When it comes to measuring transaction risk, the more transactions there are to parse, the harder it is for fraud and compliance experts. This is what makes back-to-school and other high-volume shopping seasons such a challenge.
At-a-glance
- Fraudsters are using synthetic identities to bypass traditional fraud detection methods. By combining real data to create fake identities, they can easily slip past checks relying on deterministic data.
- Risk scores offer a more comprehensive approach to fraud prevention. By analyzing vast amounts of data on user behavior and identity attributes, risk scores can identify suspicious activity that traditional methods miss.
- Our ML-powered analytics deliver effective risk scoring. By combining deterministic and probabilistic assessments, the risk score and supporting data provides intuitive concise insights into user behavior — especially helpful during periods of elevated transaction activity.
During busy seasons, fraudsters and other scam artists look to capitalize on weaknesses in organizations’ fraud and compliance technology. They’re aware of the strain that increased transaction activity puts on organizations — from both a personnel and technology perspective.
Your job is to ensure your organization can withstand the pressure, especially when transaction activity spikes. To keep trusted users’ information safe, your team must look for more comprehensive methods of reducing transaction risk and validating user identities. The key is to implement a nimble and effective fraud management solution that does not adversely affect the customer experience — this balance is critical to increasing revenue potential.
How are fraudsters capitalizing on busy shopping seasons?
The initial step in lowering transaction risk is recognizing that fraud operates on a global scale and being aware of the methods used by international cybercriminals. Exactly what strategies do fraudsters implement during busy shopping seasons to defraud trusted users?
Bad actors have a wide variety of methods to steal personally identifiable information (PII). Phishing schemes, email spoofing and whaling attacks are just a few of the social engineering tactics in their portfolio.
However, many fraudsters have taken their approach a step further through an attack known as synthetic identity theft. Using this method, the fraudster assembles a composite of first-party data to create a new identity. From the fraud reviewer’s perspective, every data point in this user’s identity checks out and fraudulent transactions aren’t detected.
Traditional Anti-Money Laundering and Know Your Customer (AML and KYC) compliance checks weren’t designed to catch these attacks. At least, not by themselves — and especially not when manual reviewers are swamped with transactions during back-to-school and other busy seasons. Ultimately, organizations need a new approach to gauging transaction risk — as well as the technology and infrastructure to match it.
Learn more about mitigating transaction risk
Measuring transaction risk with risk scores
The answer to more sophisticated fraudster attacks is a more sophisticated understanding of risk. This is where risk scores come into play. Risk scores provide a comprehensive look at the risk that each user poses in real time. This enables you to approve or question specific transactions with greater confidence, without adding friction to the customer experience.
But how does Mastercard Identity ensure these risk scores are accurate and delivered in real time? In short, through machine learning (ML)-powered data analytics. Since the average person has over 240 online accounts where identity data is siloed, the data must be contextualized into a comprehensive format so the data elements can be assessed for how they are used over time and across organizations. Our ML-powered data analytics assesses these customer identity elements against historical and current data for legitimate and fraudulent transactions. This assessment validates identification elements and a comprehensive probability for fraud, delivering an accurate overarching risk score.
For instance, being able to verify in real-time that the same valid email address has been used for purchases with three different companies across different countries within five minutes indicates a high likelihood of fraud, especially if this email address is usually observed only six times in a month. Such insight enables organizations to proactively prevent fraud before it occurs.
The ML-powered analytics assesses the data points from the customer against these datasets to calculate a final risk score in real time. This allows individual reviewers to streamline transaction processing and recover time for situations where manual intervention is needed. From a compliance perspective, this permits leadership to focus on remaining compliant with privacy and security regulations, knowing a reliable fraud infrastructure is in place.
Leveraging Mastercard Identity this back-to-school season
Back-to-school isn’t the only season that brings an influx of unpredictable transaction activity. The ability to secure your transactions at scale is always essential from a compliance and business perspective.
To adapt to increased network activity and evolving fraud methods, you need a comprehensive, identity-based solution, which is where Mastercard’s Identity solutions enter the picture.
