Fraud Detection: How It Works & Why It’s Needed
Are you looking for information on Fraud Detection? We explain the different types of fraudulent activities, fraud detection methods, and the benefits of fraud detection.
What Is Fraud Detection?
Fraud detection is the process of recognizing unauthorized activities where money or property is obtained through false pretenses, such as phishing, stolen credit cards, and identity theft. Fraud detection relies on machine learning and data analytics to identify these malicious transactions.
Why Fraud Detection Matters
Most forms of financial fraud involve impersonation or identity fraud. According to a recent study by Javelin Strategy, losses stemming from identity fraud totaled $56 billion in 2020 and has shown no signs of slowing down. As more businesses utilize digital channels to serve their customers, fraudsters work tirelessly to find ways to exploit them.
With the advent of Crimeware-as-a-Service (CaaS), digital fraud is now easier to execute than ever before. The CaaS model puts spyware and fraud tools into the hands of average criminals, making cyber fraud accessible to anyone, even those who lack technical know-how. CaaS offerings can include spyware attacks, phishing toolkits, money mule services, and more. In many cases, the cybercriminals behind the offerings collect a percentage of funds extracted from victims, or in the case of money mule accounts, up to 60% of the amount deposited.
Machine Learning vs. Signature-based Protection
Not all fraud detection systems are created equal. Before machine learning, signature-based mechanisms would store known threats inside a database. If the system detected activity that matched a signature, it was blocked. Bad actors quickly caught on and introduced randomness to their attack methodologies to avoid signature detection.
Machine learning works by detecting behavioral patterns and stopping threats before they can cause harm. Rather than looking only at the composition of a transaction, machine learning looks at the context for, and the relationships behind, a transaction to identify fraudulent behavior. This can include variables such as the type of payment being made, whether the payee account has received payment from the user in the past, is the amount characteristic of the user’s payment, and so on.
How Fraud Detection Works
Fraud detection leverages machine learning, statistical analysis, and behavior monitoring to identify the patterns and strategies used by criminals to commit fraud. When precursors to fraud are identified, the system can stop fraudulent activity before any damage occurs.
For fraud detection to work, the system must first study instances of known fraud. Let’s review some of the most common fraud detection learning models.
Supervised learning works by training an algorithm to detect fraud based on historical data. The training uses existing datasets with pre-marked variables. Using this past data, researchers can measure how well an algorithm performs at detecting fraud.
Supervised learning allows researchers to control what the fraud detection system learns and provides a simple framework for testing and debugging the machine learning process.
Unsupervised learning sorts unlabeled data into clusters based on the relationship each data point has with one another. Hidden relationships can often be discovered, identifying precursors to fraudulent activities.
This methodology eliminates the need for data to be labeled, which can be time-consuming and expensive. The drawback is that the algorithm may learn unnecessary patterns in the process that don’t help detect fraud.
Different analytic models can be used to identify the predictors of fraud based on the past actions of criminals through statistical analysis. With this data, fraud prevention systems can assign certain behaviors a risk score.
Technologies such as the Outseer Risk Engine, for instance, assesses more than 100 different data intelligence indicators to evaluate the relative risk associated with a transaction. It then generates a risk score based on device and behavioral profiling enriched with intelligence from the Outseer Global Data Network, a consortium of shared identity and transaction data spanning 6,000 companies in every industry and geography.
By these and other data inputs, the Outseer Risk Engine uses an advanced statistical approach to calculating a risk score. This method infers the conditional probability of an event being fraudulent, given the known factors or predictors. All available factors are taken into consideration, weighted according to relevance. The most predictive factors count more heavily toward the score.
This approach has significant benefits over other machine learning models used for fraud detection today, including artificial neural networks (ANNs), as well as their more advanced counterparts, deep neural nets, or DNNs. As the foundational technologies for our fraud detection solutions, the risk scores generated by the Outseer Risk Engine have been shown to prevent 95% of all fraud loss, while interrupting only 5% of all transactions—the best performance in the industry. To learn more, download our Risk Engine white paper.
With fraud attempts on the rise, let’s review a few of the most common types of fraud and how AI-powered fraud detection can stop them.
Malicious actors see online shopping as the perfect place to test stolen credit card information—unprotected gateways without fraud protection experience more fraudulent purchases, resulting in higher chargebacks and loss of revenue.
Payment fraud detection platforms like Outseer 3-D Secure stop credit card fraud by identifying abnormalities in purchasing behavior. This analysis works in tandem with a global network of identity and threat intelligence assets that continuously feed the system with real-time information.
With the dark web awash with compromised login credentials, account takeover surged 72% in 2020, according to Javelin. In these attacks, fraudsters use those stolen credentials to access legitimate user accounts. Once inside, the criminal impersonates the legitimate user and commits fraud under their identity. The online black market contains millions of stolen credentials, so how do you differentiate a real user from a fraudster?
Once again using Outseer as an example, we use predictive analytics and identity science to automatically sound the alarm on compromised accounts and take proactive steps to stop intruders from accessing company resources or initiating fraudulent transactions. Our fraud detection solutions can even detect insider threats and be paired with automatic remediation to stop attacks immediately.
Phishing & Brand Abuse
Cybercriminals disguise themselves as trusted individuals to trick recipients into sending money or revealing payment details, login credentials, or other private information. According to the FBI, phishing-based brand impersonation is the primary driver behind more than half of all cybercrime losses. And that’s before rogue mobile apps and fraudulent social media pages are taken into account.
Fraud detection solutions such as Outseer FraudAction continuously scan the threat landscape 24/7 to identify and take down sites used to distribute phishing attacks and malware, as well as rogue mobile apps, and social media pages impersonating your brand and targeting your customers and prospects.
How to Report Fraud
In the United States, different types of fraud are reported and investigated by multiple organizations. If you’re the victim of fraudulent activity, you can report it on the Department of Justice’s fraud report page. Alternatively, you can contact the Federal Bureau of Investigations to report corporate, financial, and investment fraud.
The Outseer Solution
By seeing what others can’t, Outseer provides seamless fraud protection without slowing down you or your customers. Outseer stops fraud long before a transaction ever occurs through a combination of machine learning and identity-based science. Protect your business through the power of frictionless fraud protection with our free demo today.