Fraud is a huge issue for most firms in almost every sector. It’s also an enormous problem for the banking sector. Association of Certified Fraud Examiners indicates that fraud costs $67 billion in the banking sector annually. Banks can’t ignore this amount of money loss. Therefore, they try to benefit from new approaches such as graph analytics for fraud detection. In fact, no firm can ignore this money loss because they try to decrease the effects of the global financial crisis. Even the biggest economies in the world face the threat of recession recently.
Why Fraud Detection Is Important for the Banks
Proper fraud detection plays a crucial role for banks to detect and prevent fraud on time. There is an interesting yet significant fact about banking fraud: 70% of fraud cases are committed by the own personnel of banks. Employees who have the maximum degree of access to IT systems might commit the crime and destroy the proofs afterward.
Fraud detection in banking is so important as no banks want to lose money to fraudsters in these economic conditions. Moreover, banks not only need to protect their own money but also need to protect their customers' money. Those customers trust the bank to keep their money safe. When a fraud case occurs in the bank and customers find out about it, they lose their trust in the bank. Therefore, their customers withdraw their money from that bank and transfer it to another bank usually. Besides, they never want to do business with that bank again. As result, the bank loses its reputation, along with its customers.
Fraud in banking may cost the bank to lose money, customer loyalty, customer trust, reputation, and ultimately both their individual and commercial customers. Fraud detection and prevention play a vital role for banks to avoid such negative circumstances. Every bank should have its solutions to prevent fraud and should work hard to minimize it. Unfortunately, preventing fraud completely is a dream.
What Is Graph Analytics?
Graph analytics has various names such as network analysis, graph algorithms, and link analysis. Graph analytics is an analytics instrument that is beneficial for studying relations and defining strength between the existing entities in an organization like products, clients, and services. We can determine the strength and direction of relationships between objects in a graph through graph analytics. The core of network analysis is on the pairwise relationship between two objects at a time and the structural characteristics of the graph as a whole. Thanks to graphs, we can see these relationships clearly.
Demonstrating relationships via graphs can assist in responding to numerous questions for the problem-solving procedure. Also, it helps maximize the output. There are various algorithms to execute for each kind of graph analytics to find the best solution. Coming up with the best solution depends on the nature and seriousness of the issue. Data is the raw material of an examination and graphs deliver the plot and perspective we need to make excellent insights and decisions from it.
How Graph Analytics is Different from Traditional Analytical Methods?
Traditional analytics benefits from operations research, computer programming, and statistics. On the other hand, graph analytics benefits from graph-specific algorithms to discover connections between two objects. Clustering, partitioning, PageRank, and shortest path algorithms are exclusive to graph analytics.
Graph analytics uses graph databases, and they are more flexible than relational database management systems. Unlike relational database management systems, you can easily add new data connections in graph databases.
Graph analytics has a lot of advantages compared to traditional analytics methods.
- It saves time and requires less effort.
- It is easier to work with.
- Data storage and modelling are way easier.
- Since graphs are used, working with the data and interpreting the data is much easier.
- Having the right insights and making correct decisions are more likely with graph analytics.
How Banks Use Graph Analytics for Fraud Detection?
Graph analytics has several use cases in fraud detection, national security, marketing, healthcare, supply chain optimization, journalism, etc. Especially in recent years, using graph analytics for fraud detection became highly popular. In addition, graph analytics in banking has been a new concept.
As we mentioned above, fighting fraud is incredibly important for banks and other financial institutions. With the enhancement in technology, fraudsters come up with new fraud techniques and strategies. Hence, banks should come up with new techniques and strategies to prevent fraud as well. Graph analytics is a convenient way to detect and prevent fraud in the banking sector.
Thanks to graph analytics, connections between people, bank accounts, phones, locations, etc. can be analysed easily. Since it’s visually appealing, one can quickly notice suspicious activities.
A method to identify fraud is to find groups of individuals or transactions which have an abnormally high number of interconnections. Graph analytics can detect abnormal behaviours that traditional fraud detection methods may label as normal.
Let’s say there’s a $2 million transaction between two people. One of these people wants to send this $2 million to the other person without getting caught by internal bank alerts. She divides the money into many transactions to be below the compulsory reporting limit. This process includes numerous intermediary bank accounts and repeats a couple of times.
There are many processes and layers. Everything seems normal at first look. It seems like one of these people’s companies pays various suppliers and the other one’s company is being paid by various clients. Nevertheless, when you evaluate these transactions visually with graph analytics, you can see that the $2 million pouring out from a specific artery into plenty of capillaries and then back into one key artery.
Integrating Graph Analytics with Machine Learning
Graph analytics method is so convenient to be able to find connections between entities and detect fraud if any. Machine learning is another advancement to detect and prevent fraud. Benefiting from both machine learning and advanced graph analytics is a perfect solution for fraud detection and prevention. That’s why more and more banks prefer using both methods in their fraud prevention and detection processes. Graphs offer outstanding data for machine learning systems to be trained. They produce data from the connections between distinct objects in the database and machine learning systems can use this data to analyse and learn patterns further.
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