A typical consumer might hear the phrase ‘fraud protection’ and immediately think about taking steps to prevent credit card information from being stolen. Preventing data theft is certainly part of fraud protection, but it is just one small part. Fraud protection in the cybersecurity realm is a multifaceted approach to mitigating loss.
Fraud protection is also a primary use case for darknet intelligence strategies. Organizations like DarkOwl leverage darknet intelligence to help clients in the financial services sector prevent fraud. Every prevented incident represents effective loss prevention. So it’s no wonder the financial services organizations and their security experts appreciate organizations like DarkOwl.
The Role of Threat Intelligence in Fraud Protection
Prior to the internet, fraud protection in the credit card realm was pretty simple. Credit card issuers published books containing questionable credit card numbers. The books were distributed to merchants on a monthly basis. Prior to every credit card transaction, a merchant would look up the customer’s credit card number in the book. If that number could be found, the credit card was not accepted.
Such a quaint and simple way of preventing credit card fraud would not work today. The internet has made things far too complex. In addition, today’s fraud protection strategies are more proactive than reactive. That’s where threat intelligence comes in.
Threat intelligence relies on dark web monitoring to analyze countless volumes of data. The data informs security experts of the possibility of stolen credit and debit card information circulating on the dark web. It can also alert them to ongoing unauthorized use of debit and credit cards.
Data Analysis Is Everything
The actual practice of threat intelligence involves going out and gathering data. But in order for the data to be useful, it has to be analyzed and transformed into actionable strategies. Enter data analysis. One could make the case that analysis is everything in fraud protection.
Fortunately, modern threat intelligence tools utilize advanced data analytics and machine learning to uncover threats. Tools are designed to:
- Detect Anomalies – Data points or transactions that deviate significantly from norms are considered anomalies. Detecting anomalies can help uncover fraud.
- Recognize Patterns – Certain types of patterns, like repetitive sequences or relationships within financial data, often indicate fraud.
- Predict Incidents – Through machine learning and AI, threat intelligence platforms are capable of predicting incidents of fraud. Being able to anticipate future incidents is key to shutting down fraud before it begins.
As important as data analysis is to fraud protection, it is incapable of preventing fraud entirely. Data analysis is supported by other strategies to keep fraud potential and damage to a minimum.
Real Time Monitoring
Another big part of the multifaceted approach to fraud protection is real time monitoring. Security experts continually monitor the dark web and adjacent sites with the knowledge that stolen credit and debit card information will always be out there. Real time monitoring is designed to alert organizations as quickly as possible about compromised data.
Monitoring accounts for compromised card information threat actors are trying to sell on dark web marketplaces. It accounts for activities known as credential stuffing. In short, real-time monitoring is the fraud protection equivalent of using a smoke alarm in a home. It is looking for any signs of potential danger.
Fraud protection is not a one-off exercise. It is an ongoing endeavor designed to mitigate losses attributed to credit and debit card theft. It is also a multifaceted approach to limiting losses. When successful, fraud protection benefits both consumers and financial services companies. Even vendors are protected against fraudulent transactions that would otherwise contribute to mounting losses.
