The AI Fraud Arms Race: New Strategies for Detecting Synthetic Identity and Sophisticated Attacks

Highlights: 

• Artificial intelligence (AI) and generative AI are now being leveraged by sophisticated fraudsters to execute more automated attacks, including creating entirely new synthetic identities that pose a major threat.

• Fraud prevention experts are successfully fighting "AI with AI" by deploying machine learning systems that use behavioral analytics and real-time anomaly detection to maintain a critical edge against evolving threats.

There's no question that artificial intelligence (AI) is changing the world around us — especially when it comes to fraud. Fraudsters are using AI to target your business in new and deceptive ways. At the same time, experts on the cutting edge of fraud prevention use AI to fight back and beat fraudsters at their own game. What we’ve learned in the fight on the front lines can help keep criminals at bay and, hopefully, strengthen our allies in the war on AI-powered fraud.

AI use in fraud attempts, detection, and prevention

Forty years ago, fraudsters used telephone scams and fake mail orders to steal money and private information. Twenty years later, email and text messaging provided rich new inroads for criminals. These days, the tools, and fraudsters, are more sophisticated and smarter than ever, with AI at the heart of effective modern fraud technology. 

In 2019, the Federal Reserve warned of the fast-growing threat of synthetic identity fraud. Fraudsters were using pieces of personally identifiable information (PII) to create entirely new, fake people and companies. By 2021, this threat became a major reality in the fraud detection and prevention world. The next year saw the release of ChatGPT and similar services, putting generative AI into the reach of everyday users.

As more people get their hands on AI technology, it begins to power more attempts at fraud. Criminals invested in fraud seek to outsize their returns through AI and machine learning. Those “returns” come from stealing the revenue of real businesses. However, fraud analysts began to harness the power of AI and machine learning almost two decades ago. We’ve got real experience fighting AI with AI.

The future lies in AI answers to AI challenges

Modern fraudsters continue to work on masking, automation, and even the generation of whole new identities. Meanwhile, artificial intelligence empowers defenders with the tools they need to not only thwart attempts but also protect against future attacks in new ways.

Machine learning delivers a form of experience and intuition that acts as a bulwark in real time. It also provides actionable insight through analysis, empowering us as real, human experts to further refine protections. Fraud analysts use this highly effective cycle to create some of the strongest protection available and drive the development of new machine learning technology.

Though fraud remains a challenge almost as old as commerce itself, there’s no one-size-fits-all solution now or realistically around the corner. The accelerated evolution of artificial intelligence will no doubt continue to transform fraud and prevention efforts. Fraudsters will likely make more attempts than ever, with greater levels of sophistication. One of the greatest advantages we have against this threat is the ability to learn and grow from literally every attempt, turning their zealous, continuous onslaught against them.

Fighting AI with AI

At the intersection of experience and intuition, AI fraud prevention shines. Advanced systems engage in behavioral analytics to deliver near-instant anomaly detection. Piecing together the clues that reveal synthetic identities requires cutting-edge technology. By combining experience, intuition, and the work of real fraud analysts, the best modern systems maintain an edge over even unconventional fraudsters.

This is where the proverbial arms race really heats up. Each attempt provides feedback for tools that track, identify, and thwart fraud. Identifying patterns in attempts and anomalies in user behavior further refines these tools. And this goes both ways. Criminals who rely on fraud continue to develop new systems and try to find new ways to get away with nefarious activity. Those systems in turn push modern detection and prevention to new heights.