Visualizing First-party Bank Fraud

Learn about fraud detection and how diagrams help to uncover fraud cases

What is fraud detection?

Fraud refers to the abuse of the assets of an organization, company, or person to make a profit. Many companies worldwide become victims of fraudsters even though most of them tend to believe that fraud is something that "could not happen to them". In general, most fraud cases are not identified immediately, but only after remarkable damage has been caused. Unfortunately, this damage does not limit only on a severe economic loss but also invokes other liability issues towards clients, employees, financial institutions, and many other involved entities. Thus, it is fundamental to be able to identify fraud cases immediately and respond quickly.

Fraud detection refers to all methods and techniques applied for the identification of potential fraud cases, their investigation to determine whether the identified cases are actual fraud cases or not, and the response to them. Unfortunately, there exist a lot of different types of fraud and no unique mechanism that can identify all of them. Thus, auditors have to develop separate strategies to combat each type of fraud.

What is first-party bank fraud?

In a first-party bank fraud scenario, fraudsters request legal high-value products from financial institutions, i.e., new accounts, checks, loans, or credit cards, with no intention to pay them back. To overcome the financial institutions’ approval stage, which verifies whether the client is trustworthy, they initially behave like regular, reliable clients that pay their debts on time. However, they suddenly stop the payments and disappear with the money, leaving no trace behind since, in most cases, they used fake identities or shared contact information.

This image visually describes the typical process of first-party bank fraud. The diagram outlines each step a fraudster takes: starting by obtaining a fake identity, then opening bank accounts or requesting credit cards, building a good credit history, securing a loan or credit, and finally disappearing with the money. This clear step-by-step visualization helps illustrate how such fraud schemes are planned and executed, highlighting the importance of recognizing suspicious behaviors early to prevent significant financial losses.

Advantages of visualizations in first-party bank fraud detection

First-party bank fraud cannot be easily spotted in advance since the financial institutions rely on the previous record of “trustworthiness” for each client. Also, fraudsters usually tend to plan the fraud in only a minimal number of institutions so that they are not easily spotted.

Therefore, first-party fraud detection requires an in-depth examination of the client’s profile. The prediction of future intentions is more accurately if the client’s profiles from more institutions are combined.

However, this in-depth examination needs a vast amount of data that stems from many different auditing systems with varying types of data that have to be investigated to detect potential first-party bank fraud cases. One possible solution can be the visualization of this data. The auditors can use visualizations to reveal relationships quickly, detect suspicious patterns, or identify significant structures that hide in this vast amount of data. Besides the visual exploration, interaction with the data allows for a deeper understanding of the dependencies within the data changing over time.

Fraud detection demo

This image showcases the advantages of using visualizations in first-party bank fraud detection. The interactive graph reveals suspicious network patterns, such as fraud rings—groups of users with shared details or unusual relationships that may indicate collusion. Central clusters are visually highlighted, making it easy for auditors to spot problematic structures instantly. Additional account holder details and a data table provide context for deeper analysis. By visualizing complex, multi-source data, investigators can quickly identify relationships, detect fraudulent behavior, and gain a clearer understanding of how fraud schemes evolve over time, leading to faster and more effective detection.

A typical pattern to look for is the so-called fraud ring . Fraud rings are persons that form cycles and share the same contact information. More precisely, in a first-party bank fraud scenario, auditors could look for potential fraud rings. In this scenario, visualization helps to detect fraud cases easier than digging into a large number of database raws.