Software companies such as Neo4j and Cambridge Intelligence, provide network visualization products so that organizations could apply social network techniques to analyze data. They advocate for fraud prevention, and have worked with organizations all over the world, including law enforcement. As credit cards have become an increasingly popular form of payment worldwide, these software companies provide insight on the importance of graphing visualization tools in detecting credit card fraud. This is an interesting topic to discuss because of how widespread the problem has become globally and how complex it is to solve it. Many may argue that preventing all fraud incidents is not possible, as it is similar to the cat and mouse scenario, where both parties will always try to be one step ahead of each other. However, there are many innovative graphing tools today, along with machine learning and artificial intelligence algorithms that analysts can use to help prevent complicated criminal activities that were previously undetectable.
Detecting fraud is difficult because of constantly changing tactics that criminals use and their increasingly sophisticated strategies. Fraudulent activities could generally be categorized as two different kinds, known and unknown fraud. Known fraud is an activity that analysts have identified before and can be detected by defined algorithms and automation. Unknown fraud is an activity that needs to be detected, as it has not been encountered before.
Using graphing technology, the large amount of data that is often incomplete and complex could be rearranged into a network, which analysts could then use to extrapolate or interpolate patterns and find anomalies that otherwise could go unnoticed. While many algorithms are able to detect known fraud scenarios, detecting fraud when criminals work together and collaborate prove to be much harder to expose.
Identifying credit card fraud involves studying the relationships between accounts, transactions, events and people. By displaying these entities in a graph, it would be easier to pinpoint the point of origin and common denominators. Additionally, Cambridge Intelligence provide solutions where noise data could be filtered out to highlight underlying patterns and features to support temporal based analyses.
Numerous graphing analyses and techniques can be used to detect fraud. In the case of credit card fraud, multiple network relationships are created by criminals to hide fraudulent activities. In a network, there may be thousands of credit card users and, for example, a new client might be interested in applying for a credit card. Using graph visualization tools to rearrange data, analysts will be able to identify networks that the client is a part of (community detection). Through identifying related clusters and the connectivity of their network, analysts can determine how likely a client is to be a fraud. As an example, a basic indicator of potential fraudulent activity is if the client is trying to apply for a credit card with a SIN number that is the same SIN as a few different other accounts, but those accounts are under a different name or already marked as fraud. Thus, an investigation could be initiated. A lot more other attributes could be analyzed (i.e. phone numbers, addresses), but generally it is more simple and fast to follow the network connections than a brute force approach. By doing further investigations, such as calculating the node degrees of the social network, you may be able to find the person with the highest influence or perhaps which specific attributes prove to be the most influential in the network. This is useful because it will help in breaking down potential fraud rings, or preventing one to form. It may be the case where the neighbouring nodes that are one edge away from the client in the graph are not known as frauds. However, further analyses may reveal a few frauds 2 edges away from the client. Further more edges away, and a pattern may start to form and show that the client may be involved in some fraud activity. The goal of these graph analyses and identifying such patterns are to prevent fraud before it occurs.
In a similar way, these graphing analyzation and visualization techniques could be used to detect Insurance fraud and Bank fraud.
Fraud costs businesses billions of dollars each year. As crimes become harder to detect due to the level of complexity and sophistication, it is important for data tools and analyzing techniques to also continue to develop and innovate, with the goal of detecting such fraudulent crimes so it can be prevented. Graph technology and social network analyses help take part in minimizing such losses.
Article:
Sadowksi, Gorka, and Philip Rathle. “Fraud Detection: Discovering Connections with Graph Databases.” Neo Technology, Neo Technology, Jan. 2015.
Relevant Links:
https://neo4j.com/graphconnect-2018/session/graph-technology-ai-machine-learning