Opinion: AI Is Making Credit Card Fraud More Difficult to Detect and Stop

Artificial intelligence has moved out of the pilot phase in the finance space and is now deep into application. Among its many functions, it’s used to approve loan and credit card applications, detect fraud, provide customer service, and monitor spending patterns.

On the surface, the benefits seem to go on and on. But the deeper AI moves into the financial system, the more consumers realize that convenience comes with new risks, fewer human interactions, and a sense of losing control. 

The general consensus is that AI has improved efficiency and productivity. Transactions happen quicker, decisions on credit cards are faster, and security alerts are instantaneous. At the same time, this technology has also ushered in some new problems. 

AI fraud is making authorized and unauthorized transactions harder to detect. AI scams can manipulate cardholders into willingly initiating transactions, leaving you — the consumer — to contend with not just increased disputes but those that aren’t always so easy to solve. 

While human-created scams were rife with detectable flaws, from strange misspellings to awkward phrasing, those created by AI often are not. 

Deloitte’s January 2026 State of AI in the Enterprise report found surveyed companies expanded workforce access to sanctioned AI tools by 50% in one year, growing from under 40% to under 60% of workers.

That means AI is now an integral part of everyday business operations, including at financial services companies. The problem is consumers are often left to deal with the outcome when systems fail. 

Receiving and using correct information is of the utmost importance in finance. Card-not-present fraud is especially challenging.

Remote transactions expose you to AI-generated synthetic identities such as false profiles stolen from invented data; deepfake verification artifacts like simulated videos and voices; and personalized phishing that can be almost undetectable from the real thing. 

Avoiding AI scams is everyone’s business, and while expensive to deploy, building defensive measures is an investment in sound business practice. 

Personal and institutional losses due to fraud cost more. Consumers aren’t the only party that needs to pay attention to expenses. 

Credit scores are invaluable when determining the right lending products. Time-tested mathematical models have helped lenders reduce the possibility of lending to borrowers who are too risky while not shutting out those who genuinely qualify. 

It’s a beautiful balance, but there has to be trust, which is established by clarity and transparency. FICO Scores and VantageScores publish what goes into the scores, and most consumers find that information easy to understand. When 35% of a score is payment history, they know how essential it is to get payments in on time. 

But credit scoring companies are incorporating AI and machine learning into the system now. This can provide dynamic assessments using real-time data and predictive pattern recognition.

So what’s the problem? If an applicant is denied based on their credit report and credit score, the lenders have to explain why. Traditional credit and fraud models rely on a borrower’s historical behavior patterns, their identity, and rule-based anomaly detection. AI adds layers of information to the mix. That can make explanations far more complicated, putting credit card companies at risk of potential regulatory compliance problems. 

As noted in an April 2026 American Banker story, AI chatbots can and do go rogue. Andrew Sutton, partner at DarrowEverett LLP confirmed that hallucinations are not just common, they pose real risks. 

The last thing lenders and consumers want is for AI systems to make up information that lands on bank statements and credit reports. Out of control AI can wrongly approve or block transactions, leak customer card information, or create any number of costly mistakes.

So, while AI can seem magical, it’s certainly not perfect. For this reason it’s better not to completely depend on AI models, but to make sure there is a human in the loop, checking to ensure everything is correct. 

One of the biggest challenges in finance is checking for fraud and the new forms it has taken since AI entered the picture. 

The FBI’s Internet Crime Complaint Center received 22,364 internet crime complaints that contained references to AI in 2025. And AI-related losses have reached a staggering $893 million and aren’t slowing down. 

Consumers face a growing number of phishing scams, fake customer service calls, and deepfake videos that look and sound frighteningly like the real thing.  

The difference is that scams today look so real, they can get consumers to authorize transactions or submit payments they did not intend. To safeguard security, financial institutions add more protections, which make legitimate transactions more complex.

022,364 AI crime complaints in 2025, per the FBI's Internet Crime Complaint Center

Because the FBI has just made AI fraud a formal category, increased regulation will follow. When these crimes are tracked at the federal level, there will be new compliance expectations and updated liability standards for banks and lenders.

The best order for a defense framework is a cybersecurity platform that uses AI to automate threat detection. That means, among other things, learning a customer’s typical activity, flagging unauthorized access, and enforcing strict multifactor authentication. 

Keeping the human touch is critical at every stage of the process. No matter how advanced the technology at play, it can’t replace human judgment. 

Designated fraud specialists working with AI defense are needed to monitor the process, identify new scams, and adjust the response to threats.

The other human element is to beef up consumer education resources to help consumers learn to identify the signs of AI-generated voice-cloning and phishing scams.   

AI has its benefits and its costs. The technology is powerful, but so are the downsides. Human employees may be more valuable in the age of AI than you think.