Beyond Blacklists: How AI Is Redefining Fraud Prevention for Legitimate Businesses

The New Face of Fraud and Its Unintended Victims

When UK engineering firm Arup lost $25 million to a sophisticated deepfake scam in early 2024, it served as a wake-up call about how dramatically fraud has evolved. Fraudsters used AI-generated video and voice to impersonate company executives, convincing an employee to authorize a massive wire transfer. This incident highlighted not just the growing sophistication of fraud, but also the limitations of traditional prevention systems that often penalize legitimate businesses alongside actual criminals.

The New Face of Fraud and Its Unintended Victims
The High Cost of False Positives
Next-Generation Solutions: Smarter Detection, Fewer Mistakes
The Push for Transparency and Accountability
The Human Impact of Automated Decisions
The Future: From Gatekeeping to Partnership

Across industries like CBD, telehealth, gaming, and alternative finance—often categorized as “high-risk”—even legally operating businesses face sudden account freezes and blacklisting by automated fraud systems. Major payment processors sometimes terminate relationships without explanation, leaving companies stranded with frozen assets and no clear path to resolution.

The High Cost of False Positives

According to industry estimates, false positives cost merchants approximately 2.8% of their annual revenue. The consequences extend beyond immediate financial losses. Kirk Fredrickson, founder of compliance specialist firm 2Accept, explains the broader impact: “We’ve seen companies lose accounts overnight for nothing more than a keyword in their product description. That kind of overreach doesn’t just hurt business; it undermines trust in the entire financial system.”

For businesses already operating under regulatory scrutiny, being mistakenly labeled as fraudulent can be devastating. The damage to reputation, coupled with frozen operations, can effectively end a company‘s ability to function, regardless of its actual compliance status., as covered previously

Next-Generation Solutions: Smarter Detection, Fewer Mistakes

Forward-thinking companies are developing AI systems that distinguish between actual fraud and legitimate business activity. These solutions analyze patterns across transactions, chargebacks, and merchant behavior to create more nuanced risk assessments.

Fredrickson’s company reports that their systems reduce account termination risk by up to 60% for merchants in high-risk sectors. “Most companies we work with aren’t trying to skirt the rules,” he notes. “They’re trying to play by them, and our job is to make sure AI can tell the difference.”

The industry’s major players are following similar paths. Mastercard’s Decision Intelligence Pro analyzes 160 billion transactions annually, combining behavioral and device data to improve accuracy. HSBC reported that their AI models reduced false positives by 60% while detecting two to four times more actual fraud.

The Push for Transparency and Accountability

As AI takes greater responsibility in fraud detection, demands for explainability are growing. “The tools we build have to be explainable,” Fredrickson emphasizes. “It’s not enough to flag a transaction. You have to be able to say why and what can be done about it.”

This expectation is increasingly backed by regulation. The EU AI Act and frameworks like the Digital Operational Resilience Act require transparency in high-risk AI applications. In the United States, agencies like the Consumer Financial Protection Bureau are investigating whether financial institutions’ AI tools unfairly limit access to services.

The industry is responding with hybrid systems that blend automation with human review. According to Experian’s recent research, over half of businesses are now investing in tools specifically designed to avoid mistaking legitimate customers for criminals.

The Human Impact of Automated Decisions

When AI systems misread patterns or flag companies based on misunderstood keywords, the consequences extend beyond financial metrics. Frozen accounts can destroy years of business development, damage hard-earned reputations, and leave entrepreneurs with no clear recourse.

Fredrickson highlights the human element: “You can’t build trust with one hand and take it away with the other. If AI is going to govern access to financial infrastructure, then it has to work for everyone, especially those trying to do things right.”

This is particularly crucial in sectors like CBD or wellness, where up to 70% of merchants face closure within their first year due to banking challenges. Tools that reduce wrongful termination rates by half can mean the difference between business survival and failure.

The Future: From Gatekeeping to Partnership

Fraud prevention is evolving from rigid rule enforcement to sophisticated pattern recognition. Instead of automatically cutting off businesses at the first sign of potential risk, modern systems are learning to pause, assess context, and adapt their responses.

The next phase of fraud prevention focuses on creating fairer systems that protect against criminal activity without punishing legitimate businesses. As Fredrickson puts it: “Our job isn’t to judge an industry. It’s to make sure ethical businesses have a fair shot at thriving.”

This shift represents a fundamental rethinking of AI’s role in financial security—from being a simple gatekeeper to becoming a intelligent partner that understands the difference between actual threats and legitimate commerce operating within complex regulatory environments.