shows the number of node creation/removal events with a bar for each point in time.
- Contains a time frame rectangle to select which time segment is represented in the main graph by resizing/dragging it.
- Provides three detail levels (days/months/years) that are switched by scrolling anywhere in the component.
- Updates the highlights/selection in the main graph when hovering/selecting bars.
- Offers a to automatically move the time frame to the right while updating the main graph.
Inspection View Component
- Opens an inspection view of a fraud ring when double-clicking on fraud ring elements or the according symbol in the toolbar. Also, hovering over a fraud ring symbol animates the viewport to the corresponding fraud component.
- Shows a single graph component that contains fraud rings along with its own timeline.
- Updates the layout when clicking the Layout-button in the toolbar.