Leveraging Artificial Intelligence (AI) for Fraud Detection using Device Fingerprinting

Still relying on rule-based identification to drive your decision-making for fraud? Maybe it’s time to consider an upgrade to the latest AI-based fraud detection using Device Fingerprinting.

For digital businesses, fraud can be tricky and fraud detection can be challenging. Fraudsters have become accustomed to traditional techniques (such as cookies) and have switched to new technologies to commit fraud, becoming more dangerous than ever. Some of the latest techniques that are prevalent include phishing attacks with AI or chatbots and using deepfake technology for social engineering. In recent years, such scams have resulted in a huge loss of sensitive data and financial losses for digital organizations.  A report from IBM shows that businesses that fall victim to such fraud can expect a financial hit of nearly $5 million on average. 

So this is a cause of concern!!

There is a dire need for digital platforms to revisit their fraud prevention techniques. 

A fraud prevention technique that doesn’t change with trends will only ever worsen in its ability to block threats over time. Artificial Intelligence (AI) on the other hand avoids this by using data to always improve an algorithm’s accuracy. 

AI algorithms:

• Can continuously evolve, learning from new data and adapting to emerging patterns and trends.
• Have the ability to analyze vast amounts of data in real time allows AI to identify subtle, complex fraud patterns that traditional methods might miss.
• Can quickly and accurately detect fraud patterns.

It gets even better when AI algorithms are combined with the device fingerprinting technology for fraud detection. Using device fingerprinting technology digital platforms can create a unique device ID for every device that accesses the platform. This device ID is very unique as it comprises specific attributes such as IP addresses, wi-fi network, screen resolution, device model and brand, operating system, language setting, time zone, GPS coordinates, etc.

 

Now if in this case fraud is encountered, platform owners can get to know the exact device responsible for it. From the largest fraud syndicates to your average opportunists, with the power of these two technologies, you can effectively identify all. 

Another major benefit of device fingerprinting + AI-based fraud detection is that you can identify fraud before it happens. Meaning using these technologies it is possible to set device risk indicators. These indicators can effectively reveal any predefined set of tools and techniques that are typically associated with fraud. Some of the tools include app cloners, GPS spoofers, emulators, and VPNs, and techniques can include app tampering, device tampering, and signs of jailbreaking. Using this technique, risky devices are flagged in advance, with each device's level of risk assessed. This allows platform managers to proactively address potential threats and take appropriate action based on the identified risk levels.

The major types of fraud that AI combined with Device Fingerprint can effectively detect:

• Fake account creation fraud
• Account takeover
• Payment fraud
• Bot attacks
• Account sharing & subscription abuse