Did you know that businesses lose around 5% of their income every year due to fraud (ACFE)? So, when you apply a ratio to the gross world product value (i.e. approx $80 trillion), the loss due to fraud comes to around $4 trillion a year. Let's look at how fraud graphs can help you prevent these losses.
Looking at the statistics, you realise that fraud has turned out to be a trillion-dollar business, widespread across different industries, and has become more complex. Many companies are constantly struggling to identify, track and prevent criminal hackers and use various fraud detection tools. This is because fraud detection in the modern world depends on following a systematic model for matching data points with suspicious activities. Thus, when you use the traditional approach for fraud detection, i.e. rudimentary rules and analytics, to check the anomalies from separate data sets; there’s a chance that fraud detection would result in a lost cause. The criminal hacker would be long gone before the fraud is detected.
What is Anomaly in Fraud Detection?
When it comes to detecting frauds, specific events or items raise suspicion. These data patterns are outliers that differ from expected behavior within datasets and can potentially cause loss to the company. In other terms, they are known as anomalies.
An anomaly in fraud detection works perfectly if the system and the processes that follow provide the correct business outcome. For instance, it wouldn’t work if the fraud detection system only identifies the criminal hacker or the suspicious transaction. The system needs to go beyond the obvious and send alerts to the fraud investigation team or block accounts. Moreover, an anomaly in fraud detection needs to be an agile system that learns constantly, attempts to predict patterns, and generates false positives.
Let us understand with an example. Jenny works at a factory in New York. Every day, she buys coffee from a local shop, goes to work, and buys lunch. While doing so, she pays the bill with her credit card. Now, one day, a transaction is sent to her bank account to pay $10 for lunch from Portland, Oregon. Your system doesn’t know whether Jenny is on vacation or her card has gone missing and continues with the transaction. This is a problem because it can be someone else paying $10 bills with Jenny’s account on a “Card-holder-not-present” basis. These types of anomalous transactions can be easily neglected by the naked eye. So, you must invest in a robust system that identifies these anomalies and not treat them as non-trivial cases.
Due to the evolving nature of fraud, new advanced techniques like fraud graphs and machine learning have emerged for effective anomaly detection. It helps to streamline the process and improve the level of trust in financial transactions.
What is a Fraud Graph?
Fraud can be carried out by one person or a group of people using multiple identities, making it challenging to detect any unusual activity. To overcome this issue, fraud graphs come to your rescue. The uniqueness of the fraud graph is that it stores relevant information between people or transactions, enabling you to find data patterns and build systems that can detect any suspicious activities. In simple terms, you can identify common phone numbers or email addresses between connected users and create a network of similar information that can be analysed to detect fraud. You can also use this complex information to find the vulnerability gaps and learn about all the possible ways how frauds can occur.
How to Detect Anomalies Using Fraud Graphs?
Traditional relational databases lack the efficiency to store and analyse information between different entities. They are not an ideal solution to explore and visualise the relationships within the data. That’s why you need to use graph databases to overcome the poor performance of relational databases.
Ideally, a group of people who are working on committing fraud steals identity information. So, in the above example, you can see three account holders having separate accounts using overlapping phone numbers, SSN, and addresses. If you use a graph database, the data becomes streamlined and structured, making it easy and straightforward for you to identify suspicious transactions.
With the in-built purpose of storing and navigating relationships, graph databases are designed to ease the querying of data. These databases create effective data patterns that can help you make fast connections between them. By using flexible schema, graph databases can become a real-time tool for detecting anomalies in fraud detection.
In a fraud graph anomaly representation, the indices become a common vertex, and the edges determine the relationship between them. By using algorithms, you can analyse these relationships and find suspicious patterns. Due to the natural indexation of relationships, users can include data without performing any modelling in advance, which makes it helpful in keeping up with the speed of fraudsters.
So, when you create a fraud graph using the above graph database, you would understand that three people share the same contact information, thereby resulting in a graph ring. These rings can provide clarity and powerful insights into the transactions. After identifying these fraud rings, you can calculate the financial risk and take necessary action.
Machine Learning and Fraud Graphs
The correlation between them in detecting anomalies
Due to the rising fraud cases, new technologies like machine learning or artificial intelligence have come into the picture to detect fraud. The machine learning software identifies anomalous patterns in real-time by using predictive analytics and computing power. So, when fraud graphs are applied to artificial intelligence or machine learning applications, the system finds more relevant and precise relationships between the entities. This powerful fraud detection tool increases your efficiency, thereby letting you use your time more wisely. It means that you can focus on the most significant cases, provide more accurate alerts, evaluate and also reduce the number of customer rejections.
Undoubtedly, fraudsters are getting more innovative and advanced. And it's only fair as a business that you stay ahead of them by adopting cutting-edge technologies that help you detect fraudulent transactions and vulnerabilities. One such streamlined and simple tool is the fraud graph. It allows you to be proactive in tracking and monitoring anomalies and helps you to eliminate the risk of recurrence in the future. By investing in this tool, you can take swift actions at the right time, build your customer's trust, and save your brand's reputation.
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